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| import gradio as gr | |
| from pipeline_utils import task_dropdown_choices, handle_task_change, review_training_choices, test_pipeline | |
| playground = gr.Blocks() | |
| def create_playground_header(): | |
| gr.Markdown(""" | |
| # 🤗 Hugging Face Playground | |
| **Try your ideas here. Select from Text, Image or Audio** | |
| """) | |
| def create_playground_footer(): | |
| gr.Markdown(""" | |
| ### To Learn More about 🤗 Hugging Face,[Click Here](https://huggingface.co/docs) | |
| ### [Click Here](https://huggingface.co/spaces/nsethi610/ns-gradio-apps/discussions/1) to provide Feedback, or participate in development of this tool.Let's make AI easy for everyone. | |
| """) | |
| def create_tabs_header(): | |
| with gr.Row(): | |
| with gr.Column(scale=4): | |
| radio = gr.Radio( | |
| ["Use Pipeline", "Fine Tune"], | |
| label="Select Use Pipeline to try out HF models or Fine Tune to test it on your own datasets", | |
| value="Use Pipeline", | |
| interactive=True, | |
| ) | |
| with gr.Column(scale=1): | |
| test_pipeline_button = gr.Button( | |
| value="Test", variant="primary", size="sm") | |
| return radio, test_pipeline_button | |
| with playground: | |
| create_playground_header() | |
| with gr.Tabs(): | |
| with gr.TabItem("Text"): | |
| radio, test_pipeline_button = create_tabs_header() | |
| with gr.Row(visible=True) as use_pipeline: | |
| with gr.Column(): | |
| task_dropdown = gr.Dropdown( | |
| choices=task_dropdown_choices(), | |
| label="Task", | |
| interactive=True, | |
| info="Select Pipelines for natural language processing tasks or type if you have your own." | |
| ) | |
| model_dropdown = gr.Dropdown( | |
| [], label="Model", info="Select appropriate Model based on the task you selected") | |
| prompt_textarea = gr.TextArea( | |
| label="Prompt", | |
| value="Enter your prompt here", | |
| text_align="left", | |
| info="Copy/Paste or type your prompt to try out. Make sure to provide clear prompt or try with different prompts" | |
| ) | |
| context_for_question_answer = gr.TextArea( | |
| label="Context", | |
| value="Enter Context for your question here", | |
| visible=False, | |
| interactive=True, | |
| info="Question answering tasks return an answer given a question. If you’ve ever asked a virtual assistant like Alexa, Siri or Google what the weather is, then you’ve used a question answering model before. Here, we are doing Extractive(extract the answer from the given context) Question answering. " | |
| ) | |
| task_dropdown.change(handle_task_change, | |
| inputs=[task_dropdown], | |
| outputs=[context_for_question_answer, | |
| model_dropdown, task_dropdown]) | |
| with gr.Column(): | |
| text = gr.TextArea(label="Generated Text") | |
| radio.change(review_training_choices, | |
| inputs=radio, outputs=use_pipeline) | |
| test_pipeline_button.click(test_pipeline, | |
| inputs=[ | |
| task_dropdown, model_dropdown, prompt_textarea, | |
| context_for_question_answer], | |
| outputs=text) | |
| with gr.TabItem("Image"): | |
| radio, test_pipeline_button = create_tabs_header() | |
| gr.Markdown(""" | |
| > WIP | |
| """) | |
| with gr.TabItem("Audio"): | |
| radio, test_pipeline_button = create_tabs_header() | |
| gr.Markdown(""" | |
| > WIP | |
| """) | |
| create_playground_footer() | |
| playground.launch(share=True) | |