Update app.py

app.py

@@ -1481,123 +1481,61 @@ files_list = [
     "178041_000197000852_V2.pdf"
 ]
 
-def generate_table_html(responses):
-    # HTML structure for the table
-    table_html = """
-    <h2>Top 3 Responses</h2>
-    <table style="width:100%; border-collapse: collapse; border: 1px solid black;">
-        <tr>
-            <th style="border: 1px solid black; padding: 8px;">Container (Document Name)</th>
-            <th style="border: 1px solid black; padding: 8px;">Page Number</th>
-            <th style="border: 1px solid black; padding: 8px;">Response</th>
-            <th style="border: 1px solid black; padding: 8px;">Metadata</th>
-        </tr>
-    """
 
-    for response in responses[:3]:  # Limiting to top 3 responses
-        document_name = response.get("document_name", "Unknown")
-        page_number = response.get("page_number", "Unknown")
-        content = response.get("content", "No content available")
-        metadata = response.get("metadata", "No metadata available")
-
-        # Adding rows dynamically to the table
-        table_html += f"""
-        <tr>
-            <td style="border: 1px solid black; padding: 8px;">{document_name}</td>
-            <td style="border: 1px solid black; padding: 8px;">{page_number}</td>
-            <td style="border: 1px solid black; padding: 8px;">{content}</td>
-            <td style="border: 1px solid black; padding: 8px;">{metadata}</td>
-        </tr>
-        """
 
-    # Closing the table
-    table_html += "</table>"
-    return table_html
 
 
-def generate_bot_response_with_table(history, choice, retrieval_mode, model_choice):
-    if not history:
-        return
 
-    # Select the model
-    selected_model = chat_model if model_choice == "LM-1" else (chat_model1 if model_choice == "LM-3" else phi_pipe)
 
-    response, addresses = generate_answer(history[-1][0], choice, retrieval_mode, selected_model)
-
-    # Extract relevant information from the response to create table content
-    responses = []
-    for doc in response[:3]:  # Limiting to top 3 results
-        responses.append({
-            "document_name": doc.get("document_name", "Unknown"),
-            "page_number": doc.get("page_number", "Unknown"),
-            "content": doc.get("content", "No content available"),
-            "metadata": doc.get("metadata", "No metadata available")
-        })
-
-    # Generate the HTML for the table
-    table_html = generate_table_html(responses)
-
-    history[-1][1] = ""
-
-    for character in response:
-        history[-1][1] += character
-        yield history, table_html  # Yielding history and table HTML for real-time updates
-
-    yield history, table_html  # Final yield with the complete response and table
-
-
-
-
-
-# with gr.Blocks(theme='gradio/soft') as demo:
+with gr.Blocks(theme='gradio/soft') as demo:
 
    
-#     with gr.Row():
-#         with gr.Column():
-#             state = gr.State()
+    with gr.Row():
+        with gr.Column():
+            state = gr.State()
 
-#             # File dropdown
-#             # file_dropdown = gr.Dropdown(label="Select Document", choices=files_list, value=None)
+            # File dropdown
+            # file_dropdown = gr.Dropdown(label="Select Document", choices=files_list, value=None)
 
-#             chatbot = gr.Chatbot([], elem_id="RADAR:Channel 94.1", bubble_full_width=False)
-#             choice = gr.Radio(label="Select Style", choices=["Details", "Conversational"], value="Conversational",interactive=False,visible=False)
-#             retrieval_mode = gr.Radio(label="Retrieval Mode", choices=["VDB", "KGF"], value="VDB",interactive=False,visible=False)
-#             model_choice = gr.Dropdown(label="Choose Model", choices=["LM-2"], value="LM-2")
+            chatbot = gr.Chatbot([], elem_id="RADAR:Channel 94.1", bubble_full_width=False)
+            choice = gr.Radio(label="Select Style", choices=["Details", "Conversational"], value="Conversational",interactive=False,visible=False)
+            retrieval_mode = gr.Radio(label="Retrieval Mode", choices=["VDB", "KGF"], value="VDB",interactive=False,visible=False)
+            model_choice = gr.Dropdown(label="Choose Model", choices=["LM-2"], value="LM-2")
 
-#             # Link the dropdown change to handle_model_choice_change
-#             model_choice.change(fn=handle_model_choice_change, inputs=model_choice, outputs=[retrieval_mode, choice, choice])
+            # Link the dropdown change to handle_model_choice_change
+            model_choice.change(fn=handle_model_choice_change, inputs=model_choice, outputs=[retrieval_mode, choice, choice])
 
-#             # gr.Markdown("<h1 style='color: red;'>Talk to RADAR</h1>", elem_id="voice-markdown")
+            # gr.Markdown("<h1 style='color: red;'>Talk to RADAR</h1>", elem_id="voice-markdown")
 
-#             chat_input = gr.Textbox(show_copy_button=True, interactive=True, show_label=False, label="ASK Radar !!!")
-#             tts_choice = gr.Radio(label="Select TTS System", choices=["Alpha", "Beta"], value="Alpha")
+            chat_input = gr.Textbox(show_copy_button=True, interactive=True, show_label=False, label="ASK Radar !!!")
+            tts_choice = gr.Radio(label="Select TTS System", choices=["Alpha", "Beta"], value="Alpha")
         
-#             retriever_button = gr.Button("Retriever")
+            retriever_button = gr.Button("Retriever")
 
-#             clear_button = gr.Button("Clear")
-#             clear_button.click(lambda: [None, None], outputs=[chat_input, state])
+            clear_button = gr.Button("Clear")
+            clear_button.click(lambda: [None, None], outputs=[chat_input, state])
 
-#             # gr.Markdown("<h1 style='color: red;'>Radar Map</h1>", elem_id="Map-Radar")
-#             # location_output = gr.HTML()
-#             audio_output = gr.Audio(interactive=False, autoplay=True)
+            # gr.Markdown("<h1 style='color: red;'>Radar Map</h1>", elem_id="Map-Radar")
+            # location_output = gr.HTML()
+            audio_output = gr.Audio(interactive=False, autoplay=True)
 
-#             def stop_audio():
-#                 audio_output.stop()
-#                 return None
+            def stop_audio():
+                audio_output.stop()
+                return None
 
                
     
            
 
-#             retriever_sequence = (
-#                 retriever_button.click(fn=stop_audio, inputs=[], outputs=[audio_output], api_name="api_stop_audio_recording")
-#                 .then(fn=add_message, inputs=[chatbot, chat_input], outputs=[chatbot, chat_input], api_name="api_addprompt_chathistory")
-#                 # First, generate the bot response
-#                 .then(fn=generate_bot_response, inputs=[chatbot, choice, retrieval_mode, model_choice], outputs=[chatbot], api_name="api_generate_bot_response")
-#                 # Then, generate the TTS response based on the bot's response
-#                 .then(fn=generate_tts_response, inputs=[chatbot, tts_choice], outputs=[audio_output], api_name="api_generate_tts_response")
-#                 .then(fn=clear_textbox, inputs=[], outputs=[chat_input], api_name="api_clear_textbox")
-#             )
+            retriever_sequence = (
+                retriever_button.click(fn=stop_audio, inputs=[], outputs=[audio_output], api_name="api_stop_audio_recording")
+                .then(fn=add_message, inputs=[chatbot, chat_input], outputs=[chatbot, chat_input], api_name="api_addprompt_chathistory")
+                # First, generate the bot response
+                .then(fn=generate_bot_response, inputs=[chatbot, choice, retrieval_mode, model_choice], outputs=[chatbot], api_name="api_generate_bot_response")
+                # Then, generate the TTS response based on the bot's response
+                .then(fn=generate_tts_response, inputs=[chatbot, tts_choice], outputs=[audio_output], api_name="api_generate_tts_response")
+                .then(fn=clear_textbox, inputs=[], outputs=[chat_input], api_name="api_clear_textbox")
+            )
 
 
 
@@ -1606,17 +1544,17 @@ def generate_bot_response_with_table(history, choice, retrieval_mode, model_choi
 
             
 
-#             chat_input.submit(fn=stop_audio, inputs=[], outputs=[audio_output], api_name="api_stop_audio_recording").then(
-#                 fn=add_message, inputs=[chatbot, chat_input], outputs=[chatbot, chat_input], api_name="api_addprompt_chathistory"
-#             ).then(
-#                 # First, generate the bot response
-#                 fn=generate_bot_response, inputs=[chatbot, choice, retrieval_mode, model_choice], outputs=[chatbot], api_name="api_generate_bot_response"
-#             ).then(
-#                 # Then, generate the TTS response based on the bot's response
-#                 fn=generate_tts_response, inputs=[chatbot, tts_choice], outputs=[audio_output], api_name="api_generate_tts_response"
-#             ).then(
-#                 fn=clear_textbox, inputs=[], outputs=[chat_input], api_name="api_clear_textbox"
-#             )
+            chat_input.submit(fn=stop_audio, inputs=[], outputs=[audio_output], api_name="api_stop_audio_recording").then(
+                fn=add_message, inputs=[chatbot, chat_input], outputs=[chatbot, chat_input], api_name="api_addprompt_chathistory"
+            ).then(
+                # First, generate the bot response
+                fn=generate_bot_response, inputs=[chatbot, choice, retrieval_mode, model_choice], outputs=[chatbot], api_name="api_generate_bot_response"
+            ).then(
+                # Then, generate the TTS response based on the bot's response
+                fn=generate_tts_response, inputs=[chatbot, tts_choice], outputs=[audio_output], api_name="api_generate_tts_response"
+            ).then(
+                fn=clear_textbox, inputs=[], outputs=[chat_input], api_name="api_clear_textbox"
+            )
 
 
 
@@ -1624,31 +1562,31 @@ def generate_bot_response_with_table(history, choice, retrieval_mode, model_choi
 
 
 
-#             audio_input = gr.Audio(sources=["microphone"], streaming=True, type='numpy', every=0.1)
-#             audio_input.stream(transcribe_function, inputs=[state, audio_input], outputs=[state, chat_input], api_name="api_voice_to_text")
+            audio_input = gr.Audio(sources=["microphone"], streaming=True, type='numpy', every=0.1)
+            audio_input.stream(transcribe_function, inputs=[state, audio_input], outputs=[state, chat_input], api_name="api_voice_to_text")
             
-#         #     gr.Markdown("<h1 style='color: red;'>Example Prompts</h1>", elem_id="Example-Prompts")
-#         #     gr.Examples(examples=examples, fn=insert_prompt,inputs=chat_input, outputs=chat_input)
+        #     gr.Markdown("<h1 style='color: red;'>Example Prompts</h1>", elem_id="Example-Prompts")
+        #     gr.Examples(examples=examples, fn=insert_prompt,inputs=chat_input, outputs=chat_input)
 
-#         # with gr.Column():
-#         #     weather_output = gr.HTML(value=fetch_local_weather())
-#         #     news_output = gr.HTML(value=fetch_local_news())
-#         #     events_output = gr.HTML(value=fetch_local_events())
+        # with gr.Column():
+        #     weather_output = gr.HTML(value=fetch_local_weather())
+        #     news_output = gr.HTML(value=fetch_local_news())
+        #     events_output = gr.HTML(value=fetch_local_events())
 
-#         # with gr.Column():
+        # with gr.Column():
            
           
-#         #     # Call update_images during the initial load to display images when the interface appears
-#         #     initial_images = update_images()
+        #     # Call update_images during the initial load to display images when the interface appears
+        #     initial_images = update_images()
             
-#         #     # Displaying the images generated using Flux API directly
-#         #     image_output_1 = gr.Image(value=initial_images[0], label="Image 1", elem_id="flux_image_1", width=400, height=400)
-#         #     image_output_2 = gr.Image(value=initial_images[1], label="Image 2", elem_id="flux_image_2", width=400, height=400)
-#         #     image_output_3 = gr.Image(value=initial_images[2], label="Image 3", elem_id="flux_image_3", width=400, height=400)
+        #     # Displaying the images generated using Flux API directly
+        #     image_output_1 = gr.Image(value=initial_images[0], label="Image 1", elem_id="flux_image_1", width=400, height=400)
+        #     image_output_2 = gr.Image(value=initial_images[1], label="Image 2", elem_id="flux_image_2", width=400, height=400)
+        #     image_output_3 = gr.Image(value=initial_images[2], label="Image 3", elem_id="flux_image_3", width=400, height=400)
     
-#         #     # Refresh button to update images
-#         #     refresh_button = gr.Button("Refresh Images")
-#         #     refresh_button.click(fn=update_images, inputs=None, outputs=[image_output_1, image_output_2, image_output_3])
+        #     # Refresh button to update images
+        #     refresh_button = gr.Button("Refresh Images")
+        #     refresh_button.click(fn=update_images, inputs=None, outputs=[image_output_1, image_output_2, image_output_3])
             
 
 
@@ -1656,62 +1594,11 @@ def generate_bot_response_with_table(history, choice, retrieval_mode, model_choi
 
            
 
-# demo.queue()
-# demo.launch(show_error=True)
-
-with gr.Blocks(theme='gradio/soft') as demo:
-
-    with gr.Row():
-        with gr.Column():
-            state = gr.State()
-
-            chatbot = gr.Chatbot([], elem_id="RADAR:Channel 94.1", bubble_full_width=False)
-            choice = gr.Radio(label="Select Style", choices=["Details", "Conversational"], value="Conversational",interactive=False,visible=False)
-            retrieval_mode = gr.Radio(label="Retrieval Mode", choices=["VDB", "KGF"], value="VDB",interactive=False,visible=False)
-            model_choice = gr.Dropdown(label="Choose Model", choices=["LM-2"], value="LM-2")
-
-            # Link the dropdown change to handle_model_choice_change
-            model_choice.change(fn=handle_model_choice_change, inputs=model_choice, outputs=[retrieval_mode, choice, choice])
-
-            chat_input = gr.Textbox(show_copy_button=True, interactive=True, show_label=False, label="ASK Radar !!!")
-            tts_choice = gr.Radio(label="Select TTS System", choices=["Alpha", "Beta"], value="Alpha")
-
-            retriever_button = gr.Button("Retriever")
-            clear_button = gr.Button("Clear")
-            clear_button.click(lambda: [None, None], outputs=[chat_input, state])
-
-            def stop_audio():
-                audio_output.stop()
-                return None
-
-            # HTML component for displaying the dynamic table
-            table_output = gr.HTML()
-
-            # Audio output for TTS
-            audio_output = gr.Audio(interactive=False, autoplay=True)
-
-            # Retriever sequence
-            retriever_sequence = (
-                retriever_button.click(fn=stop_audio, inputs=[], outputs=[audio_output], api_name="api_stop_audio_recording")
-                .then(fn=add_message, inputs=[chatbot, chat_input], outputs=[chatbot, chat_input], api_name="api_addprompt_chathistory")
-                .then(fn=generate_bot_response_with_table, inputs=[chatbot, choice, retrieval_mode, model_choice], outputs=[chatbot, table_output], api_name="api_generate_bot_response_with_table")
-                .then(fn=generate_tts_response, inputs=[chatbot, tts_choice], outputs=[audio_output], api_name="api_generate_tts_response")
-                .then(fn=clear_textbox, inputs=[], outputs=[chat_input], api_name="api_clear_textbox")
-            )
-
-            chat_input.submit(fn=stop_audio, inputs=[], outputs=[audio_output], api_name="api_stop_audio_recording").then(
-                fn=add_message, inputs=[chatbot, chat_input], outputs=[chatbot, chat_input], api_name="api_addprompt_chathistory"
-            ).then(
-                fn=generate_bot_response_with_table, inputs=[chatbot, choice, retrieval_mode, model_choice], outputs=[chatbot, table_output], api_name="api_generate_bot_response_with_table"
-            ).then(
-                fn=generate_tts_response, inputs=[chatbot, tts_choice], outputs=[audio_output], api_name="api_generate_tts_response"
-            ).then(
-                fn=clear_textbox, inputs=[], outputs=[chat_input], api_name="api_clear_textbox"
-            )
-
 demo.queue()
 demo.launch(show_error=True)
 
 
 
 
+
+