Create playground_utils.py

playground_utils.py

@@ -0,0 +1,60 @@
+import gradio as gr
+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()
+
+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["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", 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()