| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 19, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np\n", | |
| "import gradio as gr\n", | |
| "import requests\n", | |
| "import json" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "outputs": [], | |
| "source": [ | |
| "def list_to_dict(data):\n", | |
| " results = {}\n", | |
| "\n", | |
| " for i in range(len(data)):\n", | |
| " # Access the i-th dictionary in the list using an integer index\n", | |
| " d = data[i]\n", | |
| " # Assign the value of the 'label' key to the 'score' value in the results dictionary\n", | |
| " results[d['label']] = d['score']\n", | |
| "\n", | |
| " # The results dictionary will now contain the label-score pairs from the data list\n", | |
| " return results" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 21, | |
| "outputs": [], | |
| "source": [ | |
| "\n", | |
| "\n", | |
| "API_URL = \"https://api-inference.huggingface.co/models/nateraw/food\"\n", | |
| "headers = {\"Authorization\": \"Bearer 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\"}\n", | |
| "\n", | |
| "def query(filename):\n", | |
| " with open(filename, \"rb\") as f:\n", | |
| " data = f.read()\n", | |
| " response = requests.request(\"POST\", API_URL, headers=headers, data=data)\n", | |
| " output = json.loads(response.content.decode(\"utf-8\"))\n", | |
| " return list_to_dict(output),json.dumps(output, indent=2, sort_keys=True)" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 27, | |
| "outputs": [], | |
| "source": [ | |
| "def get_nutrition_info(food_name):\n", | |
| " #Make request to Nutritionix API\n", | |
| " response = requests.get(\n", | |
| " \"https://trackapi.nutritionix.com/v2/search/instant\",\n", | |
| " params={\"query\": food_name},\n", | |
| " headers={\n", | |
| " \"x-app-id\": \"63a710ef\",\n", | |
| " \"x-app-key\": \"3ddc7e3feda88e1cf6dd355fb26cb261\"\n", | |
| " }\n", | |
| " )\n", | |
| " #Parse response and return relevant information\n", | |
| " data = response.json()\n", | |
| " response = data[\"branded\"][0][\"photo\"][\"thumb\"]\n", | |
| "\n", | |
| " # Open the image using PIL\n", | |
| "\n", | |
| " return {\n", | |
| " \"food_name\": data[\"branded\"][0][\"food_name\"],\n", | |
| " \"calories\": data[\"branded\"][0][\"nf_calories\"],\n", | |
| " \"serving_size\": data[\"branded\"][0][\"serving_qty\"],\n", | |
| " \"serving_unit\": data[\"branded\"][0][\"serving_unit\"],\n", | |
| " #\"images\": data[\"branded\"][0][\"photo\"]\n", | |
| " },response" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 28, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "({'food_name': 'Hamburger',\n 'calories': 340,\n 'serving_size': 1,\n 'serving_unit': 'sandwich'},\n 'https://d2eawub7utcl6.cloudfront.net/images/nix-apple-grey.png')" | |
| }, | |
| "execution_count": 28, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "get_nutrition_info(\"Hamburger\")" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 22, | |
| "outputs": [], | |
| "source": [], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 22, | |
| "outputs": [], | |
| "source": [], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Running on local URL: http://127.0.0.1:7869\n", | |
| "Running on public URL: https://f7f1e48778aede65.gradio.app\n", | |
| "\n", | |
| "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/plain": "<IPython.core.display.HTML object>", | |
| "text/html": "<div><iframe src=\"https://f7f1e48778aede65.gradio.app\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "with gr.Blocks() as demo:\n", | |
| " gr.Markdown(\"Food-Classification-Calorie-Estimation and Volume-Estimation\")\n", | |
| " with gr.Tab(\"Food Classification\"):\n", | |
| " text_input = gr.Image(type=\"filepath\")\n", | |
| " text_output = [gr.Label(num_top_classes=6),\n", | |
| " gr.Textbox()\n", | |
| " ]\n", | |
| " text_button = gr.Button(\"Food Classification\")\n", | |
| " with gr.Tab(\"Food Calorie Estimation\"):\n", | |
| " image_input = gr.Textbox(label=\"Please enter the name of the Food you want to get calorie\")\n", | |
| " image_output = [gr.Textbox(),\n", | |
| " gr.Image(type=\"filepath\")\n", | |
| " ]\n", | |
| " image_button = gr.Button(\"Estimate Calories!\")\n", | |
| " with gr.Tab(\"Volume Estimation\"):\n", | |
| " _image_input = gr.Textbox(label=\"Please enter the name of the Food you want to get calorie\")\n", | |
| " _image_output = [gr.Textbox(),\n", | |
| " gr.Image()\n", | |
| " ]\n", | |
| " _image_button = gr.Button(\"Volume Calculation\")\n", | |
| " with gr.Tab(\"Future Works\"):\n", | |
| " gr.Markdown(\"Future work on Food Classification\")\n", | |
| " gr.Markdown(\n", | |
| " \"Currently the Model is trained on food-101 Dataset, which has 100 classes, In the future iteration of the project we would like to train the model on UNIMIB Dataset with 256 Food Classes\")\n", | |
| " gr.Markdown(\"Future work on Volume Estimation\")\n", | |
| " gr.Markdown(\n", | |
| " \"The volume model has been trained on Apple AR Toolkit and thus can be executred only on Apple devices ie a iOS platform, In futur we would like to train the volume model such that it is Platform independent\")\n", | |
| " gr.Markdown(\"Future work on Calorie Estimation\")\n", | |
| " gr.Markdown(\n", | |
| " \"The Calorie Estimation currently relies on Nutritionix API , In Future Iteration we would like to build our own Custom Database of Major Food Product across New York Restaurent\")\n", | |
| " gr.Markdown(\"https://github.com/Ali-Maq/Food-Classification-Volume-Estimation-and-Calorie-Estimation/blob/main/README.md\")\n", | |
| "\n", | |
| " text_button.click(query, inputs=text_input, outputs=text_output)\n", | |
| " image_button.click(get_nutrition_info, inputs=image_input, outputs=image_output)\n", | |
| " _image_button.click(get_nutrition_info, inputs=_image_input, outputs=_image_output)\n", | |
| " with gr.Accordion(\"Open for More!\"):\n", | |
| " gr.Markdown(\"🍎 Designed and built by Ali Under the Guidance of Professor Dennis Shasha\")\n", | |
| " gr.Markdown(\"Contact me at [email protected] 😊\")\n", | |
| "\n", | |
| "demo.launch(share=True, debug=True)" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "is_executing": true | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np\n", | |
| "import gradio as gr\n", | |
| "\n", | |
| "def flip_text(x):\n", | |
| " return x[::-1]\n", | |
| "\n", | |
| "def flip_image(x):\n", | |
| " return np.fliplr(x)\n", | |
| "\n", | |
| "with gr.Blocks() as demo:\n", | |
| " gr.Markdown(\"Flip text or image files using this demo.\")\n", | |
| " with gr.Tab(\"Flip Text\"):\n", | |
| " text_input = gr.Textbox()\n", | |
| " text_output = gr.Textbox()\n", | |
| " text_button = gr.Button(\"Flip\")\n", | |
| " with gr.Tab(\"Flip Image\"):\n", | |
| " with gr.Row():\n", | |
| " image_input = gr.Image()\n", | |
| " image_output = gr.Image()\n", | |
| " image_button = gr.Button(\"Flip\")\n", | |
| "\n", | |
| " with gr.Accordion(\"Open for More!\"):\n", | |
| " gr.Markdown(\"Look at me...\")\n", | |
| "\n", | |
| " text_button.click(get_nutrition_info, inputs=text_input, outputs=text_output)\n", | |
| " image_button.click(query, inputs=image_input, outputs=image_output)\n", | |
| "\n", | |
| "demo.launch()" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "is_executing": true | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "outputs": [], | |
| "source": [], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 2 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython2", | |
| "version": "2.7.6" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 0 | |
| } | |