refactored app.py

app.py

@@ -1,119 +1,60 @@
 import gradio as gr
-from transformers import pipeline
+from task import tasks_config
+from pipeline_utils import handle_task_change, review_training_choices, test_pipeline
+from playground_utils import create_playground_header, create_playground_footer, create_tabs_header
 
 playground = gr.Blocks()
 
-
-def review_training_choices(choice):
-    print(choice)
-    if choice == "Use Pipeline":
-        return gr.Row(visible=True)
-    else:
-        return gr.Row(visible=False)
-
-
-def show_optional_fields(task):
-    if task == "question-answering":
-        return gr.TextArea(visible=True)
-    return gr.TextArea(visible=False)
-
-
-def test_pipeline(task, model=None, prompt=None, context=None):
-    if model:
-        test = pipeline(task, model=model)
-    else:
-        if task == "ner":
-            test = pipeline(task, grouped_entities=True)
-        else:
-            test = pipeline(task)
-    if task == "question-answering":
-        if not context:
-            return "Context is required"
-        else:
-            result = test(question=prompt, context=context)
-    else:
-        result = test(prompt)
-    match task:
-        case "text-generation":
-            return gr.TextArea(result[0]["generated_text"])
-        case "fill-mask":
-            return gr.TextArea(result[0]["sequence"])
-        case "summarization":
-            return gr.TextArea(result[0]["summary_text"])
-        case "ner":
-            ner_result = "\n".join(
-                f"{k}={v}" for item in result for k, v in item.items() if k not in ["start", "end", "index"])
-            return gr.TextArea(ner_result.rstrip("\n"))
-
-        case "question-answering":
-            return gr.TextArea(result)
-
-
 with playground:
-    gr.Markdown("""
-                Try your ideas here. Select from Text, Image or Audio
-                """)
+    create_playground_header()
     with gr.Tabs():
         with gr.TabItem("Text"):
-            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")
+            radio, test_pipeline_button = create_tabs_header()
             with gr.Row(visible=True) as use_pipeline:
                 with gr.Column():
                     task_dropdown = gr.Dropdown(
-                        [("Text Generation", "text-generation"), ("Fill Mask",
-                                                                  "fill-mask"), ("Summarization", "summarization"), ("Named Entity Recognition", "ner"), ("Question Answering", "question-answering")],
-                        label="task",
+                        choices=[(task["name"], task_id)
+                                 for task_id, task in tasks_config.items()],
+                        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",
-                        allow_custom_value=True,
-                        interactive=True
-                    )
+                        [], 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")
+                        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)
-                    task_dropdown.change(show_optional_fields, inputs=[
-                                         task_dropdown], outputs=[context_for_question_answer])
+                        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)
+            test_pipeline_button.click(test_pipeline,
+                                       inputs=[
+                                           task_dropdown, model_dropdown, prompt_textarea, context_for_question_answer],
+                                       outputs=text)
         with gr.TabItem("Image"):
-            with gr.Row():
-                with gr.Column(scale=3):
-                    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")
+            radio, test_pipeline_button = create_tabs_header()
+            gr.Markdown("""
+                        > WIP
+                        """)
         with gr.TabItem("Audio"):
-            with gr.Row():
-                with gr.Column(scale=3):
-                    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")
-
-playground.launch(share=True)
+            radio, test_pipeline_button = create_tabs_header()
+            gr.Markdown("""
+                        > WIP
+                        """)
+    create_playground_footer()
+playground.launch()