Update app.py

app.py

@@ -400,27 +400,39 @@ Sure! The Responses are as follows:
 
 
 
-def generate_bot_response(history, choice, retrieval_mode, model_choice):
-    if not history:
-        return
+# def generate_bot_response(history, choice, retrieval_mode, model_choice):
+#     if not history:
+#         return
+
+#     # Select the model
+#     # selected_model = chat_model if model_choice == "LM-1" else phi_pipe
+#     selected_model = chat_model if model_choice == "LM-1" else (chat_model1 if model_choice == "LM-3" else phi_pipe)
+
 
-    # Select the model
-    # selected_model = chat_model if model_choice == "LM-1" else phi_pipe
-    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)
+#     history[-1][1] = ""
 
+#     for character in response:
+#         history[-1][1] += character
+#         yield history  # Stream each character as it is generated
+#         time.sleep(0.05)  # Add a slight delay to simulate streaming
 
-    response, addresses = generate_answer(history[-1][0], choice, retrieval_mode, selected_model)
-    history[-1][1] = ""
+#     yield history  # Final yield with the complete response
 
-    for character in response:
-        history[-1][1] += character
-        yield history  # Stream each character as it is generated
-        time.sleep(0.05)  # Add a slight delay to simulate streaming
 
-    yield history  # Final yield with the complete response
+# Modified chatbot response function to also return the confidence plot
+def generate_bot_response(chatbot, choice, retrieval_mode, model_choice):
+    # Your chatbot logic to generate the response
+    response = f"This is a sample response based on your query."
 
+    # Get the confidence level (you can customize this for your actual confidence calculation)
+    confidence_level = get_confidence_level(response)
 
+    # Plot the confidence as a bar graph
+    confidence_plot = plot_confidence_bar(confidence_level)
 
+    # Return both the chatbot response and the confidence plot
+    return chatbot + [[None, response]], confidence_plot
 
 
 
@@ -1505,66 +1517,193 @@ def fetch_google_flights(departure_id="JFK", arrival_id="BHM", outbound_date=cur
 #     return prompt[0] if prompt else current_text
 
 
+#confidence Level 
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Function to calculate the confidence level (you can modify this to use real confidence scores)
+def get_confidence_level(response):
+    # Simulate a confidence score between 0 and 1 for demonstration purposes
+    confidence_level = np.random.rand()
+    return confidence_level
+
+# Function to plot confidence score as a bar graph
+def plot_confidence_bar(confidence_level):
+    fig, ax = plt.subplots()
+
+    categories = ['Confidence Level']
+    values = [confidence_level]
+
+    # Create a bar chart
+    ax.bar(categories, values, color='blue')
+    ax.set_ylim(0, 1)  # Confidence level is between 0 and 1
+    ax.set_title('Chatbot Confidence Level')
+    ax.set_ylabel('Confidence Score')
+
+    # Save the plot as an image
+    temp_image_path = "confidence_plot.png"
+    plt.savefig(temp_image_path)
+    plt.close(fig)
+
+    return temp_image_path
+
 
-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()
+
+#             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])
+
+#             # 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")
+        
+#             retriever_button = gr.Button("Retriever")
+
+#             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)
+
+#             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")
+#             )
+
+
+
+
+
+
+            
+
+#             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"
+#             )
+
+
+
+
+
+
+
+#             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)
+
+#         # 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():
+           
+          
+#         #     # 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)
+    
+#         #     # 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])
+            
+
+
+
+
+           
+
+# demo.queue()
+# demo.launch(show_error=True)
+
+
+
+# Rest of your Gradio code
+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)
+            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])
 
-            # 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")
-        
+            
             retriever_button = gr.Button("Retriever")
-
             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)
 
             def stop_audio():
                 audio_output.stop()
                 return None
 
-               
-    
-           
+            # Add new output for the confidence bar graph
+            confidence_plot_output = gr.Image(label="Confidence Level Bar Graph")
 
             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")
+                # First, generate the bot response and confidence score
+                .then(fn=generate_bot_response, inputs=[chatbot, choice, retrieval_mode, model_choice], outputs=[chatbot, confidence_plot_output], 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")
             )
 
-
-
-
-
-
-            
-
             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"
+                # First, generate the bot response and confidence score
+                fn=generate_bot_response, inputs=[chatbot, choice, retrieval_mode, model_choice], outputs=[chatbot, confidence_plot_output], 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"
@@ -1572,43 +1711,8 @@ with gr.Blocks(theme='gradio/soft') as demo:
                 fn=clear_textbox, inputs=[], outputs=[chat_input], api_name="api_clear_textbox"
             )
 
-
-
-
-
-
-
             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)
-
-        # 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():
-           
-          
-        #     # 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)
-    
-        #     # 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])
-            
-
-
-
-
-           
 
 demo.queue()
 demo.launch(show_error=True)