diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..b3b07e0f6bae3eb08943fa81dd7faa4ae70f31f3 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +demos/calculator/screenshot.gif filter=lfs diff=lfs merge=lfs -text +demos/kitchen_sink/files/world.mp4 filter=lfs diff=lfs merge=lfs -text +demos/video_component/files/world.mp4 filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md index 7581b65d3cc23643ac9808dd4088f2ce903274f1..eabed400dbb3d54fd7e393262dbcbf704dc2b49b 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,11 @@ + --- -title: Pr 9339 All Demos -emoji: 🐨 -colorFrom: blue -colorTo: yellow +title: pr-9339-all-demos +emoji: 💩 +colorFrom: indigo +colorTo: indigo sdk: gradio sdk_version: 4.44.0 app_file: app.py pinned: false --- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..11ba2b45209e6786614b7a7f8030b70460ce5c51 --- /dev/null +++ b/app.py @@ -0,0 +1,48 @@ +import importlib +import gradio as gr +import os +import sys +import copy +import pathlib +from fastapi import FastAPI, Request +from fastapi.templating import Jinja2Templates +import uvicorn +from gradio.utils import get_space + +os.environ["GRADIO_ANALYTICS_ENABLED"] = "False" + +demo_dir = pathlib.Path(__file__).parent / "demos" + +app = FastAPI() + +templates = Jinja2Templates(directory="templates") + +names = sorted(os.listdir("./demos")) + +@app.get("/") +def index(request: Request): + names = [[p[0], p[2]] for p in all_demos] + return templates.TemplateResponse("index.html", {"request": request, "names": names, + "initial_demo": names[0][0], "is_space": get_space()}) + +all_demos = [] +demo_module = None +for p in sorted(os.listdir("./demos")): + old_path = copy.deepcopy(sys.path) + sys.path = [os.path.join(demo_dir, p)] + sys.path + try: # Some demos may not be runnable because of 429 timeouts, etc. + if demo_module is None: + demo_module = importlib.import_module("run") + else: + demo_module = importlib.reload(demo_module) + all_demos.append((p, demo_module.demo, False)) + except Exception as e: + with gr.Blocks() as demo: + gr.Markdown(f"Error loading demo: {e}") + all_demos.append((p, demo, True)) + +for demo_name, demo, _ in all_demos: + app = gr.mount_gradio_app(app, demo, f"/demo/{demo_name}") + +if __name__ == "__main__": + uvicorn.run(app, port=7860, host="0.0.0.0") diff --git a/demos/audio_debugger/cantina.wav b/demos/audio_debugger/cantina.wav new file mode 100644 index 0000000000000000000000000000000000000000..41f020438468229763ec4a2321325e5916e09106 Binary files /dev/null and b/demos/audio_debugger/cantina.wav differ diff --git a/demos/audio_debugger/run.ipynb b/demos/audio_debugger/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..208af9aed8213849ec5b2470d481042b76fe3b79 --- /dev/null +++ b/demos/audio_debugger/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: audio_debugger"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/audio_debugger/cantina.wav"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import subprocess\n", "import os\n", "\n", "audio_file = os.path.join(os.path.abspath(''), \"cantina.wav\")\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Tab(\"Audio\"):\n", " gr.Audio(audio_file)\n", " with gr.Tab(\"Interface\"):\n", " gr.Interface(\n", " lambda x: x, \"audio\", \"audio\", examples=[audio_file], cache_examples=True\n", " )\n", " with gr.Tab(\"Streaming\"):\n", " gr.Interface(\n", " lambda x: x,\n", " gr.Audio(streaming=True),\n", " \"audio\",\n", " examples=[audio_file],\n", " cache_examples=True,\n", " )\n", " with gr.Tab(\"console\"):\n", " ip = gr.Textbox(label=\"User IP Address\")\n", " gr.Interface(\n", " lambda cmd: subprocess.run([cmd], capture_output=True, shell=True, check=False)\n", " .stdout.decode(\"utf-8\")\n", " .strip(),\n", " \"text\",\n", " \"text\",\n", " )\n", "\n", " def get_ip(request: gr.Request):\n", " return request.client.host\n", "\n", " demo.load(get_ip, None, ip)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/audio_debugger/run.py b/demos/audio_debugger/run.py new file mode 100644 index 0000000000000000000000000000000000000000..fe4b04d2d72836f1898fea483b11e1672acaecc7 --- /dev/null +++ b/demos/audio_debugger/run.py @@ -0,0 +1,38 @@ +import gradio as gr +import subprocess +import os + +audio_file = os.path.join(os.path.dirname(__file__), "cantina.wav") + +with gr.Blocks() as demo: + with gr.Tab("Audio"): + gr.Audio(audio_file) + with gr.Tab("Interface"): + gr.Interface( + lambda x: x, "audio", "audio", examples=[audio_file], cache_examples=True + ) + with gr.Tab("Streaming"): + gr.Interface( + lambda x: x, + gr.Audio(streaming=True), + "audio", + examples=[audio_file], + cache_examples=True, + ) + with gr.Tab("console"): + ip = gr.Textbox(label="User IP Address") + gr.Interface( + lambda cmd: subprocess.run([cmd], capture_output=True, shell=True, check=False) + .stdout.decode("utf-8") + .strip(), + "text", + "text", + ) + + def get_ip(request: gr.Request): + return request.client.host + + demo.load(get_ip, None, ip) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_essay/run.ipynb b/demos/blocks_essay/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..8878ec76a8de8534413e83ba5c0908d3f70a11d2 --- /dev/null +++ b/demos/blocks_essay/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_essay"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "countries_cities_dict = {\n", " \"USA\": [\"New York\", \"Los Angeles\", \"Chicago\"],\n", " \"Canada\": [\"Toronto\", \"Montreal\", \"Vancouver\"],\n", " \"Pakistan\": [\"Karachi\", \"Lahore\", \"Islamabad\"],\n", "}\n", "\n", "def change_textbox(choice):\n", " if choice == \"short\":\n", " return gr.Textbox(lines=2, visible=True), gr.Button(interactive=True)\n", " elif choice == \"long\":\n", " return gr.Textbox(lines=8, visible=True, value=\"Lorem ipsum dolor sit amet\"), gr.Button(interactive=True)\n", " else:\n", " return gr.Textbox(visible=False), gr.Button(interactive=False)\n", "\n", "with gr.Blocks() as demo:\n", " radio = gr.Radio(\n", " [\"short\", \"long\", \"none\"], label=\"What kind of essay would you like to write?\"\n", " )\n", " text = gr.Textbox(lines=2, interactive=True, show_copy_button=True)\n", "\n", " with gr.Row():\n", " num = gr.Number(minimum=0, maximum=100, label=\"input\")\n", " out = gr.Number(label=\"output\")\n", " minimum_slider = gr.Slider(0, 100, 0, label=\"min\")\n", " maximum_slider = gr.Slider(0, 100, 100, label=\"max\")\n", " submit_btn = gr.Button(\"Submit\", variant=\"primary\")\n", "\n", " with gr.Row():\n", " country = gr.Dropdown(list(countries_cities_dict.keys()), label=\"Country\")\n", " cities = gr.Dropdown([], label=\"Cities\")\n", " @country.change(inputs=country, outputs=cities)\n", " def update_cities(country):\n", " cities = list(countries_cities_dict[country])\n", " return gr.Dropdown(choices=cities, value=cities[0], interactive=True)\n", "\n", " def reset_bounds(minimum, maximum):\n", " return gr.Number(minimum=minimum, maximum=maximum)\n", "\n", " radio.change(fn=change_textbox, inputs=radio, outputs=[text, submit_btn])\n", " gr.on(\n", " [minimum_slider.change, maximum_slider.change],\n", " reset_bounds,\n", " [minimum_slider, maximum_slider],\n", " outputs=num,\n", " )\n", " num.submit(lambda x: x, num, out)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/blocks_essay/run.py b/demos/blocks_essay/run.py new file mode 100644 index 0000000000000000000000000000000000000000..f06f10a88f7fd41125ec78e0eccd01c71ee394cf --- /dev/null +++ b/demos/blocks_essay/run.py @@ -0,0 +1,51 @@ +import gradio as gr + +countries_cities_dict = { + "USA": ["New York", "Los Angeles", "Chicago"], + "Canada": ["Toronto", "Montreal", "Vancouver"], + "Pakistan": ["Karachi", "Lahore", "Islamabad"], +} + +def change_textbox(choice): + if choice == "short": + return gr.Textbox(lines=2, visible=True), gr.Button(interactive=True) + elif choice == "long": + return gr.Textbox(lines=8, visible=True, value="Lorem ipsum dolor sit amet"), gr.Button(interactive=True) + else: + return gr.Textbox(visible=False), gr.Button(interactive=False) + +with gr.Blocks() as demo: + radio = gr.Radio( + ["short", "long", "none"], label="What kind of essay would you like to write?" + ) + text = gr.Textbox(lines=2, interactive=True, show_copy_button=True) + + with gr.Row(): + num = gr.Number(minimum=0, maximum=100, label="input") + out = gr.Number(label="output") + minimum_slider = gr.Slider(0, 100, 0, label="min") + maximum_slider = gr.Slider(0, 100, 100, label="max") + submit_btn = gr.Button("Submit", variant="primary") + + with gr.Row(): + country = gr.Dropdown(list(countries_cities_dict.keys()), label="Country") + cities = gr.Dropdown([], label="Cities") + @country.change(inputs=country, outputs=cities) + def update_cities(country): + cities = list(countries_cities_dict[country]) + return gr.Dropdown(choices=cities, value=cities[0], interactive=True) + + def reset_bounds(minimum, maximum): + return gr.Number(minimum=minimum, maximum=maximum) + + radio.change(fn=change_textbox, inputs=radio, outputs=[text, submit_btn]) + gr.on( + [minimum_slider.change, maximum_slider.change], + reset_bounds, + [minimum_slider, maximum_slider], + outputs=num, + ) + num.submit(lambda x: x, num, out) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_group/run.ipynb b/demos/blocks_group/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f8b5f935659b9ba4e63e4cead1f1ef7bf46c68eb --- /dev/null +++ b/demos/blocks_group/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_group"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def greet(name):\n", " return \"Hello \" + name + \"!\"\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\"### This is a couple of elements without any gr.Group. Form elements naturally group together anyway.\")\n", " gr.Textbox(\"A\")\n", " gr.Number(3)\n", " gr.Button()\n", " gr.Image()\n", " gr.Slider()\n", "\n", " gr.Markdown(\"### This is the same set put in a gr.Group.\")\n", " with gr.Group():\n", " gr.Textbox(\"A\")\n", " gr.Number(3)\n", " gr.Button()\n", " gr.Image()\n", " gr.Slider()\n", "\n", " gr.Markdown(\"### Now in a Row, no group.\")\n", " with gr.Row():\n", " gr.Textbox(\"A\")\n", " gr.Number(3)\n", " gr.Button()\n", " gr.Image()\n", " gr.Slider()\n", "\n", " gr.Markdown(\"### Now in a Row in a group.\")\n", " with gr.Group():\n", " with gr.Row():\n", " gr.Textbox(\"A\")\n", " gr.Number(3)\n", " gr.Button()\n", " gr.Image()\n", " gr.Slider()\n", "\n", " gr.Markdown(\"### Several rows grouped together.\")\n", " with gr.Group():\n", " with gr.Row():\n", " gr.Textbox(\"A\")\n", " gr.Number(3)\n", " gr.Button()\n", " with gr.Row():\n", " gr.Image()\n", " gr.Audio()\n", "\n", " gr.Markdown(\"### Several columns grouped together. If columns are uneven, there is a gray group background.\")\n", " with gr.Group():\n", " with gr.Row():\n", " with gr.Column():\n", " name = gr.Textbox(label=\"Name\")\n", " btn = gr.Button(\"Hello\")\n", " gr.Dropdown([\"a\", \"b\", \"c\"], interactive=True)\n", " gr.Number()\n", " gr.Textbox()\n", " with gr.Column():\n", " gr.Image()\n", " gr.Dropdown([\"a\", \"b\", \"c\"], interactive=True)\n", " with gr.Row():\n", " gr.Number(scale=2)\n", " gr.Textbox()\n", "\n", " gr.Markdown(\"### container=False removes label, padding, and block border, placing elements 'directly' on background.\")\n", " gr.Radio([1,2,3], container=False)\n", " gr.Textbox(container=False)\n", " gr.Image(\"https://picsum.photos/id/237/200/300\", container=False, height=200)\n", "\n", " gr.Markdown(\"### Textbox, Dropdown, and Number input boxes takes up full space when within a group without a container.\")\n", "\n", " with gr.Group():\n", " name = gr.Textbox(label=\"Name\")\n", " output = gr.Textbox(show_label=False, container=False)\n", " greet_btn = gr.Button(\"Greet\")\n", " with gr.Row():\n", " gr.Dropdown([\"a\", \"b\", \"c\"], interactive=True, container=False)\n", " gr.Textbox(container=False)\n", " gr.Number(container=False)\n", " gr.Image(height=100)\n", " greet_btn.click(fn=greet, inputs=name, outputs=output, api_name=\"greet\")\n", "\n", " gr.Markdown(\"### More examples\")\n", "\n", " with gr.Group():\n", " gr.Chatbot()\n", " with gr.Row():\n", " name = gr.Textbox(label=\"Prompot\", container=False)\n", " go = gr.Button(\"go\", scale=0)\n", "\n", " with gr.Column():\n", " gr.Radio([1,2,3], container=False)\n", " gr.Slider(0, 20, container=False)\n", "\n", " with gr.Group():\n", " with gr.Row():\n", " gr.Dropdown([\"a\", \"b\", \"c\"], interactive=True, container=False, elem_id=\"here2\")\n", " gr.Number(container=False)\n", " gr.Textbox(container=False)\n", "\n", " with gr.Row():\n", " with gr.Column():\n", " gr.Dropdown([\"a\", \"b\", \"c\"], interactive=True, container=False, elem_id=\"here2\")\n", " with gr.Column():\n", " gr.Number(container=False)\n", " with gr.Column():\n", " gr.Textbox(container=False)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/blocks_group/run.py b/demos/blocks_group/run.py new file mode 100644 index 0000000000000000000000000000000000000000..b91d4bc5c71fe2c389fd3a4f8b1fbce1ab5b8c8a --- /dev/null +++ b/demos/blocks_group/run.py @@ -0,0 +1,110 @@ +import gradio as gr + +def greet(name): + return "Hello " + name + "!" + +with gr.Blocks() as demo: + gr.Markdown("### This is a couple of elements without any gr.Group. Form elements naturally group together anyway.") + gr.Textbox("A") + gr.Number(3) + gr.Button() + gr.Image() + gr.Slider() + + gr.Markdown("### This is the same set put in a gr.Group.") + with gr.Group(): + gr.Textbox("A") + gr.Number(3) + gr.Button() + gr.Image() + gr.Slider() + + gr.Markdown("### Now in a Row, no group.") + with gr.Row(): + gr.Textbox("A") + gr.Number(3) + gr.Button() + gr.Image() + gr.Slider() + + gr.Markdown("### Now in a Row in a group.") + with gr.Group(): + with gr.Row(): + gr.Textbox("A") + gr.Number(3) + gr.Button() + gr.Image() + gr.Slider() + + gr.Markdown("### Several rows grouped together.") + with gr.Group(): + with gr.Row(): + gr.Textbox("A") + gr.Number(3) + gr.Button() + with gr.Row(): + gr.Image() + gr.Audio() + + gr.Markdown("### Several columns grouped together. If columns are uneven, there is a gray group background.") + with gr.Group(): + with gr.Row(): + with gr.Column(): + name = gr.Textbox(label="Name") + btn = gr.Button("Hello") + gr.Dropdown(["a", "b", "c"], interactive=True) + gr.Number() + gr.Textbox() + with gr.Column(): + gr.Image() + gr.Dropdown(["a", "b", "c"], interactive=True) + with gr.Row(): + gr.Number(scale=2) + gr.Textbox() + + gr.Markdown("### container=False removes label, padding, and block border, placing elements 'directly' on background.") + gr.Radio([1,2,3], container=False) + gr.Textbox(container=False) + gr.Image("https://picsum.photos/id/237/200/300", container=False, height=200) + + gr.Markdown("### Textbox, Dropdown, and Number input boxes takes up full space when within a group without a container.") + + with gr.Group(): + name = gr.Textbox(label="Name") + output = gr.Textbox(show_label=False, container=False) + greet_btn = gr.Button("Greet") + with gr.Row(): + gr.Dropdown(["a", "b", "c"], interactive=True, container=False) + gr.Textbox(container=False) + gr.Number(container=False) + gr.Image(height=100) + greet_btn.click(fn=greet, inputs=name, outputs=output, api_name="greet") + + gr.Markdown("### More examples") + + with gr.Group(): + gr.Chatbot() + with gr.Row(): + name = gr.Textbox(label="Prompot", container=False) + go = gr.Button("go", scale=0) + + with gr.Column(): + gr.Radio([1,2,3], container=False) + gr.Slider(0, 20, container=False) + + with gr.Group(): + with gr.Row(): + gr.Dropdown(["a", "b", "c"], interactive=True, container=False, elem_id="here2") + gr.Number(container=False) + gr.Textbox(container=False) + + with gr.Row(): + with gr.Column(): + gr.Dropdown(["a", "b", "c"], interactive=True, container=False, elem_id="here2") + with gr.Column(): + gr.Number(container=False) + with gr.Column(): + gr.Textbox(container=False) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_js_methods/run.ipynb b/demos/blocks_js_methods/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..1a5195c09b6de1ce5d3510e0a47eb1d1aa1e1d65 --- /dev/null +++ b/demos/blocks_js_methods/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_js_methods"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "blocks = gr.Blocks()\n", "\n", "with blocks as demo:\n", " subject = gr.Textbox(placeholder=\"subject\")\n", " verb = gr.Radio([\"ate\", \"loved\", \"hated\"])\n", " object = gr.Textbox(placeholder=\"object\")\n", "\n", " with gr.Row():\n", " btn = gr.Button(\"Create sentence.\")\n", " reverse_btn = gr.Button(\"Reverse sentence.\")\n", " foo_bar_btn = gr.Button(\"Append foo\")\n", " reverse_then_to_the_server_btn = gr.Button(\n", " \"Reverse sentence and send to server.\"\n", " )\n", "\n", " def sentence_maker(w1, w2, w3):\n", " return f\"{w1} {w2} {w3}\"\n", "\n", " output1 = gr.Textbox(label=\"output 1\")\n", " output2 = gr.Textbox(label=\"verb\")\n", " output3 = gr.Textbox(label=\"verb reversed\")\n", " output4 = gr.Textbox(label=\"front end process and then send to backend\")\n", "\n", " btn.click(sentence_maker, [subject, verb, object], output1)\n", " reverse_btn.click(\n", " None, [subject, verb, object], output2, js=\"(s, v, o) => o + ' ' + v + ' ' + s\"\n", " )\n", " verb.change(lambda x: x, verb, output3, js=\"(x) => [...x].reverse().join('')\")\n", " foo_bar_btn.click(None, [], subject, js=\"(x) => x + ' foo'\")\n", "\n", " reverse_then_to_the_server_btn.click(\n", " sentence_maker,\n", " [subject, verb, object],\n", " output4,\n", " js=\"(s, v, o) => [s, v, o].map(x => [...x].reverse().join(''))\",\n", " )\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/blocks_js_methods/run.py b/demos/blocks_js_methods/run.py new file mode 100644 index 0000000000000000000000000000000000000000..2abb2d9d3bca4114009da9ae61ef1ebfc347da81 --- /dev/null +++ b/demos/blocks_js_methods/run.py @@ -0,0 +1,41 @@ +import gradio as gr + +blocks = gr.Blocks() + +with blocks as demo: + subject = gr.Textbox(placeholder="subject") + verb = gr.Radio(["ate", "loved", "hated"]) + object = gr.Textbox(placeholder="object") + + with gr.Row(): + btn = gr.Button("Create sentence.") + reverse_btn = gr.Button("Reverse sentence.") + foo_bar_btn = gr.Button("Append foo") + reverse_then_to_the_server_btn = gr.Button( + "Reverse sentence and send to server." + ) + + def sentence_maker(w1, w2, w3): + return f"{w1} {w2} {w3}" + + output1 = gr.Textbox(label="output 1") + output2 = gr.Textbox(label="verb") + output3 = gr.Textbox(label="verb reversed") + output4 = gr.Textbox(label="front end process and then send to backend") + + btn.click(sentence_maker, [subject, verb, object], output1) + reverse_btn.click( + None, [subject, verb, object], output2, js="(s, v, o) => o + ' ' + v + ' ' + s" + ) + verb.change(lambda x: x, verb, output3, js="(x) => [...x].reverse().join('')") + foo_bar_btn.click(None, [], subject, js="(x) => x + ' foo'") + + reverse_then_to_the_server_btn.click( + sentence_maker, + [subject, verb, object], + output4, + js="(s, v, o) => [s, v, o].map(x => [...x].reverse().join(''))", + ) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_layout/run.ipynb b/demos/blocks_layout/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..a3ea74271df38babdd0d24e3cdf437d6aafc9bbd --- /dev/null +++ b/demos/blocks_layout/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_layout"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", " with gr.Row():\n", " gr.Image(interactive=True, scale=2)\n", " gr.Image()\n", " with gr.Row():\n", " gr.Textbox(label=\"Text\")\n", " gr.Number(label=\"Count\", scale=2)\n", " gr.Radio(choices=[\"One\", \"Two\"])\n", " with gr.Row():\n", " gr.Button(\"500\", scale=0, min_width=500)\n", " gr.Button(\"A\", scale=0)\n", " gr.Button(\"grow\")\n", " with gr.Row():\n", " gr.Textbox()\n", " gr.Textbox()\n", " gr.Button()\n", " with gr.Row():\n", " with gr.Row():\n", " with gr.Column():\n", " gr.Textbox(label=\"Text\")\n", " gr.Number(label=\"Count\")\n", " gr.Radio(choices=[\"One\", \"Two\"])\n", " gr.Image()\n", " with gr.Column():\n", " gr.Image(interactive=True)\n", " gr.Image()\n", " gr.Image()\n", " gr.Textbox(label=\"Text\")\n", " gr.Number(label=\"Count\")\n", " gr.Radio(choices=[\"One\", \"Two\"])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/blocks_layout/run.py b/demos/blocks_layout/run.py new file mode 100644 index 0000000000000000000000000000000000000000..3f7686f4bbec3db5cd2e660f7bec095989680084 --- /dev/null +++ b/demos/blocks_layout/run.py @@ -0,0 +1,37 @@ +import gradio as gr + +demo = gr.Blocks() + +with demo: + with gr.Row(): + gr.Image(interactive=True, scale=2) + gr.Image() + with gr.Row(): + gr.Textbox(label="Text") + gr.Number(label="Count", scale=2) + gr.Radio(choices=["One", "Two"]) + with gr.Row(): + gr.Button("500", scale=0, min_width=500) + gr.Button("A", scale=0) + gr.Button("grow") + with gr.Row(): + gr.Textbox() + gr.Textbox() + gr.Button() + with gr.Row(): + with gr.Row(): + with gr.Column(): + gr.Textbox(label="Text") + gr.Number(label="Count") + gr.Radio(choices=["One", "Two"]) + gr.Image() + with gr.Column(): + gr.Image(interactive=True) + gr.Image() + gr.Image() + gr.Textbox(label="Text") + gr.Number(label="Count") + gr.Radio(choices=["One", "Two"]) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_multiple_event_triggers/requirements.txt b/demos/blocks_multiple_event_triggers/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..5cb63bcc8c366f4abc569ed8ef379a90d4759ff4 --- /dev/null +++ b/demos/blocks_multiple_event_triggers/requirements.txt @@ -0,0 +1,2 @@ +plotly +pypistats \ No newline at end of file diff --git a/demos/blocks_multiple_event_triggers/run.ipynb b/demos/blocks_multiple_event_triggers/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..daded4de2023864a1e366d57ccb06226a96a62ae --- /dev/null +++ b/demos/blocks_multiple_event_triggers/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_multiple_event_triggers"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio plotly pypistats"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pypistats\n", "from datetime import date\n", "from dateutil.relativedelta import relativedelta\n", "import pandas as pd\n", "\n", "def get_plot(lib, time):\n", " data = pypistats.overall(lib, total=True, format=\"pandas\")\n", " data = data.groupby(\"category\").get_group(\"with_mirrors\").sort_values(\"date\")\n", " start_date = date.today() - relativedelta(months=int(time.split(\" \")[0]))\n", " data = data[(data['date'] > str(start_date))]\n", " data.date = pd.to_datetime(pd.to_datetime(data.date))\n", " return gr.LinePlot(value=data, x=\"date\", y=\"downloads\",\n", " tooltip=['date', 'downloads'],\n", " title=f\"Pypi downloads of {lib} over last {time}\",\n", " overlay_point=True,\n", " height=400,\n", " width=900)\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\n", " \"\"\"\n", " ## Pypi Download Stats \ud83d\udcc8\n", " See live download stats for all of Hugging Face's open-source libraries \ud83e\udd17\n", " \"\"\")\n", " with gr.Row():\n", " lib = gr.Dropdown([\"transformers\", \"datasets\", \"huggingface-hub\", \"gradio\", \"accelerate\"],\n", " value=\"gradio\", label=\"Library\")\n", " time = gr.Dropdown([\"3 months\", \"6 months\", \"9 months\", \"12 months\"],\n", " value=\"3 months\", label=\"Downloads over the last...\")\n", "\n", " plt = gr.LinePlot()\n", " # You can add multiple event triggers in 2 lines like this\n", " for event in [lib.change, time.change, demo.load]:\n", " event(get_plot, [lib, time], [plt])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/blocks_multiple_event_triggers/run.py b/demos/blocks_multiple_event_triggers/run.py new file mode 100644 index 0000000000000000000000000000000000000000..16eeda543eb9efd732809ff69045e28b7f6528af --- /dev/null +++ b/demos/blocks_multiple_event_triggers/run.py @@ -0,0 +1,38 @@ +import gradio as gr +import pypistats +from datetime import date +from dateutil.relativedelta import relativedelta +import pandas as pd + +def get_plot(lib, time): + data = pypistats.overall(lib, total=True, format="pandas") + data = data.groupby("category").get_group("with_mirrors").sort_values("date") + start_date = date.today() - relativedelta(months=int(time.split(" ")[0])) + data = data[(data['date'] > str(start_date))] + data.date = pd.to_datetime(pd.to_datetime(data.date)) + return gr.LinePlot(value=data, x="date", y="downloads", + tooltip=['date', 'downloads'], + title=f"Pypi downloads of {lib} over last {time}", + overlay_point=True, + height=400, + width=900) + +with gr.Blocks() as demo: + gr.Markdown( + """ + ## Pypi Download Stats 📈 + See live download stats for all of Hugging Face's open-source libraries 🤗 + """) + with gr.Row(): + lib = gr.Dropdown(["transformers", "datasets", "huggingface-hub", "gradio", "accelerate"], + value="gradio", label="Library") + time = gr.Dropdown(["3 months", "6 months", "9 months", "12 months"], + value="3 months", label="Downloads over the last...") + + plt = gr.LinePlot() + # You can add multiple event triggers in 2 lines like this + for event in [lib.change, time.change, demo.load]: + event(get_plot, [lib, time], [plt]) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_update/run.ipynb b/demos/blocks_update/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..cd094c525fa4fe509a2f066f562c28e60e4fe00a --- /dev/null +++ b/demos/blocks_update/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_update"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\n", " \"\"\"\n", " # Animal Generator\n", " Once you select a species, the detail panel should be visible.\n", " \"\"\"\n", " )\n", "\n", " species = gr.Radio(label=\"Animal Class\", choices=[\"Mammal\", \"Fish\", \"Bird\"])\n", " animal = gr.Dropdown(label=\"Animal\", choices=[])\n", "\n", " with gr.Column(visible=False) as details_col:\n", " weight = gr.Slider(0, 20)\n", " details = gr.Textbox(label=\"Extra Details\")\n", " generate_btn = gr.Button(\"Generate\")\n", " output = gr.Textbox(label=\"Output\")\n", "\n", " species_map = {\n", " \"Mammal\": [\"Elephant\", \"Giraffe\", \"Hamster\"],\n", " \"Fish\": [\"Shark\", \"Salmon\", \"Tuna\"],\n", " \"Bird\": [\"Chicken\", \"Eagle\", \"Hawk\"],\n", " }\n", "\n", " def filter_species(species):\n", " return gr.Dropdown(\n", " choices=species_map[species], value=species_map[species][1]\n", " ), gr.Column(visible=True)\n", "\n", " species.change(filter_species, species, [animal, details_col])\n", "\n", " def filter_weight(animal):\n", " if animal in (\"Elephant\", \"Shark\", \"Giraffe\"):\n", " return gr.Slider(maximum=100)\n", " else:\n", " return gr.Slider(maximum=20)\n", "\n", " animal.change(filter_weight, animal, weight)\n", " weight.change(lambda w: gr.Textbox(lines=int(w / 10) + 1), weight, details)\n", "\n", " generate_btn.click(lambda x: x, details, output)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/blocks_update/run.py b/demos/blocks_update/run.py new file mode 100644 index 0000000000000000000000000000000000000000..03d4e5a6fd139e4ffc8c6d51673a3dd5d28aa7cb --- /dev/null +++ b/demos/blocks_update/run.py @@ -0,0 +1,45 @@ +import gradio as gr + +with gr.Blocks() as demo: + gr.Markdown( + """ + # Animal Generator + Once you select a species, the detail panel should be visible. + """ + ) + + species = gr.Radio(label="Animal Class", choices=["Mammal", "Fish", "Bird"]) + animal = gr.Dropdown(label="Animal", choices=[]) + + with gr.Column(visible=False) as details_col: + weight = gr.Slider(0, 20) + details = gr.Textbox(label="Extra Details") + generate_btn = gr.Button("Generate") + output = gr.Textbox(label="Output") + + species_map = { + "Mammal": ["Elephant", "Giraffe", "Hamster"], + "Fish": ["Shark", "Salmon", "Tuna"], + "Bird": ["Chicken", "Eagle", "Hawk"], + } + + def filter_species(species): + return gr.Dropdown( + choices=species_map[species], value=species_map[species][1] + ), gr.Column(visible=True) + + species.change(filter_species, species, [animal, details_col]) + + def filter_weight(animal): + if animal in ("Elephant", "Shark", "Giraffe"): + return gr.Slider(maximum=100) + else: + return gr.Slider(maximum=20) + + animal.change(filter_weight, animal, weight) + weight.change(lambda w: gr.Textbox(lines=int(w / 10) + 1), weight, details) + + generate_btn.click(lambda x: x, details, output) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/calculator/examples/log.csv b/demos/calculator/examples/log.csv new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demos/calculator/run.ipynb b/demos/calculator/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..31d7b700f5d71366141762a1d4d5744cf4cebbef --- /dev/null +++ b/demos/calculator/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: calculator"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('examples')\n", "!wget -q -O examples/log.csv https://github.com/gradio-app/gradio/raw/main/demo/calculator/examples/log.csv"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def calculator(num1, operation, num2):\n", " if operation == \"add\":\n", " return num1 + num2\n", " elif operation == \"subtract\":\n", " return num1 - num2\n", " elif operation == \"multiply\":\n", " return num1 * num2\n", " elif operation == \"divide\":\n", " if num2 == 0:\n", " raise gr.Error(\"Cannot divide by zero!\")\n", " return num1 / num2\n", "\n", "demo = gr.Interface(\n", " calculator,\n", " [\n", " \"number\",\n", " gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"]),\n", " \"number\"\n", " ],\n", " \"number\",\n", " examples=[\n", " [45, \"add\", 3],\n", " [3.14, \"divide\", 2],\n", " [144, \"multiply\", 2.5],\n", " [0, \"subtract\", 1.2],\n", " ],\n", " title=\"Toy Calculator\",\n", " description=\"Here's a sample toy calculator.\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/calculator/run.py b/demos/calculator/run.py new file mode 100644 index 0000000000000000000000000000000000000000..9883f44db9e432ca4624c7a39038fabaa08d22ab --- /dev/null +++ b/demos/calculator/run.py @@ -0,0 +1,34 @@ +import gradio as gr + +def calculator(num1, operation, num2): + if operation == "add": + return num1 + num2 + elif operation == "subtract": + return num1 - num2 + elif operation == "multiply": + return num1 * num2 + elif operation == "divide": + if num2 == 0: + raise gr.Error("Cannot divide by zero!") + return num1 / num2 + +demo = gr.Interface( + calculator, + [ + "number", + gr.Radio(["add", "subtract", "multiply", "divide"]), + "number" + ], + "number", + examples=[ + [45, "add", 3], + [3.14, "divide", 2], + [144, "multiply", 2.5], + [0, "subtract", 1.2], + ], + title="Toy Calculator", + description="Here's a sample toy calculator.", +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/calculator/screenshot.gif b/demos/calculator/screenshot.gif new file mode 100644 index 0000000000000000000000000000000000000000..8ae0d0494adf069b4e23cf30956b43262258021c --- /dev/null +++ b/demos/calculator/screenshot.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3698fb03b6507ff954de47559f6830dfff88aa66487d2029a9bcf1c2f3762e08 +size 5718090 diff --git a/demos/cancel_events/run.ipynb b/demos/cancel_events/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..82e9c09d6b630bcb671733a2440c1c4c9ccafca6 --- /dev/null +++ b/demos/cancel_events/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: cancel_events"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import time\n", "import gradio as gr\n", "import atexit\n", "import pathlib\n", "\n", "log_file = pathlib.Path(__file__).parent / \"cancel_events_output_log.txt\"\n", "\n", "def fake_diffusion(steps):\n", " log_file.write_text(\"\")\n", " for i in range(steps):\n", " print(f\"Current step: {i}\")\n", " with log_file.open(\"a\") as f:\n", " f.write(f\"Current step: {i}\\n\")\n", " time.sleep(0.2)\n", " yield str(i)\n", "\n", "def long_prediction(*args, **kwargs):\n", " time.sleep(10)\n", " return 42\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " with gr.Column():\n", " n = gr.Slider(1, 10, value=9, step=1, label=\"Number Steps\")\n", " run = gr.Button(value=\"Start Iterating\")\n", " output = gr.Textbox(label=\"Iterative Output\")\n", " stop = gr.Button(value=\"Stop Iterating\")\n", " with gr.Column():\n", " textbox = gr.Textbox(label=\"Prompt\")\n", " prediction = gr.Number(label=\"Expensive Calculation\")\n", " run_pred = gr.Button(value=\"Run Expensive Calculation\")\n", " with gr.Column():\n", " cancel_on_change = gr.Textbox(\n", " label=\"Cancel Iteration and Expensive Calculation on Change\"\n", " )\n", " cancel_on_submit = gr.Textbox(\n", " label=\"Cancel Iteration and Expensive Calculation on Submit\"\n", " )\n", " echo = gr.Textbox(label=\"Echo\")\n", " with gr.Row():\n", " with gr.Column():\n", " image = gr.Image(\n", " sources=[\"webcam\"], label=\"Cancel on clear\", interactive=True\n", " )\n", " with gr.Column():\n", " video = gr.Video(\n", " sources=[\"webcam\"], label=\"Cancel on start recording\", interactive=True\n", " )\n", "\n", " click_event = run.click(fake_diffusion, n, output)\n", " stop.click(fn=None, inputs=None, outputs=None, cancels=[click_event])\n", " pred_event = run_pred.click(\n", " fn=long_prediction, inputs=[textbox], outputs=prediction\n", " )\n", "\n", " cancel_on_change.change(None, None, None, cancels=[click_event, pred_event])\n", " cancel_on_submit.submit(\n", " lambda s: s, cancel_on_submit, echo, cancels=[click_event, pred_event]\n", " )\n", " image.clear(None, None, None, cancels=[click_event, pred_event])\n", " video.start_recording(None, None, None, cancels=[click_event, pred_event])\n", "\n", " demo.queue(max_size=20)\n", " atexit.register(lambda: log_file.unlink())\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/cancel_events/run.py b/demos/cancel_events/run.py new file mode 100644 index 0000000000000000000000000000000000000000..d7c2f5700d89fcb00b92803e3c92628482d5f0fe --- /dev/null +++ b/demos/cancel_events/run.py @@ -0,0 +1,67 @@ +import time +import gradio as gr +import atexit +import pathlib + +log_file = pathlib.Path(__file__).parent / "cancel_events_output_log.txt" + +def fake_diffusion(steps): + log_file.write_text("") + for i in range(steps): + print(f"Current step: {i}") + with log_file.open("a") as f: + f.write(f"Current step: {i}\n") + time.sleep(0.2) + yield str(i) + +def long_prediction(*args, **kwargs): + time.sleep(10) + return 42 + +with gr.Blocks() as demo: + with gr.Row(): + with gr.Column(): + n = gr.Slider(1, 10, value=9, step=1, label="Number Steps") + run = gr.Button(value="Start Iterating") + output = gr.Textbox(label="Iterative Output") + stop = gr.Button(value="Stop Iterating") + with gr.Column(): + textbox = gr.Textbox(label="Prompt") + prediction = gr.Number(label="Expensive Calculation") + run_pred = gr.Button(value="Run Expensive Calculation") + with gr.Column(): + cancel_on_change = gr.Textbox( + label="Cancel Iteration and Expensive Calculation on Change" + ) + cancel_on_submit = gr.Textbox( + label="Cancel Iteration and Expensive Calculation on Submit" + ) + echo = gr.Textbox(label="Echo") + with gr.Row(): + with gr.Column(): + image = gr.Image( + sources=["webcam"], label="Cancel on clear", interactive=True + ) + with gr.Column(): + video = gr.Video( + sources=["webcam"], label="Cancel on start recording", interactive=True + ) + + click_event = run.click(fake_diffusion, n, output) + stop.click(fn=None, inputs=None, outputs=None, cancels=[click_event]) + pred_event = run_pred.click( + fn=long_prediction, inputs=[textbox], outputs=prediction + ) + + cancel_on_change.change(None, None, None, cancels=[click_event, pred_event]) + cancel_on_submit.submit( + lambda s: s, cancel_on_submit, echo, cancels=[click_event, pred_event] + ) + image.clear(None, None, None, cancels=[click_event, pred_event]) + video.start_recording(None, None, None, cancels=[click_event, pred_event]) + + demo.queue(max_size=20) + atexit.register(lambda: log_file.unlink()) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/chatbot_multimodal/files/avatar.png b/demos/chatbot_multimodal/files/avatar.png new file mode 100644 index 0000000000000000000000000000000000000000..8f1df7156f0a690a2415903061e19c20e24adac4 Binary files /dev/null and b/demos/chatbot_multimodal/files/avatar.png differ diff --git a/demos/chatbot_multimodal/requirements.txt b/demos/chatbot_multimodal/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..d42d0ad03bdf8ecf9756a38df5cedf8fe431db79 --- /dev/null +++ b/demos/chatbot_multimodal/requirements.txt @@ -0,0 +1 @@ +plotly \ No newline at end of file diff --git a/demos/chatbot_multimodal/run.ipynb b/demos/chatbot_multimodal/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f0eb8c1733e6523ea9397f99e44670b15cfba1d8 --- /dev/null +++ b/demos/chatbot_multimodal/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatbot_multimodal"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio plotly"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/avatar.png https://github.com/gradio-app/gradio/raw/main/demo/chatbot_multimodal/files/avatar.png\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/chatbot_multimodal/tuples_testcase.py"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.\n", "\n", "def print_like_dislike(x: gr.LikeData):\n", " print(x.index, x.value, x.liked)\n", "\n", "def add_message(history, message):\n", " for x in message[\"files\"]:\n", " history.append({\"role\": \"user\", \"content\": {\"path\": x}})\n", " if message[\"text\"] is not None:\n", " history.append({\"role\": \"user\", \"content\": message[\"text\"]})\n", " return history, gr.MultimodalTextbox(value=None, interactive=False)\n", "\n", "def bot(history: list):\n", " response = \"**That's cool!**\"\n", " history.append({\"role\": \"assistant\", \"content\": \"\"})\n", " for character in response:\n", " history[-1]['content'] += character\n", " time.sleep(0.05)\n", " yield history\n", "\n", "with gr.Blocks() as demo:\n", " chatbot = gr.Chatbot(\n", " elem_id=\"chatbot\",\n", " bubble_full_width=False,\n", " type=\"messages\"\n", " )\n", "\n", " chat_input = gr.MultimodalTextbox(interactive=True,\n", " file_count=\"multiple\",\n", " placeholder=\"Enter message or upload file...\", show_label=False)\n", "\n", " chat_msg = chat_input.submit(add_message, [chatbot, chat_input], [chatbot, chat_input])\n", " bot_msg = chat_msg.then(bot, chatbot, chatbot, api_name=\"bot_response\")\n", " bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])\n", "\n", " chatbot.like(print_like_dislike, None, None)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/chatbot_multimodal/run.py b/demos/chatbot_multimodal/run.py new file mode 100644 index 0000000000000000000000000000000000000000..bf08858503568902b1c910778cf82dce7d15a823 --- /dev/null +++ b/demos/chatbot_multimodal/run.py @@ -0,0 +1,42 @@ +import gradio as gr +import time + +# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text. + +def print_like_dislike(x: gr.LikeData): + print(x.index, x.value, x.liked) + +def add_message(history, message): + for x in message["files"]: + history.append({"role": "user", "content": {"path": x}}) + if message["text"] is not None: + history.append({"role": "user", "content": message["text"]}) + return history, gr.MultimodalTextbox(value=None, interactive=False) + +def bot(history: list): + response = "**That's cool!**" + history.append({"role": "assistant", "content": ""}) + for character in response: + history[-1]['content'] += character + time.sleep(0.05) + yield history + +with gr.Blocks() as demo: + chatbot = gr.Chatbot( + elem_id="chatbot", + bubble_full_width=False, + type="messages" + ) + + chat_input = gr.MultimodalTextbox(interactive=True, + file_count="multiple", + placeholder="Enter message or upload file...", show_label=False) + + chat_msg = chat_input.submit(add_message, [chatbot, chat_input], [chatbot, chat_input]) + bot_msg = chat_msg.then(bot, chatbot, chatbot, api_name="bot_response") + bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input]) + + chatbot.like(print_like_dislike, None, None) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/chatbot_multimodal/tuples_testcase.py b/demos/chatbot_multimodal/tuples_testcase.py new file mode 100644 index 0000000000000000000000000000000000000000..c4919c5e9223bbdb630c17d1a0afe09b8787856e --- /dev/null +++ b/demos/chatbot_multimodal/tuples_testcase.py @@ -0,0 +1,46 @@ +import gradio as gr +import plotly.express as px + +# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text. + +def random_plot(): + df = px.data.iris() + fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", + size='petal_length', hover_data=['petal_width']) + return fig + +def print_like_dislike(x: gr.LikeData): + print(x.index, x.value, x.liked) + +def add_message(history, message): + for x in message["files"]: + history.append(((x,), None)) + if message["text"] is not None: + history.append((message["text"], None)) + return history, gr.MultimodalTextbox(value=None, interactive=False) + +def bot(history): + history[-1][1] = "Cool!" + return history + +fig = random_plot() + +with gr.Blocks(fill_height=True) as demo: + chatbot = gr.Chatbot( + elem_id="chatbot", + bubble_full_width=False, + scale=1, + ) + + chat_input = gr.MultimodalTextbox(interactive=True, + file_count="multiple", + placeholder="Enter message or upload file...", show_label=False) + + chat_msg = chat_input.submit(add_message, [chatbot, chat_input], [chatbot, chat_input]) + bot_msg = chat_msg.then(bot, chatbot, chatbot, api_name="bot_response") + bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input]) + + chatbot.like(print_like_dislike, None, None) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/chatinterface_streaming_echo/run.ipynb b/demos/chatinterface_streaming_echo/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d077e6dcccf98d5aecc2a3241e8879adad82d154 --- /dev/null +++ b/demos/chatinterface_streaming_echo/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatinterface_streaming_echo"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import time\n", "import gradio as gr\n", "\n", "def slow_echo(message, history):\n", " for i in range(len(message)):\n", " time.sleep(0.05)\n", " yield \"You typed: \" + message[: i + 1]\n", "\n", "demo = gr.ChatInterface(slow_echo, type=\"messages\")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/chatinterface_streaming_echo/run.py b/demos/chatinterface_streaming_echo/run.py new file mode 100644 index 0000000000000000000000000000000000000000..6e87b7dcc0759273b1cd28304b5ef461634f85b3 --- /dev/null +++ b/demos/chatinterface_streaming_echo/run.py @@ -0,0 +1,12 @@ +import time +import gradio as gr + +def slow_echo(message, history): + for i in range(len(message)): + time.sleep(0.05) + yield "You typed: " + message[: i + 1] + +demo = gr.ChatInterface(slow_echo, type="messages") + +if __name__ == "__main__": + demo.launch() diff --git a/demos/clear_components/__init__.py b/demos/clear_components/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demos/clear_components/requirements.txt b/demos/clear_components/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..4b43f7e68658e87f033212c358b00c4d36075458 --- /dev/null +++ b/demos/clear_components/requirements.txt @@ -0,0 +1 @@ +matplotlib \ No newline at end of file diff --git a/demos/clear_components/run.ipynb b/demos/clear_components/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d6d175f924f8c4dd8b617e4ffc00059d125907f1 --- /dev/null +++ b/demos/clear_components/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: clear_components"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio matplotlib"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/clear_components/__init__.py"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from datetime import datetime\n", "import os\n", "import random\n", "import string\n", "import pandas as pd\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "def random_plot():\n", " start_year = 2020\n", " x = np.arange(start_year, start_year + 5)\n", " year_count = x.shape[0]\n", " plt_format = \"-\"\n", " fig = plt.figure()\n", " ax = fig.add_subplot(111)\n", " series = np.arange(0, year_count, dtype=float)\n", " series = series**2\n", " series += np.random.rand(year_count)\n", " ax.plot(x, series, plt_format)\n", " return fig\n", "\n", "images = [\n", " \"https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80\",\n", " \"https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80\",\n", " \"https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80\",\n", "]\n", "file_dir = os.path.join(os.path.abspath(''), \"..\", \"kitchen_sink\", \"files\")\n", "model3d_dir = os.path.join(os.path.abspath(''), \"..\", \"model3D\", \"files\")\n", "highlighted_text_output_1 = [\n", " {\n", " \"entity\": \"I-LOC\",\n", " \"score\": 0.9988978,\n", " \"index\": 2,\n", " \"word\": \"Chicago\",\n", " \"start\": 5,\n", " \"end\": 12,\n", " },\n", " {\n", " \"entity\": \"I-MISC\",\n", " \"score\": 0.9958592,\n", " \"index\": 5,\n", " \"word\": \"Pakistani\",\n", " \"start\": 22,\n", " \"end\": 31,\n", " },\n", "]\n", "highlighted_text_output_2 = [\n", " {\n", " \"entity\": \"I-LOC\",\n", " \"score\": 0.9988978,\n", " \"index\": 2,\n", " \"word\": \"Chicago\",\n", " \"start\": 5,\n", " \"end\": 12,\n", " },\n", " {\n", " \"entity\": \"I-LOC\",\n", " \"score\": 0.9958592,\n", " \"index\": 5,\n", " \"word\": \"Pakistan\",\n", " \"start\": 22,\n", " \"end\": 30,\n", " },\n", "]\n", "\n", "highlighted_text = \"Does Chicago have any Pakistani restaurants\"\n", "\n", "def random_model3d():\n", " model_3d = random.choice(\n", " [os.path.join(model3d_dir, model) for model in os.listdir(model3d_dir) if model != \"source.txt\"]\n", " )\n", " return model_3d\n", "\n", "components = [\n", " gr.Textbox(value=lambda: datetime.now(), label=\"Current Time\"),\n", " gr.Number(value=lambda: random.random(), label=\"Random Percentage\"),\n", " gr.Slider(minimum=0, maximum=100, randomize=True, label=\"Slider with randomize\"),\n", " gr.Slider(\n", " minimum=0,\n", " maximum=1,\n", " value=lambda: random.random(),\n", " label=\"Slider with value func\",\n", " ),\n", " gr.Checkbox(value=lambda: random.random() > 0.5, label=\"Random Checkbox\"),\n", " gr.CheckboxGroup(\n", " choices=[\"a\", \"b\", \"c\", \"d\"],\n", " value=lambda: random.choice([\"a\", \"b\", \"c\", \"d\"]),\n", " label=\"Random CheckboxGroup\",\n", " ),\n", " gr.Radio(\n", " choices=list(string.ascii_lowercase),\n", " value=lambda: random.choice(string.ascii_lowercase),\n", " ),\n", " gr.Dropdown(\n", " choices=[\"a\", \"b\", \"c\", \"d\", \"e\"],\n", " value=lambda: random.choice([\"a\", \"b\", \"c\"]),\n", " ),\n", " gr.Image(\n", " value=lambda: random.choice(images)\n", " ),\n", " gr.Video(value=lambda: os.path.join(file_dir, \"world.mp4\")),\n", " gr.Audio(value=lambda: os.path.join(file_dir, \"cantina.wav\")),\n", " gr.File(\n", " value=lambda: random.choice(\n", " [os.path.join(file_dir, img) for img in os.listdir(file_dir)]\n", " )\n", " ),\n", " gr.Dataframe(\n", " value=lambda: pd.DataFrame({\"random_number_rows\": range(5)}, columns=[\"one\", \"two\", \"three\"]) # type: ignore\n", " ),\n", " gr.ColorPicker(value=lambda: random.choice([\"#000000\", \"#ff0000\", \"#0000FF\"])),\n", " gr.Label(value=lambda: random.choice([\"Pedestrian\", \"Car\", \"Cyclist\"])),\n", " gr.HighlightedText(\n", " value=lambda: random.choice(\n", " [\n", " {\"text\": highlighted_text, \"entities\": highlighted_text_output_1},\n", " {\"text\": highlighted_text, \"entities\": highlighted_text_output_2},\n", " ]\n", " ),\n", " ),\n", " gr.JSON(value=lambda: random.choice([{\"a\": 1}, {\"b\": 2}])),\n", " gr.HTML(\n", " value=lambda: random.choice(\n", " [\n", " '

I am red

',\n", " '

I am blue

',\n", " ]\n", " )\n", " ),\n", " gr.Gallery(\n", " value=lambda: images\n", " ),\n", " gr.Model3D(value=random_model3d),\n", " gr.Plot(value=random_plot),\n", " gr.Markdown(value=lambda: f\"### {random.choice(['Hello', 'Hi', 'Goodbye!'])}\"),\n", "]\n", "\n", "def evaluate_values(*args):\n", " are_false = []\n", " for a in args:\n", " if isinstance(a, (pd.DataFrame, np.ndarray)):\n", " are_false.append(not a.any().any()) # type: ignore\n", " elif isinstance(a, str) and a.startswith(\"#\"):\n", " are_false.append(a == \"#000000\")\n", " else:\n", " are_false.append(not a)\n", " return all(are_false)\n", "\n", "with gr.Blocks() as demo:\n", " for i, component in enumerate(components):\n", " component.label = f\"component_{str(i).zfill(2)}\"\n", " component.render()\n", " clear = gr.ClearButton(value=\"Clear\", components=components)\n", " result = gr.Textbox(label=\"Are all cleared?\")\n", " hide = gr.Button(value=\"Hide\")\n", " reveal = gr.Button(value=\"Reveal\")\n", " clear_button_and_components = components + [clear]\n", " hide.click(\n", " lambda: [c.__class__(visible=False) for c in clear_button_and_components],\n", " inputs=[],\n", " outputs=clear_button_and_components\n", " )\n", " reveal.click(\n", " lambda: [c.__class__(visible=True) for c in clear_button_and_components],\n", " inputs=[],\n", " outputs=clear_button_and_components\n", " )\n", " get_value = gr.Button(value=\"Get Values\")\n", " get_value.click(evaluate_values, components, result)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/clear_components/run.py b/demos/clear_components/run.py new file mode 100644 index 0000000000000000000000000000000000000000..3457e12c5c06f662a117214866e149f0c88358e4 --- /dev/null +++ b/demos/clear_components/run.py @@ -0,0 +1,174 @@ +import gradio as gr +from datetime import datetime +import os +import random +import string +import pandas as pd + +import numpy as np +import matplotlib.pyplot as plt + +def random_plot(): + start_year = 2020 + x = np.arange(start_year, start_year + 5) + year_count = x.shape[0] + plt_format = "-" + fig = plt.figure() + ax = fig.add_subplot(111) + series = np.arange(0, year_count, dtype=float) + series = series**2 + series += np.random.rand(year_count) + ax.plot(x, series, plt_format) + return fig + +images = [ + "https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80", + "https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80", + "https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80", +] +file_dir = os.path.join(os.path.dirname(__file__), "..", "kitchen_sink", "files") +model3d_dir = os.path.join(os.path.dirname(__file__), "..", "model3D", "files") +highlighted_text_output_1 = [ + { + "entity": "I-LOC", + "score": 0.9988978, + "index": 2, + "word": "Chicago", + "start": 5, + "end": 12, + }, + { + "entity": "I-MISC", + "score": 0.9958592, + "index": 5, + "word": "Pakistani", + "start": 22, + "end": 31, + }, +] +highlighted_text_output_2 = [ + { + "entity": "I-LOC", + "score": 0.9988978, + "index": 2, + "word": "Chicago", + "start": 5, + "end": 12, + }, + { + "entity": "I-LOC", + "score": 0.9958592, + "index": 5, + "word": "Pakistan", + "start": 22, + "end": 30, + }, +] + +highlighted_text = "Does Chicago have any Pakistani restaurants" + +def random_model3d(): + model_3d = random.choice( + [os.path.join(model3d_dir, model) for model in os.listdir(model3d_dir) if model != "source.txt"] + ) + return model_3d + +components = [ + gr.Textbox(value=lambda: datetime.now(), label="Current Time"), + gr.Number(value=lambda: random.random(), label="Random Percentage"), + gr.Slider(minimum=0, maximum=100, randomize=True, label="Slider with randomize"), + gr.Slider( + minimum=0, + maximum=1, + value=lambda: random.random(), + label="Slider with value func", + ), + gr.Checkbox(value=lambda: random.random() > 0.5, label="Random Checkbox"), + gr.CheckboxGroup( + choices=["a", "b", "c", "d"], + value=lambda: random.choice(["a", "b", "c", "d"]), + label="Random CheckboxGroup", + ), + gr.Radio( + choices=list(string.ascii_lowercase), + value=lambda: random.choice(string.ascii_lowercase), + ), + gr.Dropdown( + choices=["a", "b", "c", "d", "e"], + value=lambda: random.choice(["a", "b", "c"]), + ), + gr.Image( + value=lambda: random.choice(images) + ), + gr.Video(value=lambda: os.path.join(file_dir, "world.mp4")), + gr.Audio(value=lambda: os.path.join(file_dir, "cantina.wav")), + gr.File( + value=lambda: random.choice( + [os.path.join(file_dir, img) for img in os.listdir(file_dir)] + ) + ), + gr.Dataframe( + value=lambda: pd.DataFrame({"random_number_rows": range(5)}, columns=["one", "two", "three"]) # type: ignore + ), + gr.ColorPicker(value=lambda: random.choice(["#000000", "#ff0000", "#0000FF"])), + gr.Label(value=lambda: random.choice(["Pedestrian", "Car", "Cyclist"])), + gr.HighlightedText( + value=lambda: random.choice( + [ + {"text": highlighted_text, "entities": highlighted_text_output_1}, + {"text": highlighted_text, "entities": highlighted_text_output_2}, + ] + ), + ), + gr.JSON(value=lambda: random.choice([{"a": 1}, {"b": 2}])), + gr.HTML( + value=lambda: random.choice( + [ + '

I am red

', + '

I am blue

', + ] + ) + ), + gr.Gallery( + value=lambda: images + ), + gr.Model3D(value=random_model3d), + gr.Plot(value=random_plot), + gr.Markdown(value=lambda: f"### {random.choice(['Hello', 'Hi', 'Goodbye!'])}"), +] + +def evaluate_values(*args): + are_false = [] + for a in args: + if isinstance(a, (pd.DataFrame, np.ndarray)): + are_false.append(not a.any().any()) # type: ignore + elif isinstance(a, str) and a.startswith("#"): + are_false.append(a == "#000000") + else: + are_false.append(not a) + return all(are_false) + +with gr.Blocks() as demo: + for i, component in enumerate(components): + component.label = f"component_{str(i).zfill(2)}" + component.render() + clear = gr.ClearButton(value="Clear", components=components) + result = gr.Textbox(label="Are all cleared?") + hide = gr.Button(value="Hide") + reveal = gr.Button(value="Reveal") + clear_button_and_components = components + [clear] + hide.click( + lambda: [c.__class__(visible=False) for c in clear_button_and_components], + inputs=[], + outputs=clear_button_and_components + ) + reveal.click( + lambda: [c.__class__(visible=True) for c in clear_button_and_components], + inputs=[], + outputs=clear_button_and_components + ) + get_value = gr.Button(value="Get Values") + get_value.click(evaluate_values, components, result) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/code/file.css b/demos/code/file.css new file mode 100644 index 0000000000000000000000000000000000000000..abc61b61cbb2045ad539bb1cc23c232a97aab8ee --- /dev/null +++ b/demos/code/file.css @@ -0,0 +1,11 @@ +.class { + color: blue; +} + +#id { + color: pink; +} + +div { + color: purple; +} \ No newline at end of file diff --git a/demos/code/run.ipynb b/demos/code/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e7daf42b04a66bf6a7504a7cc4c55032a3bc13c3 --- /dev/null +++ b/demos/code/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: code"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/code/file.css"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "from time import sleep\n", "\n", "css_file = os.path.join(os.path.abspath(''), \"file.css\")\n", "\n", "def set_lang(language):\n", " print(language)\n", " return gr.Code(language=language)\n", "\n", "def set_lang_from_path():\n", " sleep(1)\n", " return gr.Code(open(css_file).read(), language=\"css\")\n", "\n", "def code(language, code):\n", " return gr.Code(code, language=language)\n", "\n", "io = gr.Interface(lambda x: x, \"code\", \"code\")\n", "\n", "with gr.Blocks() as demo:\n", " lang = gr.Dropdown(value=\"python\", choices=gr.Code.languages)\n", " with gr.Row():\n", " code_in = gr.Code(\n", " language=\"python\",\n", " label=\"Input\",\n", " value='def all_odd_elements(sequence):\\n \"\"\"Returns every odd element of the sequence.\"\"\"',\n", " )\n", " code_out = gr.Code(label=\"Output\")\n", " btn = gr.Button(\"Run\")\n", " btn_two = gr.Button(\"Load File\")\n", "\n", " lang.change(set_lang, inputs=lang, outputs=code_in)\n", " btn.click(code, inputs=[lang, code_in], outputs=code_out)\n", " btn_two.click(set_lang_from_path, inputs=None, outputs=code_out)\n", " io.render()\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/code/run.py b/demos/code/run.py new file mode 100644 index 0000000000000000000000000000000000000000..03cbad845295d7982e583f7d5f2a270ad428d239 --- /dev/null +++ b/demos/code/run.py @@ -0,0 +1,38 @@ +import gradio as gr +import os +from time import sleep + +css_file = os.path.join(os.path.dirname(__file__), "file.css") + +def set_lang(language): + print(language) + return gr.Code(language=language) + +def set_lang_from_path(): + sleep(1) + return gr.Code(open(css_file).read(), language="css") + +def code(language, code): + return gr.Code(code, language=language) + +io = gr.Interface(lambda x: x, "code", "code") + +with gr.Blocks() as demo: + lang = gr.Dropdown(value="python", choices=gr.Code.languages) + with gr.Row(): + code_in = gr.Code( + language="python", + label="Input", + value='def all_odd_elements(sequence):\n """Returns every odd element of the sequence."""', + ) + code_out = gr.Code(label="Output") + btn = gr.Button("Run") + btn_two = gr.Button("Load File") + + lang.change(set_lang, inputs=lang, outputs=code_in) + btn.click(code, inputs=[lang, code_in], outputs=code_out) + btn_two.click(set_lang_from_path, inputs=None, outputs=code_out) + io.render() + +if __name__ == "__main__": + demo.launch() diff --git a/demos/fake_diffusion_with_gif/run.ipynb b/demos/fake_diffusion_with_gif/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..91a2905550b70aa6ed878269fffb0fa6d751a495 --- /dev/null +++ b/demos/fake_diffusion_with_gif/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: fake_diffusion_with_gif"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import time\n", "import os\n", "from PIL import Image\n", "import requests\n", "from io import BytesIO\n", "\n", "def create_gif(images):\n", " pil_images = []\n", " for image in images:\n", " if isinstance(image, str):\n", " response = requests.get(image)\n", " image = Image.open(BytesIO(response.content))\n", " else:\n", " image = Image.fromarray((image * 255).astype(np.uint8))\n", " pil_images.append(image)\n", " fp_out = os.path.join(os.path.abspath(''), \"image.gif\")\n", " img = pil_images.pop(0)\n", " img.save(fp=fp_out, format='GIF', append_images=pil_images,\n", " save_all=True, duration=400, loop=0)\n", " return fp_out\n", "\n", "def fake_diffusion(steps):\n", " rng = np.random.default_rng()\n", " images = []\n", " for _ in range(steps):\n", " time.sleep(1)\n", " image = rng.random((600, 600, 3))\n", " images.append(image)\n", " yield image, gr.Image(visible=False)\n", "\n", " time.sleep(1)\n", " image = \"https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg\"\n", " images.append(image)\n", " gif_path = create_gif(images)\n", "\n", " yield image, gr.Image(value=gif_path, visible=True)\n", "\n", "demo = gr.Interface(fake_diffusion,\n", " inputs=gr.Slider(1, 10, 3, step=1),\n", " outputs=[\"image\", gr.Image(label=\"All Images\", visible=False)])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/fake_diffusion_with_gif/run.py b/demos/fake_diffusion_with_gif/run.py new file mode 100644 index 0000000000000000000000000000000000000000..833686cf5c946ea573f4222256ac8dba4143d832 --- /dev/null +++ b/demos/fake_diffusion_with_gif/run.py @@ -0,0 +1,45 @@ +import gradio as gr +import numpy as np +import time +import os +from PIL import Image +import requests +from io import BytesIO + +def create_gif(images): + pil_images = [] + for image in images: + if isinstance(image, str): + response = requests.get(image) + image = Image.open(BytesIO(response.content)) + else: + image = Image.fromarray((image * 255).astype(np.uint8)) + pil_images.append(image) + fp_out = os.path.join(os.path.dirname(__file__), "image.gif") + img = pil_images.pop(0) + img.save(fp=fp_out, format='GIF', append_images=pil_images, + save_all=True, duration=400, loop=0) + return fp_out + +def fake_diffusion(steps): + rng = np.random.default_rng() + images = [] + for _ in range(steps): + time.sleep(1) + image = rng.random((600, 600, 3)) + images.append(image) + yield image, gr.Image(visible=False) + + time.sleep(1) + image = "https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg" + images.append(image) + gif_path = create_gif(images) + + yield image, gr.Image(value=gif_path, visible=True) + +demo = gr.Interface(fake_diffusion, + inputs=gr.Slider(1, 10, 3, step=1), + outputs=["image", gr.Image(label="All Images", visible=False)]) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/fake_gan/DESCRIPTION.md b/demos/fake_gan/DESCRIPTION.md new file mode 100644 index 0000000000000000000000000000000000000000..2eb2efc0f04eb97bb167e391ab7aa8f5dea8e3b4 --- /dev/null +++ b/demos/fake_gan/DESCRIPTION.md @@ -0,0 +1 @@ +This is a fake GAN that shows how to create a text-to-image interface for image generation. Check out the Stable Diffusion demo for more: https://hf.co/spaces/stabilityai/stable-diffusion/ \ No newline at end of file diff --git a/demos/fake_gan/run.ipynb b/demos/fake_gan/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..59ab8560bf475ef9b5142ccfda4227b77c3769e1 --- /dev/null +++ b/demos/fake_gan/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: fake_gan\n", "### This is a fake GAN that shows how to create a text-to-image interface for image generation. Check out the Stable Diffusion demo for more: https://hf.co/spaces/stabilityai/stable-diffusion/\n", " "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# This demo needs to be run from the repo folder.\n", "# python demo/fake_gan/run.py\n", "import random\n", "\n", "import gradio as gr\n", "\n", "def fake_gan():\n", " images = [\n", " (random.choice(\n", " [\n", " \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-1151ce9f4b2043de0d2e3b7826127998.jpg\",\n", " \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-116b5e92936b766b7fdfc242649337f7.jpg\",\n", " \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-1163530ca19b5cebe1b002b8ec67b6fc.jpg\",\n", " \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-1116395d6e6a6581eef8b8038f4c8e55.jpg\",\n", " \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-11319be65db395d0e8e6855d18ddcef0.jpg\",\n", " ]\n", " ), f\"label {i}\")\n", " for i in range(3)\n", " ]\n", " return images\n", "\n", "with gr.Blocks() as demo:\n", " gallery = gr.Gallery(\n", " label=\"Generated images\", show_label=False, elem_id=\"gallery\"\n", " , columns=[3], rows=[1], object_fit=\"contain\", height=\"auto\")\n", " btn = gr.Button(\"Generate images\", scale=0)\n", "\n", " btn.click(fake_gan, None, gallery)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/fake_gan/run.py b/demos/fake_gan/run.py new file mode 100644 index 0000000000000000000000000000000000000000..8a9d69cb10a6f16da97d1c028a2eca7fca588a49 --- /dev/null +++ b/demos/fake_gan/run.py @@ -0,0 +1,31 @@ +# This demo needs to be run from the repo folder. +# python demo/fake_gan/run.py +import random + +import gradio as gr + +def fake_gan(): + images = [ + (random.choice( + [ + "http://www.marketingtool.online/en/face-generator/img/faces/avatar-1151ce9f4b2043de0d2e3b7826127998.jpg", + "http://www.marketingtool.online/en/face-generator/img/faces/avatar-116b5e92936b766b7fdfc242649337f7.jpg", + "http://www.marketingtool.online/en/face-generator/img/faces/avatar-1163530ca19b5cebe1b002b8ec67b6fc.jpg", + "http://www.marketingtool.online/en/face-generator/img/faces/avatar-1116395d6e6a6581eef8b8038f4c8e55.jpg", + "http://www.marketingtool.online/en/face-generator/img/faces/avatar-11319be65db395d0e8e6855d18ddcef0.jpg", + ] + ), f"label {i}") + for i in range(3) + ] + return images + +with gr.Blocks() as demo: + gallery = gr.Gallery( + label="Generated images", show_label=False, elem_id="gallery" + , columns=[3], rows=[1], object_fit="contain", height="auto") + btn = gr.Button("Generate images", scale=0) + + btn.click(fake_gan, None, gallery) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/file_explorer_component_events/dir1/bar.txt b/demos/file_explorer_component_events/dir1/bar.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demos/file_explorer_component_events/dir1/foo.txt b/demos/file_explorer_component_events/dir1/foo.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demos/file_explorer_component_events/dir2/baz.png b/demos/file_explorer_component_events/dir2/baz.png new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demos/file_explorer_component_events/dir2/foo.png b/demos/file_explorer_component_events/dir2/foo.png new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demos/file_explorer_component_events/dir3/dir3_bar.log b/demos/file_explorer_component_events/dir3/dir3_bar.log new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demos/file_explorer_component_events/dir3/dir3_foo.txt b/demos/file_explorer_component_events/dir3/dir3_foo.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demos/file_explorer_component_events/dir3/dir4/dir5/dir5_foo.txt b/demos/file_explorer_component_events/dir3/dir4/dir5/dir5_foo.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demos/file_explorer_component_events/dir3/dir4/dir7/dir7_foo.txt b/demos/file_explorer_component_events/dir3/dir4/dir7/dir7_foo.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demos/file_explorer_component_events/dir3/dir4/dir_4_bar.log b/demos/file_explorer_component_events/dir3/dir4/dir_4_bar.log new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demos/file_explorer_component_events/dir3/dir4/dir_4_foo.txt b/demos/file_explorer_component_events/dir3/dir4/dir_4_foo.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demos/file_explorer_component_events/run.ipynb b/demos/file_explorer_component_events/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..bc81feb0bd465ee19ac6bb40f83353b653c8f78d --- /dev/null +++ b/demos/file_explorer_component_events/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: file_explorer_component_events"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('dir1')\n", "!wget -q -O dir1/bar.txt https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir1/bar.txt\n", "!wget -q -O dir1/foo.txt https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir1/foo.txt\n", "os.mkdir('dir2')\n", "!wget -q -O dir2/baz.png https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir2/baz.png\n", "!wget -q -O dir2/foo.png https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir2/foo.png\n", "os.mkdir('dir3')\n", "!wget -q -O dir3/dir3_bar.log https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir3/dir3_bar.log\n", "!wget -q -O dir3/dir3_foo.txt https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir3/dir3_foo.txt\n", "!wget -q -O dir3/dir4 https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir3/dir4"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from pathlib import Path\n", "\n", "base_root = Path(__file__).parent.resolve()\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " dd = gr.Dropdown(label=\"Select File Explorer Root\",\n", " value=str(base_root / \"dir1\"),\n", " choices=[str(base_root / \"dir1\"), str(base_root / \"dir2\"),\n", " str(base_root / \"dir3\")])\n", " with gr.Group():\n", " txt_only_glob = gr.Checkbox(label=\"Show only text files\", value=False)\n", " ignore_txt_in_glob = gr.Checkbox(label=\"Ignore text files in glob\", value=False)\n", "\n", " fe = gr.FileExplorer(root_dir=str(base_root / \"dir1\"),\n", " glob=\"**/*\", interactive=True)\n", " textbox = gr.Textbox(label=\"Selected Directory\")\n", " run = gr.Button(\"Run\")\n", " total_changes = gr.Number(0, elem_id=\"total-changes\")\n", "\n", " txt_only_glob.select(lambda s: gr.FileExplorer(glob=\"*.txt\" if s else \"*\") ,\n", " inputs=[txt_only_glob], outputs=[fe])\n", " ignore_txt_in_glob.select(lambda s: gr.FileExplorer(ignore_glob=\"*.txt\" if s else None),\n", " inputs=[ignore_txt_in_glob], outputs=[fe])\n", "\n", " dd.select(lambda s: gr.FileExplorer(root_dir=s), inputs=[dd], outputs=[fe])\n", " run.click(lambda s: \",\".join(s) if isinstance(s, list) else s, inputs=[fe], outputs=[textbox])\n", " fe.change(lambda num: num + 1, inputs=total_changes, outputs=total_changes)\n", "\n", " with gr.Row():\n", " a = gr.Textbox(elem_id=\"input-box\")\n", " a.change(lambda x: x, inputs=[a])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/file_explorer_component_events/run.py b/demos/file_explorer_component_events/run.py new file mode 100644 index 0000000000000000000000000000000000000000..f8e5349a055983a50d8f941d97aa18233a5e20a4 --- /dev/null +++ b/demos/file_explorer_component_events/run.py @@ -0,0 +1,36 @@ +import gradio as gr +from pathlib import Path + +base_root = Path(__file__).parent.resolve() + +with gr.Blocks() as demo: + with gr.Row(): + dd = gr.Dropdown(label="Select File Explorer Root", + value=str(base_root / "dir1"), + choices=[str(base_root / "dir1"), str(base_root / "dir2"), + str(base_root / "dir3")]) + with gr.Group(): + txt_only_glob = gr.Checkbox(label="Show only text files", value=False) + ignore_txt_in_glob = gr.Checkbox(label="Ignore text files in glob", value=False) + + fe = gr.FileExplorer(root_dir=str(base_root / "dir1"), + glob="**/*", interactive=True) + textbox = gr.Textbox(label="Selected Directory") + run = gr.Button("Run") + total_changes = gr.Number(0, elem_id="total-changes") + + txt_only_glob.select(lambda s: gr.FileExplorer(glob="*.txt" if s else "*") , + inputs=[txt_only_glob], outputs=[fe]) + ignore_txt_in_glob.select(lambda s: gr.FileExplorer(ignore_glob="*.txt" if s else None), + inputs=[ignore_txt_in_glob], outputs=[fe]) + + dd.select(lambda s: gr.FileExplorer(root_dir=s), inputs=[dd], outputs=[fe]) + run.click(lambda s: ",".join(s) if isinstance(s, list) else s, inputs=[fe], outputs=[textbox]) + fe.change(lambda num: num + 1, inputs=total_changes, outputs=total_changes) + + with gr.Row(): + a = gr.Textbox(elem_id="input-box") + a.change(lambda x: x, inputs=[a]) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/image_editor_events/run.ipynb b/demos/image_editor_events/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5ca54231867de03662aaa9a1d133caf32227ee32 --- /dev/null +++ b/demos/image_editor_events/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_editor_events"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def predict(im):\n", " return im[\"composite\"]\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Group():\n", " with gr.Row():\n", " im = gr.ImageEditor(\n", " type=\"numpy\",\n", " crop_size=\"1:1\",\n", " elem_id=\"image_editor\",\n", " )\n", " im_preview = gr.Image()\n", " with gr.Group():\n", " with gr.Row():\n", "\n", " n_upload = gr.Label(\n", " 0,\n", " label=\"upload\",\n", " elem_id=\"upload\",\n", " )\n", " n_change = gr.Label(\n", " 0,\n", " label=\"change\",\n", " elem_id=\"change\",\n", " )\n", " n_input = gr.Label(\n", " 0,\n", " label=\"input\",\n", " elem_id=\"input\",\n", " )\n", " n_apply = gr.Label(\n", " 0,\n", " label=\"apply\",\n", " elem_id=\"apply\",\n", " )\n", " clear_btn = gr.Button(\"Clear\", elem_id=\"clear\")\n", "\n", " im.upload(\n", " lambda x: int(x) + 1, outputs=n_upload, inputs=n_upload, show_progress=\"hidden\"\n", " )\n", " im.change(\n", " lambda x: int(x) + 1, outputs=n_change, inputs=n_change, show_progress=\"hidden\"\n", " )\n", " im.input(\n", " lambda x: int(x) + 1, outputs=n_input, inputs=n_input, show_progress=\"hidden\"\n", " )\n", " im.apply(\n", " lambda x: int(x) + 1, outputs=n_apply, inputs=n_apply, show_progress=\"hidden\"\n", " )\n", " im.change(predict, outputs=im_preview, inputs=im, show_progress=\"hidden\")\n", " clear_btn.click(\n", " lambda: None,\n", " None,\n", " im,\n", " )\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/image_editor_events/run.py b/demos/image_editor_events/run.py new file mode 100644 index 0000000000000000000000000000000000000000..53884466f5fa1bf236e7c8415b408597de559f37 --- /dev/null +++ b/demos/image_editor_events/run.py @@ -0,0 +1,60 @@ +import gradio as gr + +def predict(im): + return im["composite"] + +with gr.Blocks() as demo: + with gr.Group(): + with gr.Row(): + im = gr.ImageEditor( + type="numpy", + crop_size="1:1", + elem_id="image_editor", + ) + im_preview = gr.Image() + with gr.Group(): + with gr.Row(): + + n_upload = gr.Label( + 0, + label="upload", + elem_id="upload", + ) + n_change = gr.Label( + 0, + label="change", + elem_id="change", + ) + n_input = gr.Label( + 0, + label="input", + elem_id="input", + ) + n_apply = gr.Label( + 0, + label="apply", + elem_id="apply", + ) + clear_btn = gr.Button("Clear", elem_id="clear") + + im.upload( + lambda x: int(x) + 1, outputs=n_upload, inputs=n_upload, show_progress="hidden" + ) + im.change( + lambda x: int(x) + 1, outputs=n_change, inputs=n_change, show_progress="hidden" + ) + im.input( + lambda x: int(x) + 1, outputs=n_input, inputs=n_input, show_progress="hidden" + ) + im.apply( + lambda x: int(x) + 1, outputs=n_apply, inputs=n_apply, show_progress="hidden" + ) + im.change(predict, outputs=im_preview, inputs=im, show_progress="hidden") + clear_btn.click( + lambda: None, + None, + im, + ) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/image_mod_default_image/images/cheetah1.jpg b/demos/image_mod_default_image/images/cheetah1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..c510ff30e09c1ce410afa499f0bfc3a63c751134 Binary files /dev/null and b/demos/image_mod_default_image/images/cheetah1.jpg differ diff --git a/demos/image_mod_default_image/images/lion.jpg b/demos/image_mod_default_image/images/lion.jpg new file mode 100644 index 0000000000000000000000000000000000000000..e9bf9f5d0816d6201b4862088dc74476249a6a70 Binary files /dev/null and b/demos/image_mod_default_image/images/lion.jpg differ diff --git a/demos/image_mod_default_image/images/logo.png b/demos/image_mod_default_image/images/logo.png new file mode 100644 index 0000000000000000000000000000000000000000..8f1df7156f0a690a2415903061e19c20e24adac4 Binary files /dev/null and b/demos/image_mod_default_image/images/logo.png differ diff --git a/demos/image_mod_default_image/run.ipynb b/demos/image_mod_default_image/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..cc5f2da7e9b7a0150c94f3ce3f8fecc62e3311cb --- /dev/null +++ b/demos/image_mod_default_image/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_mod_default_image"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('images')\n", "!wget -q -O images/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_mod_default_image/images/cheetah1.jpg\n", "!wget -q -O images/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_mod_default_image/images/lion.jpg\n", "!wget -q -O images/logo.png https://github.com/gradio-app/gradio/raw/main/demo/image_mod_default_image/images/logo.png"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "def image_mod(image):\n", " return image.rotate(45)\n", "\n", "cheetah = os.path.join(os.path.abspath(''), \"images/cheetah1.jpg\")\n", "\n", "demo = gr.Interface(image_mod, gr.Image(type=\"pil\", value=cheetah), \"image\",\n", " flagging_options=[\"blurry\", \"incorrect\", \"other\"], examples=[\n", " os.path.join(os.path.abspath(''), \"images/lion.jpg\"),\n", " os.path.join(os.path.abspath(''), \"images/logo.png\")\n", " ])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch(max_file_size=\"70kb\")\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/image_mod_default_image/run.py b/demos/image_mod_default_image/run.py new file mode 100644 index 0000000000000000000000000000000000000000..3eb42701a4e949205be80555d8855f41a3edd0fa --- /dev/null +++ b/demos/image_mod_default_image/run.py @@ -0,0 +1,16 @@ +import gradio as gr +import os + +def image_mod(image): + return image.rotate(45) + +cheetah = os.path.join(os.path.dirname(__file__), "images/cheetah1.jpg") + +demo = gr.Interface(image_mod, gr.Image(type="pil", value=cheetah), "image", + flagging_options=["blurry", "incorrect", "other"], examples=[ + os.path.join(os.path.dirname(__file__), "images/lion.jpg"), + os.path.join(os.path.dirname(__file__), "images/logo.png") + ]) + +if __name__ == "__main__": + demo.launch(max_file_size="70kb") diff --git a/demos/image_segmentation/DESCRIPTION.md b/demos/image_segmentation/DESCRIPTION.md new file mode 100644 index 0000000000000000000000000000000000000000..dbba2ae29e4ed391bfd1681fb2fe7d0efcb34222 --- /dev/null +++ b/demos/image_segmentation/DESCRIPTION.md @@ -0,0 +1 @@ +Simple image segmentation using gradio's AnnotatedImage component. \ No newline at end of file diff --git a/demos/image_segmentation/run.ipynb b/demos/image_segmentation/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ac39c2862e509bcadc97ace2367edf2a8672e98e --- /dev/null +++ b/demos/image_segmentation/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_segmentation\n", "### Simple image segmentation using gradio's AnnotatedImage component.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import random\n", "\n", "with gr.Blocks() as demo:\n", " section_labels = [\n", " \"apple\",\n", " \"banana\",\n", " \"carrot\",\n", " \"donut\",\n", " \"eggplant\",\n", " \"fish\",\n", " \"grapes\",\n", " \"hamburger\",\n", " \"ice cream\",\n", " \"juice\",\n", " ]\n", "\n", " with gr.Row():\n", " num_boxes = gr.Slider(0, 5, 2, step=1, label=\"Number of boxes\")\n", " num_segments = gr.Slider(0, 5, 1, step=1, label=\"Number of segments\")\n", "\n", " with gr.Row():\n", " img_input = gr.Image()\n", " img_output = gr.AnnotatedImage(\n", " color_map={\"banana\": \"#a89a00\", \"carrot\": \"#ffae00\"}\n", " )\n", "\n", " section_btn = gr.Button(\"Identify Sections\")\n", " selected_section = gr.Textbox(label=\"Selected Section\")\n", "\n", " def section(img, num_boxes, num_segments):\n", " sections = []\n", " for a in range(num_boxes):\n", " x = random.randint(0, img.shape[1])\n", " y = random.randint(0, img.shape[0])\n", " w = random.randint(0, img.shape[1] - x)\n", " h = random.randint(0, img.shape[0] - y)\n", " sections.append(((x, y, x + w, y + h), section_labels[a]))\n", " for b in range(num_segments):\n", " x = random.randint(0, img.shape[1])\n", " y = random.randint(0, img.shape[0])\n", " r = random.randint(0, min(x, y, img.shape[1] - x, img.shape[0] - y))\n", " mask = np.zeros(img.shape[:2])\n", " for i in range(img.shape[0]):\n", " for j in range(img.shape[1]):\n", " dist_square = (i - y) ** 2 + (j - x) ** 2\n", " if dist_square < r**2:\n", " mask[i, j] = round((r**2 - dist_square) / r**2 * 4) / 4\n", " sections.append((mask, section_labels[b + num_boxes]))\n", " return (img, sections)\n", "\n", " section_btn.click(section, [img_input, num_boxes, num_segments], img_output)\n", "\n", " def select_section(evt: gr.SelectData):\n", " return section_labels[evt.index]\n", "\n", " img_output.select(select_section, None, selected_section)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/image_segmentation/run.py b/demos/image_segmentation/run.py new file mode 100644 index 0000000000000000000000000000000000000000..af3793f3217683102683eeb9a559bdff92a57066 --- /dev/null +++ b/demos/image_segmentation/run.py @@ -0,0 +1,61 @@ +import gradio as gr +import numpy as np +import random + +with gr.Blocks() as demo: + section_labels = [ + "apple", + "banana", + "carrot", + "donut", + "eggplant", + "fish", + "grapes", + "hamburger", + "ice cream", + "juice", + ] + + with gr.Row(): + num_boxes = gr.Slider(0, 5, 2, step=1, label="Number of boxes") + num_segments = gr.Slider(0, 5, 1, step=1, label="Number of segments") + + with gr.Row(): + img_input = gr.Image() + img_output = gr.AnnotatedImage( + color_map={"banana": "#a89a00", "carrot": "#ffae00"} + ) + + section_btn = gr.Button("Identify Sections") + selected_section = gr.Textbox(label="Selected Section") + + def section(img, num_boxes, num_segments): + sections = [] + for a in range(num_boxes): + x = random.randint(0, img.shape[1]) + y = random.randint(0, img.shape[0]) + w = random.randint(0, img.shape[1] - x) + h = random.randint(0, img.shape[0] - y) + sections.append(((x, y, x + w, y + h), section_labels[a])) + for b in range(num_segments): + x = random.randint(0, img.shape[1]) + y = random.randint(0, img.shape[0]) + r = random.randint(0, min(x, y, img.shape[1] - x, img.shape[0] - y)) + mask = np.zeros(img.shape[:2]) + for i in range(img.shape[0]): + for j in range(img.shape[1]): + dist_square = (i - y) ** 2 + (j - x) ** 2 + if dist_square < r**2: + mask[i, j] = round((r**2 - dist_square) / r**2 * 4) / 4 + sections.append((mask, section_labels[b + num_boxes])) + return (img, sections) + + section_btn.click(section, [img_input, num_boxes, num_segments], img_output) + + def select_section(evt: gr.SelectData): + return section_labels[evt.index] + + img_output.select(select_section, None, selected_section) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/interface_random_slider/run.ipynb b/demos/interface_random_slider/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..44f22825874467a3f299e472acdc70de7f9ecc30 --- /dev/null +++ b/demos/interface_random_slider/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: interface_random_slider"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def func(slider_1, slider_2, *args):\n", " return slider_1 + slider_2 * 5\n", "\n", "demo = gr.Interface(\n", " func,\n", " [\n", " gr.Slider(minimum=1.5, maximum=250000.89, randomize=True, label=\"Random Big Range\"),\n", " gr.Slider(minimum=-1, maximum=1, randomize=True, step=0.05, label=\"Random only multiple of 0.05 allowed\"),\n", " gr.Slider(minimum=0, maximum=1, randomize=True, step=0.25, label=\"Random only multiples of 0.25 allowed\"),\n", " gr.Slider(minimum=-100, maximum=100, randomize=True, step=3, label=\"Random between -100 and 100 step 3\"),\n", " gr.Slider(minimum=-100, maximum=100, randomize=True, label=\"Random between -100 and 100\"),\n", " gr.Slider(value=0.25, minimum=5, maximum=30, step=-1),\n", " ],\n", " \"number\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/interface_random_slider/run.py b/demos/interface_random_slider/run.py new file mode 100644 index 0000000000000000000000000000000000000000..daa797bc55f0d48ac0d5e413d95d90eb771adc39 --- /dev/null +++ b/demos/interface_random_slider/run.py @@ -0,0 +1,20 @@ +import gradio as gr + +def func(slider_1, slider_2, *args): + return slider_1 + slider_2 * 5 + +demo = gr.Interface( + func, + [ + gr.Slider(minimum=1.5, maximum=250000.89, randomize=True, label="Random Big Range"), + gr.Slider(minimum=-1, maximum=1, randomize=True, step=0.05, label="Random only multiple of 0.05 allowed"), + gr.Slider(minimum=0, maximum=1, randomize=True, step=0.25, label="Random only multiples of 0.25 allowed"), + gr.Slider(minimum=-100, maximum=100, randomize=True, step=3, label="Random between -100 and 100 step 3"), + gr.Slider(minimum=-100, maximum=100, randomize=True, label="Random between -100 and 100"), + gr.Slider(value=0.25, minimum=5, maximum=30, step=-1), + ], + "number", +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/kitchen_sink/files/cantina.wav b/demos/kitchen_sink/files/cantina.wav new file mode 100644 index 0000000000000000000000000000000000000000..41f020438468229763ec4a2321325e5916e09106 Binary files /dev/null and b/demos/kitchen_sink/files/cantina.wav differ diff --git a/demos/kitchen_sink/files/cheetah1.jpg b/demos/kitchen_sink/files/cheetah1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..c510ff30e09c1ce410afa499f0bfc3a63c751134 Binary files /dev/null and b/demos/kitchen_sink/files/cheetah1.jpg differ diff --git a/demos/kitchen_sink/files/lion.jpg b/demos/kitchen_sink/files/lion.jpg new file mode 100644 index 0000000000000000000000000000000000000000..e9bf9f5d0816d6201b4862088dc74476249a6a70 Binary files /dev/null and b/demos/kitchen_sink/files/lion.jpg differ diff --git a/demos/kitchen_sink/files/logo.png b/demos/kitchen_sink/files/logo.png new file mode 100644 index 0000000000000000000000000000000000000000..8f1df7156f0a690a2415903061e19c20e24adac4 Binary files /dev/null and b/demos/kitchen_sink/files/logo.png differ diff --git a/demos/kitchen_sink/files/time.csv b/demos/kitchen_sink/files/time.csv new file mode 100644 index 0000000000000000000000000000000000000000..ddb2035c4ed0377f54ee1602fff9f05ba5daa2db --- /dev/null +++ b/demos/kitchen_sink/files/time.csv @@ -0,0 +1,8 @@ +time,value,price +1,1,4 +2,3,8 +3,6,12 +4,10,16 +5,15,20 +6,21,24 +7,28,28 \ No newline at end of file diff --git a/demos/kitchen_sink/files/titanic.csv b/demos/kitchen_sink/files/titanic.csv new file mode 100644 index 0000000000000000000000000000000000000000..63b68ab0ba98c667f515c52f08c0bbd5573d5330 --- /dev/null +++ b/demos/kitchen_sink/files/titanic.csv @@ -0,0 +1,892 @@ +PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked +1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S +2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C +3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S +4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S +5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S +6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q +7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S +8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S +9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S +10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C +11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S +12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S +13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S +14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S +15,0,3,"Vestrom, Miss. 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Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S +890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C +891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q diff --git a/demos/kitchen_sink/files/tower.jpg b/demos/kitchen_sink/files/tower.jpg new file mode 100644 index 0000000000000000000000000000000000000000..aec3fa94eedf13f6e0c4ef56e4f669a3dba59fd8 Binary files /dev/null and b/demos/kitchen_sink/files/tower.jpg differ diff --git a/demos/kitchen_sink/files/world.mp4 b/demos/kitchen_sink/files/world.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..9bce44c33e275d6107240a1101032a7835fd8eed --- /dev/null +++ b/demos/kitchen_sink/files/world.mp4 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:71944d7430c461f0cd6e7fd10cee7eb72786352a3678fc7bc0ae3d410f72aece +size 1570024 diff --git a/demos/kitchen_sink/run.ipynb b/demos/kitchen_sink/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5632ee51b11c3a131fe39a90c376661ba38f5009 --- /dev/null +++ b/demos/kitchen_sink/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: kitchen_sink"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/cantina.wav https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/cantina.wav\n", "!wget -q -O files/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/cheetah1.jpg\n", "!wget -q -O files/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/lion.jpg\n", "!wget -q -O files/logo.png https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/logo.png\n", "!wget -q -O files/time.csv https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/time.csv\n", "!wget -q -O files/titanic.csv https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/titanic.csv\n", "!wget -q -O files/tower.jpg https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/tower.jpg\n", "!wget -q -O files/world.mp4 https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/world.mp4"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import os\n", "import json\n", "\n", "import numpy as np\n", "\n", "import gradio as gr\n", "\n", "CHOICES = [\"foo\", \"bar\", \"baz\"]\n", "JSONOBJ = \"\"\"{\"items\":{\"item\":[{\"id\": \"0001\",\"type\": null,\"is_good\": false,\"ppu\": 0.55,\"batters\":{\"batter\":[{ \"id\": \"1001\", \"type\": \"Regular\" },{ \"id\": \"1002\", \"type\": \"Chocolate\" },{ \"id\": \"1003\", \"type\": \"Blueberry\" },{ \"id\": \"1004\", \"type\": \"Devil's Food\" }]},\"topping\":[{ \"id\": \"5001\", \"type\": \"None\" },{ \"id\": \"5002\", \"type\": \"Glazed\" },{ \"id\": \"5005\", \"type\": \"Sugar\" },{ \"id\": \"5007\", \"type\": \"Powdered Sugar\" },{ \"id\": \"5006\", \"type\": \"Chocolate with Sprinkles\" },{ \"id\": \"5003\", \"type\": \"Chocolate\" },{ \"id\": \"5004\", \"type\": \"Maple\" }]}]}}\"\"\"\n", "\n", "def fn(\n", " text1,\n", " text2,\n", " num,\n", " slider1,\n", " slider2,\n", " single_checkbox,\n", " checkboxes,\n", " radio,\n", " dropdown,\n", " multi_dropdown,\n", " im1,\n", " # im2,\n", " # im3,\n", " im4,\n", " video,\n", " audio1,\n", " audio2,\n", " file,\n", " df1,\n", "):\n", " return (\n", " (text1 if single_checkbox else text2)\n", " + \", selected:\"\n", " + \", \".join(checkboxes), # Text\n", " {\n", " \"positive\": num / (num + slider1 + slider2),\n", " \"negative\": slider1 / (num + slider1 + slider2),\n", " \"neutral\": slider2 / (num + slider1 + slider2),\n", " }, # Label\n", " (audio1[0], np.flipud(audio1[1]))\n", " if audio1 is not None\n", " else os.path.join(os.path.abspath(''), \"files/cantina.wav\"), # Audio\n", " np.flipud(im1)\n", " if im1 is not None\n", " else os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"), # Image\n", " video\n", " if video is not None\n", " else os.path.join(os.path.abspath(''), \"files/world.mp4\"), # Video\n", " [\n", " (\"The\", \"art\"),\n", " (\"quick brown\", \"adj\"),\n", " (\"fox\", \"nn\"),\n", " (\"jumped\", \"vrb\"),\n", " (\"testing testing testing\", None),\n", " (\"over\", \"prp\"),\n", " (\"the\", \"art\"),\n", " (\"testing\", None),\n", " (\"lazy\", \"adj\"),\n", " (\"dogs\", \"nn\"),\n", " (\".\", \"punc\"),\n", " ]\n", " + [(f\"test {x}\", f\"test {x}\") for x in range(10)], # HighlightedText\n", " # [(\"The testing testing testing\", None), (\"quick brown\", 0.2), (\"fox\", 1), (\"jumped\", -1), (\"testing testing testing\", 0), (\"over\", 0), (\"the\", 0), (\"testing\", 0), (\"lazy\", 1), (\"dogs\", 0), (\".\", 1)] + [(f\"test {x}\", x/10) for x in range(-10, 10)], # HighlightedText\n", " [\n", " (\"The testing testing testing\", None),\n", " (\"over\", 0.6),\n", " (\"the\", 0.2),\n", " (\"testing\", None),\n", " (\"lazy\", -0.1),\n", " (\"dogs\", 0.4),\n", " (\".\", 0),\n", " ]\n", " + [(\"test\", x / 10) for x in range(-10, 10)], # HighlightedText\n", " json.loads(JSONOBJ), # JSON\n", " \"\", # HTML\n", " os.path.join(os.path.abspath(''), \"files/titanic.csv\"),\n", " df1, # Dataframe\n", " np.random.randint(0, 10, (4, 4)), # Dataframe\n", " )\n", "\n", "demo = gr.Interface(\n", " fn,\n", " inputs=[\n", " gr.Textbox(value=\"Lorem ipsum\", label=\"Textbox\"),\n", " gr.Textbox(lines=3, placeholder=\"Type here..\", label=\"Textbox 2\"),\n", " gr.Number(label=\"Number\", value=42),\n", " gr.Slider(10, 20, value=15, label=\"Slider: 10 - 20\"),\n", " gr.Slider(maximum=20, step=0.04, label=\"Slider: step @ 0.04\"),\n", " gr.Checkbox(label=\"Checkbox\"),\n", " gr.CheckboxGroup(label=\"CheckboxGroup\", choices=CHOICES, value=CHOICES[0:2]),\n", " gr.Radio(label=\"Radio\", choices=CHOICES, value=CHOICES[2]),\n", " gr.Dropdown(label=\"Dropdown\", choices=CHOICES),\n", " gr.Dropdown(\n", " label=\"Multiselect Dropdown (Max choice: 2)\",\n", " choices=CHOICES,\n", " multiselect=True,\n", " max_choices=2,\n", " ),\n", " gr.Image(label=\"Image\"),\n", " # gr.Image(label=\"Image w/ Cropper\", tool=\"select\"),\n", " # gr.Image(label=\"Sketchpad\", source=\"canvas\"),\n", " gr.Image(label=\"Webcam\", sources=[\"webcam\"]),\n", " gr.Video(label=\"Video\"),\n", " gr.Audio(label=\"Audio\"),\n", " gr.Audio(label=\"Microphone\", sources=[\"microphone\"]),\n", " gr.File(label=\"File\"),\n", " gr.Dataframe(label=\"Dataframe\", headers=[\"Name\", \"Age\", \"Gender\"]),\n", " ],\n", " outputs=[\n", " gr.Textbox(label=\"Textbox\"),\n", " gr.Label(label=\"Label\"),\n", " gr.Audio(label=\"Audio\"),\n", " gr.Image(label=\"Image\", elem_id=\"output-img\"),\n", " gr.Video(label=\"Video\"),\n", " gr.HighlightedText(\n", " label=\"HighlightedText\", color_map={\"punc\": \"pink\", \"test 0\": \"blue\"}\n", " ),\n", " gr.HighlightedText(label=\"HighlightedText\", show_legend=True),\n", " gr.JSON(label=\"JSON\"),\n", " gr.HTML(label=\"HTML\"),\n", " gr.File(label=\"File\"),\n", " gr.Dataframe(label=\"Dataframe\"),\n", " gr.Dataframe(label=\"Numpy\"),\n", " ],\n", " examples=[\n", " [\n", " \"the quick brown fox\",\n", " \"jumps over the lazy dog\",\n", " 10,\n", " 12,\n", " 4,\n", " True,\n", " [\"foo\", \"baz\"],\n", " \"baz\",\n", " \"bar\",\n", " [\"foo\", \"bar\"],\n", " os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", " # os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", " # os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", " os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", " os.path.join(os.path.abspath(''), \"files/world.mp4\"),\n", " os.path.join(os.path.abspath(''), \"files/cantina.wav\"),\n", " os.path.join(os.path.abspath(''), \"files/cantina.wav\"),\n", " os.path.join(os.path.abspath(''), \"files/titanic.csv\"),\n", " [[1, 2, 3, 4], [4, 5, 6, 7], [8, 9, 1, 2], [3, 4, 5, 6]],\n", " ]\n", " ]\n", " * 3,\n", " title=\"Kitchen Sink\",\n", " description=\"Try out all the components!\",\n", " article=\"Learn more about [Gradio](http://gradio.app)\",\n", " cache_examples=True,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/kitchen_sink/run.py b/demos/kitchen_sink/run.py new file mode 100644 index 0000000000000000000000000000000000000000..2ea4833c1436b7d2548eee6248e8509fd333907f --- /dev/null +++ b/demos/kitchen_sink/run.py @@ -0,0 +1,159 @@ +import os +import json + +import numpy as np + +import gradio as gr + +CHOICES = ["foo", "bar", "baz"] +JSONOBJ = """{"items":{"item":[{"id": "0001","type": null,"is_good": false,"ppu": 0.55,"batters":{"batter":[{ "id": "1001", "type": "Regular" },{ "id": "1002", "type": "Chocolate" },{ "id": "1003", "type": "Blueberry" },{ "id": "1004", "type": "Devil's Food" }]},"topping":[{ "id": "5001", "type": "None" },{ "id": "5002", "type": "Glazed" },{ "id": "5005", "type": "Sugar" },{ "id": "5007", "type": "Powdered Sugar" },{ "id": "5006", "type": "Chocolate with Sprinkles" },{ "id": "5003", "type": "Chocolate" },{ "id": "5004", "type": "Maple" }]}]}}""" + +def fn( + text1, + text2, + num, + slider1, + slider2, + single_checkbox, + checkboxes, + radio, + dropdown, + multi_dropdown, + im1, + # im2, + # im3, + im4, + video, + audio1, + audio2, + file, + df1, +): + return ( + (text1 if single_checkbox else text2) + + ", selected:" + + ", ".join(checkboxes), # Text + { + "positive": num / (num + slider1 + slider2), + "negative": slider1 / (num + slider1 + slider2), + "neutral": slider2 / (num + slider1 + slider2), + }, # Label + (audio1[0], np.flipud(audio1[1])) + if audio1 is not None + else os.path.join(os.path.dirname(__file__), "files/cantina.wav"), # Audio + np.flipud(im1) + if im1 is not None + else os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"), # Image + video + if video is not None + else os.path.join(os.path.dirname(__file__), "files/world.mp4"), # Video + [ + ("The", "art"), + ("quick brown", "adj"), + ("fox", "nn"), + ("jumped", "vrb"), + ("testing testing testing", None), + ("over", "prp"), + ("the", "art"), + ("testing", None), + ("lazy", "adj"), + ("dogs", "nn"), + (".", "punc"), + ] + + [(f"test {x}", f"test {x}") for x in range(10)], # HighlightedText + # [("The testing testing testing", None), ("quick brown", 0.2), ("fox", 1), ("jumped", -1), ("testing testing testing", 0), ("over", 0), ("the", 0), ("testing", 0), ("lazy", 1), ("dogs", 0), (".", 1)] + [(f"test {x}", x/10) for x in range(-10, 10)], # HighlightedText + [ + ("The testing testing testing", None), + ("over", 0.6), + ("the", 0.2), + ("testing", None), + ("lazy", -0.1), + ("dogs", 0.4), + (".", 0), + ] + + [("test", x / 10) for x in range(-10, 10)], # HighlightedText + json.loads(JSONOBJ), # JSON + "", # HTML + os.path.join(os.path.dirname(__file__), "files/titanic.csv"), + df1, # Dataframe + np.random.randint(0, 10, (4, 4)), # Dataframe + ) + +demo = gr.Interface( + fn, + inputs=[ + gr.Textbox(value="Lorem ipsum", label="Textbox"), + gr.Textbox(lines=3, placeholder="Type here..", label="Textbox 2"), + gr.Number(label="Number", value=42), + gr.Slider(10, 20, value=15, label="Slider: 10 - 20"), + gr.Slider(maximum=20, step=0.04, label="Slider: step @ 0.04"), + gr.Checkbox(label="Checkbox"), + gr.CheckboxGroup(label="CheckboxGroup", choices=CHOICES, value=CHOICES[0:2]), + gr.Radio(label="Radio", choices=CHOICES, value=CHOICES[2]), + gr.Dropdown(label="Dropdown", choices=CHOICES), + gr.Dropdown( + label="Multiselect Dropdown (Max choice: 2)", + choices=CHOICES, + multiselect=True, + max_choices=2, + ), + gr.Image(label="Image"), + # gr.Image(label="Image w/ Cropper", tool="select"), + # gr.Image(label="Sketchpad", source="canvas"), + gr.Image(label="Webcam", sources=["webcam"]), + gr.Video(label="Video"), + gr.Audio(label="Audio"), + gr.Audio(label="Microphone", sources=["microphone"]), + gr.File(label="File"), + gr.Dataframe(label="Dataframe", headers=["Name", "Age", "Gender"]), + ], + outputs=[ + gr.Textbox(label="Textbox"), + gr.Label(label="Label"), + gr.Audio(label="Audio"), + gr.Image(label="Image", elem_id="output-img"), + gr.Video(label="Video"), + gr.HighlightedText( + label="HighlightedText", color_map={"punc": "pink", "test 0": "blue"} + ), + gr.HighlightedText(label="HighlightedText", show_legend=True), + gr.JSON(label="JSON"), + gr.HTML(label="HTML"), + gr.File(label="File"), + gr.Dataframe(label="Dataframe"), + gr.Dataframe(label="Numpy"), + ], + examples=[ + [ + "the quick brown fox", + "jumps over the lazy dog", + 10, + 12, + 4, + True, + ["foo", "baz"], + "baz", + "bar", + ["foo", "bar"], + os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"), + # os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"), + # os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"), + os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"), + os.path.join(os.path.dirname(__file__), "files/world.mp4"), + os.path.join(os.path.dirname(__file__), "files/cantina.wav"), + os.path.join(os.path.dirname(__file__), "files/cantina.wav"), + os.path.join(os.path.dirname(__file__), "files/titanic.csv"), + [[1, 2, 3, 4], [4, 5, 6, 7], [8, 9, 1, 2], [3, 4, 5, 6]], + ] + ] + * 3, + title="Kitchen Sink", + description="Try out all the components!", + article="Learn more about [Gradio](http://gradio.app)", + cache_examples=True, +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/kitchen_sink_random/__init__.py b/demos/kitchen_sink_random/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demos/kitchen_sink_random/constants.py b/demos/kitchen_sink_random/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..8a990fbe669e2802e6d668bd620f545a2bb45734 --- /dev/null +++ b/demos/kitchen_sink_random/constants.py @@ -0,0 +1,65 @@ +import numpy as np +import matplotlib.pyplot as plt +import random +import os + +def random_plot(): + start_year = 2020 + x = np.arange(start_year, start_year + random.randint(0, 10)) + year_count = x.shape[0] + plt_format = "-" + fig = plt.figure() + ax = fig.add_subplot(111) + series = np.arange(0, year_count, dtype=float) + series = series**2 + series += np.random.rand(year_count) + ax.plot(x, series, plt_format) + return fig + +img_dir = os.path.join(os.path.dirname(__file__), "files") +file_dir = os.path.join(os.path.dirname(__file__), "..", "kitchen_sink", "files") +model3d_dir = os.path.join(os.path.dirname(__file__), "..", "model3D", "files") +highlighted_text_output_1 = [ + { + "entity": "I-LOC", + "score": 0.9988978, + "index": 2, + "word": "Chicago", + "start": 5, + "end": 12, + }, + { + "entity": "I-MISC", + "score": 0.9958592, + "index": 5, + "word": "Pakistani", + "start": 22, + "end": 31, + }, +] +highlighted_text_output_2 = [ + { + "entity": "I-LOC", + "score": 0.9988978, + "index": 2, + "word": "Chicago", + "start": 5, + "end": 12, + }, + { + "entity": "I-LOC", + "score": 0.9958592, + "index": 5, + "word": "Pakistan", + "start": 22, + "end": 30, + }, +] + +highlighted_text = "Does Chicago have any Pakistani restaurants" + +def random_model3d(): + model_3d = random.choice( + [os.path.join(model3d_dir, model) for model in os.listdir(model3d_dir) if model != "source.txt"] + ) + return model_3d diff --git a/demos/kitchen_sink_random/files/cheetah1.jpeg b/demos/kitchen_sink_random/files/cheetah1.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..c510ff30e09c1ce410afa499f0bfc3a63c751134 Binary files /dev/null and b/demos/kitchen_sink_random/files/cheetah1.jpeg differ diff --git a/demos/kitchen_sink_random/files/cheetah1.jpg b/demos/kitchen_sink_random/files/cheetah1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..c510ff30e09c1ce410afa499f0bfc3a63c751134 Binary files /dev/null and b/demos/kitchen_sink_random/files/cheetah1.jpg differ diff --git a/demos/kitchen_sink_random/files/lion.jpg b/demos/kitchen_sink_random/files/lion.jpg new file mode 100644 index 0000000000000000000000000000000000000000..e9bf9f5d0816d6201b4862088dc74476249a6a70 Binary files /dev/null and b/demos/kitchen_sink_random/files/lion.jpg differ diff --git a/demos/kitchen_sink_random/requirements.txt b/demos/kitchen_sink_random/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..4b43f7e68658e87f033212c358b00c4d36075458 --- /dev/null +++ b/demos/kitchen_sink_random/requirements.txt @@ -0,0 +1 @@ +matplotlib \ No newline at end of file diff --git a/demos/kitchen_sink_random/run.py b/demos/kitchen_sink_random/run.py new file mode 100644 index 0000000000000000000000000000000000000000..c2da3656047b78bb7ce91f85d5ece0654372c624 --- /dev/null +++ b/demos/kitchen_sink_random/run.py @@ -0,0 +1,97 @@ +import gradio as gr +from datetime import datetime +import random +import string +import os +import pandas as pd + +from constants import ( + file_dir, + img_dir, + highlighted_text, + highlighted_text_output_2, + highlighted_text_output_1, + random_plot, + random_model3d, +) + +demo = gr.Interface( + lambda *args: args[0], + inputs=[ + gr.Textbox(value=lambda: datetime.now(), label="Current Time"), + gr.Number(value=lambda: random.random(), label="Ranom Percentage"), + gr.Slider(minimum=-1, maximum=1, randomize=True, label="Slider with randomize"), + gr.Slider( + minimum=0, + maximum=1, + value=lambda: random.random(), + label="Slider with value func", + ), + gr.Checkbox(value=lambda: random.random() > 0.5, label="Random Checkbox"), + gr.CheckboxGroup( + choices=["a", "b", "c", "d"], + value=lambda: random.choice(["a", "b", "c", "d"]), + label="Random CheckboxGroup", + ), + gr.Radio( + choices=list(string.ascii_lowercase), + value=lambda: random.choice(string.ascii_lowercase), + ), + gr.Dropdown( + choices=["a", "b", "c", "d", "e"], + value=lambda: random.choice(["a", "b", "c"]), + ), + gr.Image( + value=lambda: random.choice( + [os.path.join(img_dir, img) for img in os.listdir(img_dir)] + ) + ), + gr.Video(value=lambda: os.path.join(file_dir, "world.mp4")), + gr.Audio(value=lambda: os.path.join(file_dir, "cantina.wav")), + gr.File( + value=lambda: random.choice( + [os.path.join(file_dir, img) for img in os.listdir(file_dir)] + ) + ), + gr.Dataframe( + value=lambda: pd.DataFrame( + {"random_number_rows": range(random.randint(0, 10))} + ) + ), + gr.State(value=lambda: random.choice(string.ascii_lowercase)), + gr.ColorPicker(value=lambda: random.choice(["#000000", "#ff0000", "#0000FF"])), + gr.Label(value=lambda: random.choice(["Pedestrian", "Car", "Cyclist"])), + gr.HighlightedText( + value=lambda: random.choice( + [ + {"text": highlighted_text, "entities": highlighted_text_output_1}, + {"text": highlighted_text, "entities": highlighted_text_output_2}, + ] + ), + ), + gr.JSON(value=lambda: random.choice([{"a": 1}, {"b": 2}])), + gr.HTML( + value=lambda: random.choice( + [ + '

I am red

', + '

I am blue

', + ] + ) + ), + gr.Gallery( + value=lambda: [os.path.join(img_dir, img) for img in os.listdir(img_dir)] + ), + gr.Chatbot( + value=lambda: random.choice([[("hello", "hi!")], [("bye", "goodbye!")]]) + ), + gr.Model3D(value=random_model3d), + gr.Plot(value=random_plot), + gr.Markdown(value=lambda: f"### {random.choice(['Hello', 'Hi', 'Goodbye!'])}"), + ], + outputs=[ + gr.State(value=lambda: random.choice(string.ascii_lowercase)) + ], +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/matrix_transpose/run.ipynb b/demos/matrix_transpose/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5df77119c9241222cec8c868f9ef0636906f3d3e --- /dev/null +++ b/demos/matrix_transpose/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: matrix_transpose"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import numpy as np\n", "\n", "import gradio as gr\n", "\n", "def transpose(matrix):\n", " return matrix.T\n", "\n", "demo = gr.Interface(\n", " transpose,\n", " gr.Dataframe(type=\"numpy\", datatype=\"number\", row_count=5, col_count=3),\n", " \"numpy\",\n", " examples=[\n", " [np.zeros((3, 3)).tolist()],\n", " [np.ones((2, 2)).tolist()],\n", " [np.random.randint(0, 10, (3, 10)).tolist()],\n", " [np.random.randint(0, 10, (10, 3)).tolist()],\n", " [np.random.randint(0, 10, (10, 10)).tolist()],\n", " ],\n", " cache_examples=False\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/matrix_transpose/run.py b/demos/matrix_transpose/run.py new file mode 100644 index 0000000000000000000000000000000000000000..b9918373553b1cfc44edf691cb29e9cc30918521 --- /dev/null +++ b/demos/matrix_transpose/run.py @@ -0,0 +1,23 @@ +import numpy as np + +import gradio as gr + +def transpose(matrix): + return matrix.T + +demo = gr.Interface( + transpose, + gr.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3), + "numpy", + examples=[ + [np.zeros((3, 3)).tolist()], + [np.ones((2, 2)).tolist()], + [np.random.randint(0, 10, (3, 10)).tolist()], + [np.random.randint(0, 10, (10, 3)).tolist()], + [np.random.randint(0, 10, (10, 10)).tolist()], + ], + cache_examples=False +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/matrix_transpose/screenshot.png b/demos/matrix_transpose/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..a9434e9c1df2828ecc1df700eeaaad9609109f82 Binary files /dev/null and b/demos/matrix_transpose/screenshot.png differ diff --git a/demos/mini_leaderboard/assets/__init__.py b/demos/mini_leaderboard/assets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demos/mini_leaderboard/assets/custom_css.css b/demos/mini_leaderboard/assets/custom_css.css new file mode 100644 index 0000000000000000000000000000000000000000..91a5ff7d986e4b2026bed2175bdf99f847ed05a0 --- /dev/null +++ b/demos/mini_leaderboard/assets/custom_css.css @@ -0,0 +1,87 @@ +/* Hides the final AutoEvalColumn */ +#llm-benchmark-tab-table table td:last-child, +#llm-benchmark-tab-table table th:last-child { + display: none; +} + +/* Limit the width of the first AutoEvalColumn so that names don't expand too much */ +table td:first-child, +table th:first-child { + max-width: 400px; + overflow: auto; + white-space: nowrap; +} + +/* Full width space */ +.gradio-container { + max-width: 95%!important; +} + +/* Text style and margins */ +.markdown-text { + font-size: 16px !important; +} + +#models-to-add-text { + font-size: 18px !important; +} + +#citation-button span { + font-size: 16px !important; +} + +#citation-button textarea { + font-size: 16px !important; +} + +#citation-button > label > button { + margin: 6px; + transform: scale(1.3); +} + +#search-bar-table-box > div:first-child { + background: none; + border: none; +} + +#search-bar { + padding: 0px; +} + +.tab-buttons button { + font-size: 20px; +} + +/* Filters style */ +#filter_type{ + border: 0; + padding-left: 0; + padding-top: 0; +} +#filter_type label { + display: flex; +} +#filter_type label > span{ + margin-top: var(--spacing-lg); + margin-right: 0.5em; +} +#filter_type label > .wrap{ + width: 103px; +} +#filter_type label > .wrap .wrap-inner{ + padding: 2px; +} +#filter_type label > .wrap .wrap-inner input{ + width: 1px +} +#filter-columns-type{ + border:0; + padding:0.5; +} +#filter-columns-size{ + border:0; + padding:0.5; +} +#box-filter > .form{ + border: 0 +} \ No newline at end of file diff --git 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\ No newline at end of file diff --git a/demos/mini_leaderboard/run.ipynb b/demos/mini_leaderboard/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..509ce3966ac94c07c31071bfe21e4d729d5685a4 --- /dev/null +++ b/demos/mini_leaderboard/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: mini_leaderboard"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('assets')\n", "!wget -q -O assets/__init__.py https://github.com/gradio-app/gradio/raw/main/demo/mini_leaderboard/assets/__init__.py\n", "!wget -q -O assets/custom_css.css https://github.com/gradio-app/gradio/raw/main/demo/mini_leaderboard/assets/custom_css.css\n", "!wget -q -O assets/leaderboard_data.json https://github.com/gradio-app/gradio/raw/main/demo/mini_leaderboard/assets/leaderboard_data.json"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pandas as pd\n", "from pathlib import Path\n", "\n", "abs_path = Path(__file__).parent.absolute()\n", "\n", "df = pd.read_json(str(abs_path / \"assets/leaderboard_data.json\"))\n", "invisible_df = df.copy()\n", "\n", "COLS = [\n", " \"T\",\n", " \"Model\",\n", " \"Average \u2b06\ufe0f\",\n", " \"ARC\",\n", " \"HellaSwag\",\n", " \"MMLU\",\n", " \"TruthfulQA\",\n", " \"Winogrande\",\n", " \"GSM8K\",\n", " \"Type\",\n", " \"Architecture\",\n", " \"Precision\",\n", " \"Merged\",\n", " \"Hub License\",\n", " \"#Params (B)\",\n", " \"Hub \u2764\ufe0f\",\n", " \"Model sha\",\n", " \"model_name_for_query\",\n", "]\n", "ON_LOAD_COLS = [\n", " \"T\",\n", " \"Model\",\n", " \"Average \u2b06\ufe0f\",\n", " \"ARC\",\n", " \"HellaSwag\",\n", " \"MMLU\",\n", " \"TruthfulQA\",\n", " \"Winogrande\",\n", " \"GSM8K\",\n", " \"model_name_for_query\",\n", "]\n", "TYPES = [\n", " \"str\",\n", " \"markdown\",\n", " \"number\",\n", " \"number\",\n", " \"number\",\n", " \"number\",\n", " \"number\",\n", " \"number\",\n", " \"number\",\n", " \"str\",\n", " \"str\",\n", " \"str\",\n", " \"str\",\n", " \"bool\",\n", " \"str\",\n", " \"number\",\n", " \"number\",\n", " \"bool\",\n", " \"str\",\n", " \"bool\",\n", " \"bool\",\n", " \"str\",\n", "]\n", "NUMERIC_INTERVALS = {\n", " \"?\": pd.Interval(-1, 0, closed=\"right\"),\n", " \"~1.5\": pd.Interval(0, 2, closed=\"right\"),\n", " \"~3\": pd.Interval(2, 4, closed=\"right\"),\n", " \"~7\": pd.Interval(4, 9, closed=\"right\"),\n", " \"~13\": pd.Interval(9, 20, closed=\"right\"),\n", " \"~35\": pd.Interval(20, 45, closed=\"right\"),\n", " \"~60\": pd.Interval(45, 70, closed=\"right\"),\n", " \"70+\": pd.Interval(70, 10000, closed=\"right\"),\n", "}\n", "MODEL_TYPE = [str(s) for s in df[\"T\"].unique()]\n", "Precision = [str(s) for s in df[\"Precision\"].unique()]\n", "\n", "# Searching and filtering\n", "def update_table(\n", " hidden_df: pd.DataFrame,\n", " columns: list,\n", " type_query: list,\n", " precision_query: str,\n", " size_query: list,\n", " query: str,\n", "):\n", " filtered_df = filter_models(hidden_df, type_query, size_query, precision_query) # type: ignore\n", " filtered_df = filter_queries(query, filtered_df)\n", " df = select_columns(filtered_df, columns)\n", " return df\n", "\n", "def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:\n", " return df[(df[\"model_name_for_query\"].str.contains(query, case=False))] # type: ignore\n", "\n", "def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:\n", " # We use COLS to maintain sorting\n", " filtered_df = df[[c for c in COLS if c in df.columns and c in columns]]\n", " return filtered_df # type: ignore\n", "\n", "def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:\n", " final_df = []\n", " if query != \"\":\n", " queries = [q.strip() for q in query.split(\";\")]\n", " for _q in queries:\n", " _q = _q.strip()\n", " if _q != \"\":\n", " temp_filtered_df = search_table(filtered_df, _q)\n", " if len(temp_filtered_df) > 0:\n", " final_df.append(temp_filtered_df)\n", " if len(final_df) > 0:\n", " filtered_df = pd.concat(final_df)\n", " filtered_df = filtered_df.drop_duplicates( # type: ignore\n", " subset=[\"Model\", \"Precision\", \"Model sha\"]\n", " )\n", "\n", " return filtered_df\n", "\n", "def filter_models(\n", " df: pd.DataFrame,\n", " type_query: list,\n", " size_query: list,\n", " precision_query: list,\n", ") -> pd.DataFrame:\n", " # Show all models\n", " filtered_df = df\n", "\n", " type_emoji = [t[0] for t in type_query]\n", " filtered_df = filtered_df.loc[df[\"T\"].isin(type_emoji)]\n", " filtered_df = filtered_df.loc[df[\"Precision\"].isin(precision_query + [\"None\"])]\n", "\n", " numeric_interval = pd.IntervalIndex(\n", " sorted([NUMERIC_INTERVALS[s] for s in size_query]) # type: ignore\n", " )\n", " params_column = pd.to_numeric(df[\"#Params (B)\"], errors=\"coerce\")\n", " mask = params_column.apply(lambda x: any(numeric_interval.contains(x))) # type: ignore\n", " filtered_df = filtered_df.loc[mask]\n", "\n", " return filtered_df\n", "\n", "demo = gr.Blocks(css=str(abs_path / \"assets/leaderboard_data.json\"))\n", "with demo:\n", " gr.Markdown(\"\"\"Test Space of the LLM Leaderboard\"\"\", elem_classes=\"markdown-text\")\n", "\n", " with gr.Tabs(elem_classes=\"tab-buttons\") as tabs:\n", " with gr.TabItem(\"\ud83c\udfc5 LLM Benchmark\", elem_id=\"llm-benchmark-tab-table\", id=0):\n", " with gr.Row():\n", " with gr.Column():\n", " with gr.Row():\n", " search_bar = gr.Textbox(\n", " placeholder=\" \ud83d\udd0d Search for your model (separate multiple queries with `;`) and press ENTER...\",\n", " show_label=False,\n", " elem_id=\"search-bar\",\n", " )\n", " with gr.Row():\n", " shown_columns = gr.CheckboxGroup(\n", " choices=COLS,\n", " value=ON_LOAD_COLS,\n", " label=\"Select columns to show\",\n", " elem_id=\"column-select\",\n", " interactive=True,\n", " )\n", " with gr.Column(min_width=320):\n", " filter_columns_type = gr.CheckboxGroup(\n", " label=\"Model types\",\n", " choices=MODEL_TYPE,\n", " value=MODEL_TYPE,\n", " interactive=True,\n", " elem_id=\"filter-columns-type\",\n", " )\n", " filter_columns_precision = gr.CheckboxGroup(\n", " label=\"Precision\",\n", " choices=Precision,\n", " value=Precision,\n", " interactive=True,\n", " elem_id=\"filter-columns-precision\",\n", " )\n", " filter_columns_size = gr.CheckboxGroup(\n", " label=\"Model sizes (in billions of parameters)\",\n", " choices=list(NUMERIC_INTERVALS.keys()),\n", " value=list(NUMERIC_INTERVALS.keys()),\n", " interactive=True,\n", " elem_id=\"filter-columns-size\",\n", " )\n", "\n", " leaderboard_table = gr.components.Dataframe(\n", " value=df[ON_LOAD_COLS], # type: ignore\n", " headers=ON_LOAD_COLS,\n", " datatype=TYPES,\n", " elem_id=\"leaderboard-table\",\n", " interactive=False,\n", " visible=True,\n", " column_widths=[\"2%\", \"33%\"],\n", " )\n", "\n", " # Dummy leaderboard for handling the case when the user uses backspace key\n", " hidden_leaderboard_table_for_search = gr.components.Dataframe(\n", " value=invisible_df[COLS], # type: ignore\n", " headers=COLS,\n", " datatype=TYPES,\n", " visible=False,\n", " )\n", " search_bar.submit(\n", " update_table,\n", " [\n", " hidden_leaderboard_table_for_search,\n", " shown_columns,\n", " filter_columns_type,\n", " filter_columns_precision,\n", " filter_columns_size,\n", " search_bar,\n", " ],\n", " leaderboard_table,\n", " )\n", " for selector in [\n", " shown_columns,\n", " filter_columns_type,\n", " filter_columns_precision,\n", " filter_columns_size,\n", " ]:\n", " selector.change(\n", " update_table,\n", " [\n", " hidden_leaderboard_table_for_search,\n", " shown_columns,\n", " filter_columns_type,\n", " filter_columns_precision,\n", " filter_columns_size,\n", " search_bar,\n", " ],\n", " leaderboard_table,\n", " queue=True,\n", " )\n", "\n", "if __name__ == \"__main__\":\n", " demo.queue(default_concurrency_limit=40).launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/mini_leaderboard/run.py b/demos/mini_leaderboard/run.py new file mode 100644 index 0000000000000000000000000000000000000000..6eef4c3bc6a911152efb650b3539e9531164444a --- /dev/null +++ b/demos/mini_leaderboard/run.py @@ -0,0 +1,236 @@ +import gradio as gr +import pandas as pd +from pathlib import Path + +abs_path = Path(__file__).parent.absolute() + +df = pd.read_json(str(abs_path / "assets/leaderboard_data.json")) +invisible_df = df.copy() + +COLS = [ + "T", + "Model", + "Average ⬆️", + "ARC", + "HellaSwag", + "MMLU", + "TruthfulQA", + "Winogrande", + "GSM8K", + "Type", + "Architecture", + "Precision", + "Merged", + "Hub License", + "#Params (B)", + "Hub ❤️", + "Model sha", + "model_name_for_query", +] +ON_LOAD_COLS = [ + "T", + "Model", + "Average ⬆️", + "ARC", + "HellaSwag", + "MMLU", + "TruthfulQA", + "Winogrande", + "GSM8K", + "model_name_for_query", +] +TYPES = [ + "str", + "markdown", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "str", + "str", + "str", + "str", + "bool", + "str", + "number", + "number", + "bool", + "str", + "bool", + "bool", + "str", +] +NUMERIC_INTERVALS = { + "?": pd.Interval(-1, 0, closed="right"), + "~1.5": pd.Interval(0, 2, closed="right"), + "~3": pd.Interval(2, 4, closed="right"), + "~7": pd.Interval(4, 9, closed="right"), + "~13": pd.Interval(9, 20, closed="right"), + "~35": pd.Interval(20, 45, closed="right"), + "~60": pd.Interval(45, 70, closed="right"), + "70+": pd.Interval(70, 10000, closed="right"), +} +MODEL_TYPE = [str(s) for s in df["T"].unique()] +Precision = [str(s) for s in df["Precision"].unique()] + +# Searching and filtering +def update_table( + hidden_df: pd.DataFrame, + columns: list, + type_query: list, + precision_query: str, + size_query: list, + query: str, +): + filtered_df = filter_models(hidden_df, type_query, size_query, precision_query) # type: ignore + filtered_df = filter_queries(query, filtered_df) + df = select_columns(filtered_df, columns) + return df + +def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame: + return df[(df["model_name_for_query"].str.contains(query, case=False))] # type: ignore + +def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame: + # We use COLS to maintain sorting + filtered_df = df[[c for c in COLS if c in df.columns and c in columns]] + return filtered_df # type: ignore + +def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame: + final_df = [] + if query != "": + queries = [q.strip() for q in query.split(";")] + for _q in queries: + _q = _q.strip() + if _q != "": + temp_filtered_df = search_table(filtered_df, _q) + if len(temp_filtered_df) > 0: + final_df.append(temp_filtered_df) + if len(final_df) > 0: + filtered_df = pd.concat(final_df) + filtered_df = filtered_df.drop_duplicates( # type: ignore + subset=["Model", "Precision", "Model sha"] + ) + + return filtered_df + +def filter_models( + df: pd.DataFrame, + type_query: list, + size_query: list, + precision_query: list, +) -> pd.DataFrame: + # Show all models + filtered_df = df + + type_emoji = [t[0] for t in type_query] + filtered_df = filtered_df.loc[df["T"].isin(type_emoji)] + filtered_df = filtered_df.loc[df["Precision"].isin(precision_query + ["None"])] + + numeric_interval = pd.IntervalIndex( + sorted([NUMERIC_INTERVALS[s] for s in size_query]) # type: ignore + ) + params_column = pd.to_numeric(df["#Params (B)"], errors="coerce") + mask = params_column.apply(lambda x: any(numeric_interval.contains(x))) # type: ignore + filtered_df = filtered_df.loc[mask] + + return filtered_df + +demo = gr.Blocks(css=str(abs_path / "assets/leaderboard_data.json")) +with demo: + gr.Markdown("""Test Space of the LLM Leaderboard""", elem_classes="markdown-text") + + with gr.Tabs(elem_classes="tab-buttons") as tabs: + with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0): + with gr.Row(): + with gr.Column(): + with gr.Row(): + search_bar = gr.Textbox( + placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...", + show_label=False, + elem_id="search-bar", + ) + with gr.Row(): + shown_columns = gr.CheckboxGroup( + choices=COLS, + value=ON_LOAD_COLS, + label="Select columns to show", + elem_id="column-select", + interactive=True, + ) + with gr.Column(min_width=320): + filter_columns_type = gr.CheckboxGroup( + label="Model types", + choices=MODEL_TYPE, + value=MODEL_TYPE, + interactive=True, + elem_id="filter-columns-type", + ) + filter_columns_precision = gr.CheckboxGroup( + label="Precision", + choices=Precision, + value=Precision, + interactive=True, + elem_id="filter-columns-precision", + ) + filter_columns_size = gr.CheckboxGroup( + label="Model sizes (in billions of parameters)", + choices=list(NUMERIC_INTERVALS.keys()), + value=list(NUMERIC_INTERVALS.keys()), + interactive=True, + elem_id="filter-columns-size", + ) + + leaderboard_table = gr.components.Dataframe( + value=df[ON_LOAD_COLS], # type: ignore + headers=ON_LOAD_COLS, + datatype=TYPES, + elem_id="leaderboard-table", + interactive=False, + visible=True, + column_widths=["2%", "33%"], + ) + + # Dummy leaderboard for handling the case when the user uses backspace key + hidden_leaderboard_table_for_search = gr.components.Dataframe( + value=invisible_df[COLS], # type: ignore + headers=COLS, + datatype=TYPES, + visible=False, + ) + search_bar.submit( + update_table, + [ + hidden_leaderboard_table_for_search, + shown_columns, + filter_columns_type, + filter_columns_precision, + filter_columns_size, + search_bar, + ], + leaderboard_table, + ) + for selector in [ + shown_columns, + filter_columns_type, + filter_columns_precision, + filter_columns_size, + ]: + selector.change( + update_table, + [ + hidden_leaderboard_table_for_search, + shown_columns, + filter_columns_type, + filter_columns_precision, + filter_columns_size, + search_bar, + ], + leaderboard_table, + queue=True, + ) + +if __name__ == "__main__": + demo.queue(default_concurrency_limit=40).launch() diff --git a/demos/model3D/files/Bunny.obj b/demos/model3D/files/Bunny.obj new file mode 100644 index 0000000000000000000000000000000000000000..9baeb363cce8feb5dd62ecaf8d64a14b6c50ce37 --- /dev/null +++ b/demos/model3D/files/Bunny.obj @@ -0,0 +1,7474 @@ +# OBJ file format with ext .obj +# vertex count = 2503 +# face count = 4968 +v -3.4101800e-003 1.3031957e-001 2.1754370e-002 +v -8.1719160e-002 1.5250145e-001 2.9656090e-002 +v 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", 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792 603 601 +f 793 601 843 +f 844 669 794 +f 599 843 601 +f 598 844 843 +f 597 845 844 +f 845 675 669 +f 845 596 591 +f 780 783 778 +# 1610 faces, 0 coords texture + +# End of File diff --git a/demos/model3D/files/sofia.stl b/demos/model3D/files/sofia.stl new file mode 100644 index 0000000000000000000000000000000000000000..8a811c851675350bc5f9ab0d1f0eff69651104ae Binary files /dev/null and b/demos/model3D/files/sofia.stl differ diff --git a/demos/model3D/files/source.txt b/demos/model3D/files/source.txt new file mode 100644 index 0000000000000000000000000000000000000000..95f4bcc925f0bf5cf6a00913e13532fd0616f77a --- /dev/null +++ b/demos/model3D/files/source.txt @@ -0,0 +1,13 @@ +Stanford Bunny: +https://graphics.stanford.edu/data/3Dscanrep/ +https://graphics.stanford.edu/~mdfisher/Data/Meshes/bunny.obj + +Duck & Fox: +https://github.com/KhronosGroup/glTF-Sample-Models + +Face: +https://github.com/mikedh/trimesh/tree/main/models + +NASA SOFIA: +https://nasa3d.arc.nasa.gov/detail/sofia +https://github.com/nasa/NASA-3D-Resources/blob/master/3D%20Models/SOFIA/Fuselage_top.stl \ No newline at end of file diff --git a/demos/model3D/run.ipynb b/demos/model3D/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..17b9ec140751406d0d006d43ab2d2ff94a34ef68 --- /dev/null +++ b/demos/model3D/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: model3D"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/Bunny.obj https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/Bunny.obj\n", "!wget -q -O files/Duck.glb https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/Duck.glb\n", "!wget -q -O files/Fox.gltf https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/Fox.gltf\n", "!wget -q -O files/face.obj https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/face.obj\n", "!wget -q -O files/sofia.stl https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/sofia.stl\n", "!wget -q -O files/source.txt https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/source.txt"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "def load_mesh(mesh_file_name):\n", " return mesh_file_name\n", "\n", "demo = gr.Interface(\n", " fn=load_mesh,\n", " inputs=gr.Model3D(),\n", " outputs=gr.Model3D(\n", " clear_color=(0.0, 0.0, 0.0, 0.0), label=\"3D Model\", display_mode=\"wireframe\"),\n", " examples=[\n", " [os.path.join(os.path.abspath(''), \"files/Bunny.obj\")],\n", " [os.path.join(os.path.abspath(''), \"files/Duck.glb\")],\n", " [os.path.join(os.path.abspath(''), \"files/Fox.gltf\")],\n", " [os.path.join(os.path.abspath(''), \"files/face.obj\")],\n", " [os.path.join(os.path.abspath(''), \"files/sofia.stl\")],\n", " [\"https://huggingface.co/datasets/dylanebert/3dgs/resolve/main/bonsai/bonsai-7k-mini.splat\"],\n", " [\"https://huggingface.co/datasets/dylanebert/3dgs/resolve/main/luigi/luigi.ply\"],\n", " ],\n", " cache_examples=True\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/model3D/run.py b/demos/model3D/run.py new file mode 100644 index 0000000000000000000000000000000000000000..659a13d65036c67f4de35e73587f3a847bfa0efd --- /dev/null +++ b/demos/model3D/run.py @@ -0,0 +1,25 @@ +import gradio as gr +import os + +def load_mesh(mesh_file_name): + return mesh_file_name + +demo = gr.Interface( + fn=load_mesh, + inputs=gr.Model3D(), + outputs=gr.Model3D( + clear_color=(0.0, 0.0, 0.0, 0.0), label="3D Model", display_mode="wireframe"), + examples=[ + [os.path.join(os.path.dirname(__file__), "files/Bunny.obj")], + [os.path.join(os.path.dirname(__file__), "files/Duck.glb")], + [os.path.join(os.path.dirname(__file__), "files/Fox.gltf")], + [os.path.join(os.path.dirname(__file__), "files/face.obj")], + [os.path.join(os.path.dirname(__file__), "files/sofia.stl")], + ["https://huggingface.co/datasets/dylanebert/3dgs/resolve/main/bonsai/bonsai-7k-mini.splat"], + ["https://huggingface.co/datasets/dylanebert/3dgs/resolve/main/luigi/luigi.ply"], + ], + cache_examples=True +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/native_plots/bar_plot_demo.py b/demos/native_plots/bar_plot_demo.py new file mode 100644 index 0000000000000000000000000000000000000000..857f801838926c072b279426943c5851c1f98903 --- /dev/null +++ b/demos/native_plots/bar_plot_demo.py @@ -0,0 +1,74 @@ +import gradio as gr +from data import temp_sensor_data, food_rating_data + +with gr.Blocks() as bar_plots: + with gr.Row(): + start = gr.DateTime("2021-01-01 00:00:00", label="Start") + end = gr.DateTime("2021-01-05 00:00:00", label="End") + apply_btn = gr.Button("Apply", scale=0) + with gr.Row(): + group_by = gr.Radio(["None", "30m", "1h", "4h", "1d"], value="None", label="Group by") + aggregate = gr.Radio(["sum", "mean", "median", "min", "max"], value="sum", label="Aggregation") + + temp_by_time = gr.BarPlot( + temp_sensor_data, + x="time", + y="temperature", + ) + temp_by_time_location = gr.BarPlot( + temp_sensor_data, + x="time", + y="temperature", + color="location", + ) + + time_graphs = [temp_by_time, temp_by_time_location] + group_by.change( + lambda group: [gr.BarPlot(x_bin=None if group == "None" else group)] * len(time_graphs), + group_by, + time_graphs + ) + aggregate.change( + lambda aggregate: [gr.BarPlot(y_aggregate=aggregate)] * len(time_graphs), + aggregate, + time_graphs + ) + + def rescale(select: gr.SelectData): + return select.index + rescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None, [start, end]) + + for trigger in [apply_btn.click, rescale_evt.then]: + trigger( + lambda start, end: [gr.BarPlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs + ) + + with gr.Row(): + price_by_cuisine = gr.BarPlot( + food_rating_data, + x="cuisine", + y="price", + ) + with gr.Column(scale=0): + gr.Button("Sort $ > $$$").click(lambda: gr.BarPlot(sort="y"), None, price_by_cuisine) + gr.Button("Sort $$$ > $").click(lambda: gr.BarPlot(sort="-y"), None, price_by_cuisine) + gr.Button("Sort A > Z").click(lambda: gr.BarPlot(sort=["Chinese", "Italian", "Mexican"]), None, price_by_cuisine) + + with gr.Row(): + price_by_rating = gr.BarPlot( + food_rating_data, + x="rating", + y="price", + x_bin=1, + ) + price_by_rating_color = gr.BarPlot( + food_rating_data, + x="rating", + y="price", + color="cuisine", + x_bin=1, + color_map={"Italian": "red", "Mexican": "green", "Chinese": "blue"}, + ) + +if __name__ == "__main__": + bar_plots.launch() diff --git a/demos/native_plots/data.py b/demos/native_plots/data.py new file mode 100644 index 0000000000000000000000000000000000000000..52aef7942fc213ebda2eeae466a1d397c3a55256 --- /dev/null +++ b/demos/native_plots/data.py @@ -0,0 +1,20 @@ +import pandas as pd +from random import randint, random + +temp_sensor_data = pd.DataFrame( + { + "time": pd.date_range("2021-01-01", end="2021-01-05", periods=200), + "temperature": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)], + "humidity": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)], + "location": ["indoor", "outdoor"] * 100, + } +) + +food_rating_data = pd.DataFrame( + { + "cuisine": [["Italian", "Mexican", "Chinese"][i % 3] for i in range(100)], + "rating": [random() * 4 + 0.5 * (i % 3) for i in range(100)], + "price": [randint(10, 50) + 4 * (i % 3) for i in range(100)], + "wait": [random() for i in range(100)], + } +) diff --git a/demos/native_plots/line_plot_demo.py b/demos/native_plots/line_plot_demo.py new file mode 100644 index 0000000000000000000000000000000000000000..166859615cdac183af15128b7ddabbabe36558ce --- /dev/null +++ b/demos/native_plots/line_plot_demo.py @@ -0,0 +1,66 @@ +import gradio as gr +from data import temp_sensor_data, food_rating_data + +with gr.Blocks() as line_plots: + with gr.Row(): + start = gr.DateTime("2021-01-01 00:00:00", label="Start") + end = gr.DateTime("2021-01-05 00:00:00", label="End") + apply_btn = gr.Button("Apply", scale=0) + with gr.Row(): + group_by = gr.Radio(["None", "30m", "1h", "4h", "1d"], value="None", label="Group by") + aggregate = gr.Radio(["sum", "mean", "median", "min", "max"], value="sum", label="Aggregation") + + temp_by_time = gr.LinePlot( + temp_sensor_data, + x="time", + y="temperature", + ) + temp_by_time_location = gr.LinePlot( + temp_sensor_data, + x="time", + y="temperature", + color="location", + ) + + time_graphs = [temp_by_time, temp_by_time_location] + group_by.change( + lambda group: [gr.LinePlot(x_bin=None if group == "None" else group)] * len(time_graphs), + group_by, + time_graphs + ) + aggregate.change( + lambda aggregate: [gr.LinePlot(y_aggregate=aggregate)] * len(time_graphs), + aggregate, + time_graphs + ) + + def rescale(select: gr.SelectData): + return select.index + rescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None, [start, end]) + + for trigger in [apply_btn.click, rescale_evt.then]: + trigger( + lambda start, end: [gr.LinePlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs + ) + + price_by_cuisine = gr.LinePlot( + food_rating_data, + x="cuisine", + y="price", + ) + with gr.Row(): + price_by_rating = gr.LinePlot( + food_rating_data, + x="rating", + y="price", + ) + price_by_rating_color = gr.LinePlot( + food_rating_data, + x="rating", + y="price", + color="cuisine", + color_map={"Italian": "red", "Mexican": "green", "Chinese": "blue"}, + ) + +if __name__ == "__main__": + line_plots.launch() diff --git a/demos/native_plots/requirements.txt b/demos/native_plots/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..d1c8a7ae0396d12417907feb8ef43fb4bcc8e89c --- /dev/null +++ b/demos/native_plots/requirements.txt @@ -0,0 +1 @@ +vega_datasets \ No newline at end of file diff --git a/demos/native_plots/run.ipynb b/demos/native_plots/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..19d02266ed36a60df7e491a1f2cbb672f2b9b20e --- /dev/null +++ b/demos/native_plots/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: native_plots"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio vega_datasets"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/native_plots/bar_plot_demo.py\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/native_plots/data.py\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/native_plots/line_plot_demo.py\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/native_plots/scatter_plot_demo.py"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "from scatter_plot_demo import scatter_plots\n", "from line_plot_demo import line_plots\n", "from bar_plot_demo import bar_plots\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Tabs():\n", " with gr.TabItem(\"Line Plot\"):\n", " line_plots.render()\n", " with gr.TabItem(\"Scatter Plot\"):\n", " scatter_plots.render()\n", " with gr.TabItem(\"Bar Plot\"):\n", " bar_plots.render()\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/native_plots/run.py b/demos/native_plots/run.py new file mode 100644 index 0000000000000000000000000000000000000000..b27c756117035ffd227afa74e16ac047631960dd --- /dev/null +++ b/demos/native_plots/run.py @@ -0,0 +1,17 @@ +import gradio as gr + +from scatter_plot_demo import scatter_plots +from line_plot_demo import line_plots +from bar_plot_demo import bar_plots + +with gr.Blocks() as demo: + with gr.Tabs(): + with gr.TabItem("Line Plot"): + line_plots.render() + with gr.TabItem("Scatter Plot"): + scatter_plots.render() + with gr.TabItem("Bar Plot"): + bar_plots.render() + +if __name__ == "__main__": + demo.launch() diff --git a/demos/native_plots/scatter_plot_demo.py b/demos/native_plots/scatter_plot_demo.py new file mode 100644 index 0000000000000000000000000000000000000000..9b7e57bf526dd7f643af89d68761ff638d6a7bd9 --- /dev/null +++ b/demos/native_plots/scatter_plot_demo.py @@ -0,0 +1,68 @@ +import gradio as gr +from data import temp_sensor_data, food_rating_data + +with gr.Blocks() as scatter_plots: + with gr.Row(): + start = gr.DateTime("2021-01-01 00:00:00", label="Start") + end = gr.DateTime("2021-01-05 00:00:00", label="End") + apply_btn = gr.Button("Apply", scale=0) + with gr.Row(): + group_by = gr.Radio(["None", "30m", "1h", "4h", "1d"], value="None", label="Group by") + aggregate = gr.Radio(["sum", "mean", "median", "min", "max"], value="sum", label="Aggregation") + + temp_by_time = gr.ScatterPlot( + temp_sensor_data, + x="time", + y="temperature", + ) + temp_by_time_location = gr.ScatterPlot( + temp_sensor_data, + x="time", + y="temperature", + color="location", + ) + + time_graphs = [temp_by_time, temp_by_time_location] + group_by.change( + lambda group: [gr.ScatterPlot(x_bin=None if group == "None" else group)] * len(time_graphs), + group_by, + time_graphs + ) + aggregate.change( + lambda aggregate: [gr.ScatterPlot(y_aggregate=aggregate)] * len(time_graphs), + aggregate, + time_graphs + ) + + # def rescale(select: gr.SelectData): + # return select.index + # rescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None, [start, end]) + + # for trigger in [apply_btn.click, rescale_evt.then]: + # trigger( + # lambda start, end: [gr.ScatterPlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs + # ) + + price_by_cuisine = gr.ScatterPlot( + food_rating_data, + x="cuisine", + y="price", + ) + with gr.Row(): + price_by_rating = gr.ScatterPlot( + food_rating_data, + x="rating", + y="price", + color="wait", + show_actions_button=True, + ) + price_by_rating_color = gr.ScatterPlot( + food_rating_data, + x="rating", + y="price", + color="cuisine", + # color_map={"Italian": "red", "Mexican": "green", "Chinese": "blue"}, + ) + +if __name__ == "__main__": + scatter_plots.launch() diff --git a/demos/reverse_audio/audio/cantina.wav b/demos/reverse_audio/audio/cantina.wav new file mode 100644 index 0000000000000000000000000000000000000000..41f020438468229763ec4a2321325e5916e09106 Binary files /dev/null and b/demos/reverse_audio/audio/cantina.wav differ diff --git a/demos/reverse_audio/audio/recording1.wav b/demos/reverse_audio/audio/recording1.wav new file mode 100644 index 0000000000000000000000000000000000000000..a58ba5006d4afbfce2b13764e0fc8768286a340d Binary files /dev/null and b/demos/reverse_audio/audio/recording1.wav differ diff --git a/demos/reverse_audio/run.ipynb b/demos/reverse_audio/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..464bab62c96ca32a420df36973208da428e24f4d --- /dev/null +++ b/demos/reverse_audio/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: reverse_audio"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('audio')\n", "!wget -q -O audio/cantina.wav https://github.com/gradio-app/gradio/raw/main/demo/reverse_audio/audio/cantina.wav\n", "!wget -q -O audio/recording1.wav https://github.com/gradio-app/gradio/raw/main/demo/reverse_audio/audio/recording1.wav"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["\n", "import numpy as np\n", "\n", "import gradio as gr\n", "\n", "def reverse_audio(audio):\n", " sr, data = audio\n", " return (sr, np.flipud(data))\n", "\n", "input_audio = gr.Audio(\n", " sources=[\"microphone\"],\n", " waveform_options=gr.WaveformOptions(\n", " waveform_color=\"#01C6FF\",\n", " waveform_progress_color=\"#0066B4\",\n", " skip_length=2,\n", " show_controls=False,\n", " ),\n", ")\n", "demo = gr.Interface(\n", " fn=reverse_audio,\n", " inputs=input_audio,\n", " outputs=\"audio\"\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/reverse_audio/run.py b/demos/reverse_audio/run.py new file mode 100644 index 0000000000000000000000000000000000000000..3d60f77c5a99cfcdc24704c5c175eda2008bfc15 --- /dev/null +++ b/demos/reverse_audio/run.py @@ -0,0 +1,26 @@ + +import numpy as np + +import gradio as gr + +def reverse_audio(audio): + sr, data = audio + return (sr, np.flipud(data)) + +input_audio = gr.Audio( + sources=["microphone"], + waveform_options=gr.WaveformOptions( + waveform_color="#01C6FF", + waveform_progress_color="#0066B4", + skip_length=2, + show_controls=False, + ), +) +demo = gr.Interface( + fn=reverse_audio, + inputs=input_audio, + outputs="audio" +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/reverse_audio/screenshot.png b/demos/reverse_audio/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..796151630ac2bb931d09cfa511c0084a6fc391a1 Binary files /dev/null and b/demos/reverse_audio/screenshot.png differ diff --git a/demos/stream_audio/run.ipynb b/demos/stream_audio/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..177973a133c6b1e961e854537f57b8c9870c82ea --- /dev/null +++ b/demos/stream_audio/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: stream_audio"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import time\n", "\n", "def add_to_stream(audio, instream):\n", " time.sleep(1)\n", " if audio is None:\n", " return gr.Audio(), instream\n", " if instream is None:\n", " ret = audio\n", " else:\n", " ret = (audio[0], np.concatenate((instream[1], audio[1])))\n", " return ret, ret\n", "\n", "with gr.Blocks() as demo:\n", " inp = gr.Audio(sources=[\"microphone\"])\n", " out = gr.Audio()\n", " stream = gr.State()\n", " clear = gr.Button(\"Clear\")\n", "\n", " inp.stream(add_to_stream, [inp, stream], [out, stream])\n", " clear.click(lambda: [None, None, None], None, [inp, out, stream])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/stream_audio/run.py b/demos/stream_audio/run.py new file mode 100644 index 0000000000000000000000000000000000000000..6277041827b55c8e5ed80b2eeea785c0779791b5 --- /dev/null +++ b/demos/stream_audio/run.py @@ -0,0 +1,25 @@ +import gradio as gr +import numpy as np +import time + +def add_to_stream(audio, instream): + time.sleep(1) + if audio is None: + return gr.Audio(), instream + if instream is None: + ret = audio + else: + ret = (audio[0], np.concatenate((instream[1], audio[1]))) + return ret, ret + +with gr.Blocks() as demo: + inp = gr.Audio(sources=["microphone"]) + out = gr.Audio() + stream = gr.State() + clear = gr.Button("Clear") + + inp.stream(add_to_stream, [inp, stream], [out, stream]) + clear.click(lambda: [None, None, None], None, [inp, out, stream]) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/stream_frames/run.ipynb b/demos/stream_frames/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..1fab7111bf62bd9e9dbbc33ffeb473bf5701a19b --- /dev/null +++ b/demos/stream_frames/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: stream_frames"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "\n", "def flip(im):\n", " return np.flipud(im)\n", "\n", "demo = gr.Interface(\n", " flip,\n", " gr.Image(sources=[\"webcam\"], streaming=True),\n", " \"image\",\n", " live=True\n", ")\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/stream_frames/run.py b/demos/stream_frames/run.py new file mode 100644 index 0000000000000000000000000000000000000000..4ec25c423236c58a67abb5793f3fa107bc8561e7 --- /dev/null +++ b/demos/stream_frames/run.py @@ -0,0 +1,14 @@ +import gradio as gr +import numpy as np + +def flip(im): + return np.flipud(im) + +demo = gr.Interface( + flip, + gr.Image(sources=["webcam"], streaming=True), + "image", + live=True +) +if __name__ == "__main__": + demo.launch() diff --git a/demos/stt_or_tts/run.ipynb b/demos/stt_or_tts/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f1608fcaf52e70947e91c19182896debceb85979 --- /dev/null +++ b/demos/stt_or_tts/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: stt_or_tts"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "tts_examples = [\n", " \"I love learning machine learning\",\n", " \"How do you do?\",\n", "]\n", "\n", "tts_demo = gr.load(\n", " \"huggingface/facebook/fastspeech2-en-ljspeech\",\n", " title=None,\n", " examples=tts_examples,\n", " description=\"Give me something to say!\",\n", ")\n", "\n", "stt_demo = gr.load(\n", " \"huggingface/facebook/wav2vec2-base-960h\",\n", " title=None,\n", " inputs=gr.Microphone(type=\"filepath\"),\n", " description=\"Let me try to guess what you're saying!\",\n", ")\n", "\n", "demo = gr.TabbedInterface([tts_demo, stt_demo], [\"Text-to-speech\", \"Speech-to-text\"])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/stt_or_tts/run.py b/demos/stt_or_tts/run.py new file mode 100644 index 0000000000000000000000000000000000000000..e364cba3e7b42c0965d751e2aa3bf3999445c041 --- /dev/null +++ b/demos/stt_or_tts/run.py @@ -0,0 +1,25 @@ +import gradio as gr + +tts_examples = [ + "I love learning machine learning", + "How do you do?", +] + +tts_demo = gr.load( + "huggingface/facebook/fastspeech2-en-ljspeech", + title=None, + examples=tts_examples, + description="Give me something to say!", +) + +stt_demo = gr.load( + "huggingface/facebook/wav2vec2-base-960h", + title=None, + inputs=gr.Microphone(type="filepath"), + description="Let me try to guess what you're saying!", +) + +demo = gr.TabbedInterface([tts_demo, stt_demo], ["Text-to-speech", "Speech-to-text"]) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/video_component/files/a.mp4 b/demos/video_component/files/a.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..95a61f6b4a753497d97f51c6a8f18727cef7d628 Binary files /dev/null and b/demos/video_component/files/a.mp4 differ diff --git a/demos/video_component/files/b.mp4 b/demos/video_component/files/b.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..7b2d7c723ea53ce763eb49f551bd955858850673 Binary files /dev/null and b/demos/video_component/files/b.mp4 differ diff --git a/demos/video_component/files/world.mp4 b/demos/video_component/files/world.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..9bce44c33e275d6107240a1101032a7835fd8eed --- /dev/null +++ b/demos/video_component/files/world.mp4 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:71944d7430c461f0cd6e7fd10cee7eb72786352a3678fc7bc0ae3d410f72aece +size 1570024 diff --git a/demos/video_component/run.ipynb b/demos/video_component/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e64eb8c111834e557ef31fc6cfadae995a565921 --- /dev/null +++ b/demos/video_component/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: video_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/a.mp4 https://github.com/gradio-app/gradio/raw/main/demo/video_component/files/a.mp4\n", "!wget -q -O files/b.mp4 https://github.com/gradio-app/gradio/raw/main/demo/video_component/files/b.mp4\n", "!wget -q -O files/world.mp4 https://github.com/gradio-app/gradio/raw/main/demo/video_component/files/world.mp4"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "a = os.path.join(os.path.abspath(''), \"files/world.mp4\") # Video\n", "b = os.path.join(os.path.abspath(''), \"files/a.mp4\") # Video\n", "c = os.path.join(os.path.abspath(''), \"files/b.mp4\") # Video\n", "\n", "demo = gr.Interface(\n", " fn=lambda x: x,\n", " inputs=gr.Video(),\n", " outputs=gr.Video(),\n", " examples=[\n", " [a],\n", " [b],\n", " [c],\n", " ],\n", " cache_examples=True\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/video_component/run.py b/demos/video_component/run.py new file mode 100644 index 0000000000000000000000000000000000000000..385026b01d5de19043080980d341be3bc2f5acdf --- /dev/null +++ b/demos/video_component/run.py @@ -0,0 +1,21 @@ +import gradio as gr +import os + +a = os.path.join(os.path.dirname(__file__), "files/world.mp4") # Video +b = os.path.join(os.path.dirname(__file__), "files/a.mp4") # Video +c = os.path.join(os.path.dirname(__file__), "files/b.mp4") # Video + +demo = gr.Interface( + fn=lambda x: x, + inputs=gr.Video(), + outputs=gr.Video(), + examples=[ + [a], + [b], + [c], + ], + cache_examples=True +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/zip_files/files/titanic.csv b/demos/zip_files/files/titanic.csv new file mode 100644 index 0000000000000000000000000000000000000000..63b68ab0ba98c667f515c52f08c0bbd5573d5330 --- /dev/null +++ b/demos/zip_files/files/titanic.csv @@ -0,0 +1,892 @@ +PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked +1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S +2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C +3,1,3,"Heikkinen, Miss. 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Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S +890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C +891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q diff --git a/demos/zip_files/run.ipynb b/demos/zip_files/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3208c2010db0d09de877148c759e8745ef4f3824 --- /dev/null +++ b/demos/zip_files/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: zip_files"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/titanic.csv https://github.com/gradio-app/gradio/raw/main/demo/zip_files/files/titanic.csv"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import os\n", "from zipfile import ZipFile\n", "\n", "import gradio as gr\n", "\n", "def zip_files(files):\n", " with ZipFile(\"tmp.zip\", \"w\") as zipObj:\n", " for idx, file in enumerate(files):\n", " zipObj.write(file.name, file.name.split(\"/\")[-1])\n", " return \"tmp.zip\"\n", "\n", "demo = gr.Interface(\n", " zip_files,\n", " gr.File(file_count=\"multiple\", file_types=[\"text\", \".json\", \".csv\"]),\n", " \"file\",\n", " examples=[[[os.path.join(os.path.abspath(''),\"files/titanic.csv\"),\n", " os.path.join(os.path.abspath(''),\"files/titanic.csv\"),\n", " os.path.join(os.path.abspath(''),\"files/titanic.csv\")]]],\n", " cache_examples=True\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/zip_files/run.py b/demos/zip_files/run.py new file mode 100644 index 0000000000000000000000000000000000000000..6f087c0855f926444d0ef10f8ab5215ca08bdca0 --- /dev/null +++ b/demos/zip_files/run.py @@ -0,0 +1,23 @@ +import os +from zipfile import ZipFile + +import gradio as gr + +def zip_files(files): + with ZipFile("tmp.zip", "w") as zipObj: + for idx, file in enumerate(files): + zipObj.write(file.name, file.name.split("/")[-1]) + return "tmp.zip" + +demo = gr.Interface( + zip_files, + gr.File(file_count="multiple", file_types=["text", ".json", ".csv"]), + "file", + examples=[[[os.path.join(os.path.dirname(__file__),"files/titanic.csv"), + os.path.join(os.path.dirname(__file__),"files/titanic.csv"), + os.path.join(os.path.dirname(__file__),"files/titanic.csv")]]], + cache_examples=True +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/zip_files/screenshot.png b/demos/zip_files/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..64591ff8a23e8c77759f60f7caddc61fb84f3324 Binary files /dev/null and b/demos/zip_files/screenshot.png differ diff --git a/image.png b/image.png new file mode 100644 index 0000000000000000000000000000000000000000..dc392ea95476974e07a68ad98a9659c240e1089b Binary files /dev/null and b/image.png differ diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..7a97b0710cd819a273f79e317c70a0f70a838b75 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,6 @@ +gradio-client @ git+https://github.com/gradio-app/gradio@4837a6c9f0976b20a98686806bad9fba1e9cd326#subdirectory=client/python +https://gradio-pypi-previews.s3.amazonaws.com/4837a6c9f0976b20a98686806bad9fba1e9cd326/gradio-4.44.0-py3-none-any.whl +pypistats==1.1.0 +plotly +altair +vega_datasets \ No newline at end of file diff --git a/templates/index.html b/templates/index.html new file mode 100644 index 0000000000000000000000000000000000000000..3eef8da37e6796a02af89cf666bbbe0b733a6079 --- /dev/null +++ b/templates/index.html @@ -0,0 +1,118 @@ + + + + {%if is_space %} + + {% endif %} + + + + +
+
+ +
+
+ {% for name in names %} + {{ name[0] }} {% if name[1] %}❌{% endif %} + {% endfor %} +
+
+
+ +
+ + \ No newline at end of file