Update app.py

app.py

@@ -358,65 +358,35 @@ Sure! Here's the information you requested:
 """
 
 
-# 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)
-
-
-#     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
-
-#     yield history  # Final yield with the complete response
-
-# Function to generate bot response
-def bot_response(history, choice, retrieval_mode, model_choice):
+def generate_bot_response(history, choice, retrieval_mode, model_choice):
     if not history:
-        return history, None
-    
+        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)
-    
-    # Generate bot's response
+
+
     response, addresses = generate_answer(history[-1][0], choice, retrieval_mode, selected_model)
-    
-    # Clear current message in history
     history[-1][1] = ""
 
-    # Return the generated response and updated history
-    return history, 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
 
 
 
 
 
 
-# def generate_tts_response(response, tts_choice):
-#     with concurrent.futures.ThreadPoolExecutor() as executor:
-#         if tts_choice == "Alpha":
-#             audio_future = executor.submit(generate_audio_elevenlabs, response)
-#         elif tts_choice == "Beta":
-#             audio_future = executor.submit(generate_audio_parler_tts, response)
-#         # elif tts_choice == "Gamma":
-#         #     audio_future = executor.submit(generate_audio_mars5, response)
-
-#         audio_path = audio_future.result()
-#         return audio_path
 
 
-# Function to generate TTS response
-def tts_response(history, response, tts_choice):
+
+def generate_tts_response(response, tts_choice):
     with concurrent.futures.ThreadPoolExecutor() as executor:
-        # Based on the TTS choice, submit the corresponding task
         if tts_choice == "Alpha":
             audio_future = executor.submit(generate_audio_elevenlabs, response)
         elif tts_choice == "Beta":
@@ -424,88 +394,49 @@ def tts_response(history, response, tts_choice):
         # elif tts_choice == "Gamma":
         #     audio_future = executor.submit(generate_audio_mars5, response)
 
-        # Simulate typing effect by adding characters with delay
-        for character in response:
-            history[-1][1] += character
-            time.sleep(0.05)
-            yield history, None
-        
-        # Get the path of the generated audio
         audio_path = audio_future.result()
-
-        # Return the final history and audio path
-        yield history, audio_path
-
-
+        return audio_path
 
 
 
-# import concurrent.futures
-# # Existing bot function with concurrent futures for parallel processing
-# def bot(history, choice, tts_choice, retrieval_mode, model_choice):
-#     # Initialize an empty response
-#     response = ""
 
-#     # Create a thread pool to handle both text generation and TTS conversion in parallel
-#     with concurrent.futures.ThreadPoolExecutor() as executor:
-#         # Start the bot response generation in parallel
-#         bot_future = executor.submit(generate_bot_response, history, choice, retrieval_mode, model_choice)
-
-#         # Wait for the text generation to start
-#         for history_chunk in bot_future.result():
-#             response = history_chunk[-1][1]  # Update the response with the current state
-#             yield history_chunk, None  # Stream the text output as it's generated
 
-#         # Once text is fully generated, start the TTS conversion
-#         tts_future = executor.submit(generate_tts_response, response, tts_choice)
 
-#         # Get the audio output after TTS is done
-#         audio_path = tts_future.result()
 
-#         # Stream the final text and audio output
-#         yield history, audio_path
 
-
-# Full process combining both bot response and TTS
+import concurrent.futures
+# Existing bot function with concurrent futures for parallel processing
 def bot(history, choice, tts_choice, retrieval_mode, model_choice):
-    # Generate bot response
-    history, response = bot_response(history, choice, retrieval_mode, model_choice)
-    
-    # Start TTS process if response exists
-    if response:
-        tts_gen = tts_response(history, response, tts_choice)
+    # Initialize an empty response
+    response = ""
 
-        # Process the TTS generator and yield history and audio
-        for updated_history, audio_path in tts_gen:
-            yield updated_history, audio_path
-    else:
-        yield history, None
+    # Create a thread pool to handle both text generation and TTS conversion in parallel
+    with concurrent.futures.ThreadPoolExecutor() as executor:
+        # Start the bot response generation in parallel
+        bot_future = executor.submit(generate_bot_response, history, choice, retrieval_mode, model_choice)
+
+        # Wait for the text generation to start
+        for history_chunk in bot_future.result():
+            response = history_chunk[-1][1]  # Update the response with the current state
+            yield history_chunk, None  # Stream the text output as it's generated
 
+        # Once text is fully generated, start the TTS conversion
+        tts_future = executor.submit(generate_tts_response, response, tts_choice)
 
+        # Get the audio output after TTS is done
+        audio_path = tts_future.result()
 
+        # Stream the final text and audio output
+        yield history, audio_path
 
 
 
-# # Modified bot function to separate chatbot response and TTS generation
 
-# 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)
 
 
-#     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
 
-#     yield history  # Final yield with the complete response
 
 
 
@@ -1475,9 +1406,9 @@ with gr.Blocks(theme='Pijush2023/scikit-learn-pijush') as demo:
                 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=bot_response, inputs=[chatbot, choice, retrieval_mode, model_choice], outputs=[chatbot], api_name="api_generate_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=tts_response, inputs=[chatbot, tts_choice], outputs=[audio_output], api_name="api_generate_tts_response")
+                .then(fn=generate_tts_response, inputs=[chatbot, tts_choice], outputs=[audio_output], api_name="api_generate_tts_response")
                 .then(fn=show_map_if_details, inputs=[chatbot, choice], outputs=[location_output, location_output], api_name="api_show_map_details")
                 .then(fn=clear_textbox, inputs=[], outputs=[chat_input], api_name="api_clear_textbox")
             )
@@ -1493,10 +1424,10 @@ with gr.Blocks(theme='Pijush2023/scikit-learn-pijush') as demo:
                 fn=add_message, inputs=[chatbot, chat_input], outputs=[chatbot, chat_input], api_name="api_addprompt_chathistory"
             ).then(
                 # First, generate the bot response
-                fn=bot_response, inputs=[chatbot, choice, retrieval_mode, model_choice], outputs=[chatbot], api_name="api_generate_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=tts_response, inputs=[chatbot, tts_choice], outputs=[audio_output], api_name="api_generate_tts_response"
+                fn=generate_tts_response, inputs=[chatbot, tts_choice], outputs=[audio_output], api_name="api_generate_tts_response"
             ).then(
                 fn=show_map_if_details, inputs=[chatbot, choice], outputs=[location_output, location_output], api_name="api_show_map_details"
             ).then(