Update app.py

app.py

@@ -367,11 +367,31 @@ QA_CHAIN_PROMPT_2 = PromptTemplate(input_variables=["context", "question"], temp
 # Sure! Here's the information:
 # """
 
+# phi_custom_template = """
+# <|system|>
+# Sei un esperto di madrelingua italiana. Il tuo compito è fornire risposte precise, accurate, concise, nitide e brevi basate sul documento fornito. Dovresti restituire le informazioni nel seguente formato:
+
+# - Nome del documento: (il nome del documento)
+# - Numero di pagina: (numero di pagina)
+# - Contenuto effettivo: (contenuto pertinente del documento)
+
+# Se non riesci a trovare la risposta nel documento, rispondi semplicemente con "Questa domanda va oltre la mia conoscenza".
+# <|end|>
+
+# <|user|>
+# {context}
+# Question: {question}<|end|>
+
+# <|assistant|>
+# Sure! The Responses are as follows:
+# """
+
+# Modify the Phi-3.5 template to include the selected file
 phi_custom_template = """
 <|system|>
 Sei un esperto di madrelingua italiana. Il tuo compito è fornire risposte precise, accurate, concise, nitide e brevi basate sul documento fornito. Dovresti restituire le informazioni nel seguente formato:
 
-- Nome del documento: (il nome del documento)
+- Nome del documento: {document_name}
 - Numero di pagina: (numero di pagina)
 - Contenuto effettivo: (contenuto pertinente del documento)
 
@@ -573,154 +593,155 @@ Detailed Answer:
 
 import traceback
 
-def generate_answer(message, choice, retrieval_mode, selected_model):
-    logging.debug(f"generate_answer called with choice: {choice}, retrieval_mode: {retrieval_mode}, and selected_model: {selected_model}")
-
-    # Logic for disabling options for Phi-3.5
-    if selected_model == "LM-2":
-        choice = None
-        retrieval_mode = None
-
-    try:
-        # Select the appropriate template based on the choice and model
-        if choice == "Details" and selected_model == chat_model1:  # GPT-4o-mini
-            prompt_template = PromptTemplate(input_variables=["context", "question"], template=gpt4o_mini_template_details)
-        elif choice == "Details":
-            prompt_template = QA_CHAIN_PROMPT_1
-        elif choice == "Conversational":
-            prompt_template = QA_CHAIN_PROMPT_2
-        else:
-            prompt_template = QA_CHAIN_PROMPT_1  # Fallback to template1
-
-        # # Handle hotel-related queries
-        # if "hotel" in message.lower() or "hotels" in message.lower() and "birmingham" in message.lower():
-        #     logging.debug("Handling hotel-related query")
-        #     response = fetch_google_hotels()
-        #     logging.debug(f"Hotel response: {response}")
-        #     return response, extract_addresses(response)
-
-        # # Handle restaurant-related queries
-        # if "restaurant" in message.lower() or "restaurants" in message.lower() and "birmingham" in message.lower():
-        #     logging.debug("Handling restaurant-related query")
-        #     response = fetch_yelp_restaurants()
-        #     logging.debug(f"Restaurant response: {response}")
-        #     return response, extract_addresses(response)
-
-        # # Handle flight-related queries
-        # if "flight" in message.lower() or "flights" in message.lower() and "birmingham" in message.lower():
-        #     logging.debug("Handling flight-related query")
-        #     response = fetch_google_flights()
-        #     logging.debug(f"Flight response: {response}")
-        #     return response, extract_addresses(response)
-
-        # Retrieval-based response
-        if retrieval_mode == "VDB":
-            logging.debug("Using VDB retrieval mode")
-            if selected_model == chat_model:
-                logging.debug("Selected model: LM-1")
-                retriever = gpt_retriever
-                context = retriever.get_relevant_documents(message)
-                logging.debug(f"Retrieved context: {context}")
-
-                prompt = prompt_template.format(context=context, question=message)
-                logging.debug(f"Generated prompt: {prompt}")
-
-                qa_chain = RetrievalQA.from_chain_type(
-                    llm=chat_model,
-                    chain_type="stuff",
-                    retriever=retriever,
-                    chain_type_kwargs={"prompt": prompt_template}
-                )
-                response = qa_chain({"query": message})
-                logging.debug(f"LM-1 response: {response}")
-                return response['result'], extract_addresses(response['result'])
+# def generate_answer(message, choice, retrieval_mode, selected_model):
+#     logging.debug(f"generate_answer called with choice: {choice}, retrieval_mode: {retrieval_mode}, and selected_model: {selected_model}")
+
+#     # Logic for disabling options for Phi-3.5
+#     if selected_model == "LM-2":
+#         choice = None
+#         retrieval_mode = None
+
+#     try:
+#         # Select the appropriate template based on the choice and model
+#         if choice == "Details" and selected_model == chat_model1:  # GPT-4o-mini
+#             prompt_template = PromptTemplate(input_variables=["context", "question"], template=gpt4o_mini_template_details)
+#         elif choice == "Details":
+#             prompt_template = QA_CHAIN_PROMPT_1
+#         elif choice == "Conversational":
+#             prompt_template = QA_CHAIN_PROMPT_2
+#         else:
+#             prompt_template = QA_CHAIN_PROMPT_1  # Fallback to template1
+
+#         # # Handle hotel-related queries
+#         # if "hotel" in message.lower() or "hotels" in message.lower() and "birmingham" in message.lower():
+#         #     logging.debug("Handling hotel-related query")
+#         #     response = fetch_google_hotels()
+#         #     logging.debug(f"Hotel response: {response}")
+#         #     return response, extract_addresses(response)
+
+#         # # Handle restaurant-related queries
+#         # if "restaurant" in message.lower() or "restaurants" in message.lower() and "birmingham" in message.lower():
+#         #     logging.debug("Handling restaurant-related query")
+#         #     response = fetch_yelp_restaurants()
+#         #     logging.debug(f"Restaurant response: {response}")
+#         #     return response, extract_addresses(response)
+
+#         # # Handle flight-related queries
+#         # if "flight" in message.lower() or "flights" in message.lower() and "birmingham" in message.lower():
+#         #     logging.debug("Handling flight-related query")
+#         #     response = fetch_google_flights()
+#         #     logging.debug(f"Flight response: {response}")
+#         #     return response, extract_addresses(response)
+
+#         # Retrieval-based response
+#         if retrieval_mode == "VDB":
+#             logging.debug("Using VDB retrieval mode")
+#             if selected_model == chat_model:
+#                 logging.debug("Selected model: LM-1")
+#                 retriever = gpt_retriever
+#                 context = retriever.get_relevant_documents(message)
+#                 logging.debug(f"Retrieved context: {context}")
+
+#                 prompt = prompt_template.format(context=context, question=message)
+#                 logging.debug(f"Generated prompt: {prompt}")
+
+#                 qa_chain = RetrievalQA.from_chain_type(
+#                     llm=chat_model,
+#                     chain_type="stuff",
+#                     retriever=retriever,
+#                     chain_type_kwargs={"prompt": prompt_template}
+#                 )
+#                 response = qa_chain({"query": message})
+#                 logging.debug(f"LM-1 response: {response}")
+#                 return response['result'], extract_addresses(response['result'])
             
-            elif selected_model == chat_model1:
-                logging.debug("Selected model: LM-3")
-                retriever = gpt_retriever
-                context = retriever.get_relevant_documents(message)
-                logging.debug(f"Retrieved context: {context}")
-
-                prompt = prompt_template.format(context=context, question=message)
-                logging.debug(f"Generated prompt: {prompt}")
-
-                qa_chain = RetrievalQA.from_chain_type(
-                    llm=chat_model1,
-                    chain_type="stuff",
-                    retriever=retriever,
-                    chain_type_kwargs={"prompt": prompt_template}
-                )
-                response = qa_chain({"query": message})
-                logging.debug(f"LM-3 response: {response}")
-                return response['result'], extract_addresses(response['result']) 
-
+#             elif selected_model == chat_model1:
+#                 logging.debug("Selected model: LM-3")
+#                 retriever = gpt_retriever
+#                 context = retriever.get_relevant_documents(message)
+#                 logging.debug(f"Retrieved context: {context}")
+
+#                 prompt = prompt_template.format(context=context, question=message)
+#                 logging.debug(f"Generated prompt: {prompt}")
+
+#                 qa_chain = RetrievalQA.from_chain_type(
+#                     llm=chat_model1,
+#                     chain_type="stuff",
+#                     retriever=retriever,
+#                     chain_type_kwargs={"prompt": prompt_template}
+#                 )
+#                 response = qa_chain({"query": message})
+#                 logging.debug(f"LM-3 response: {response}")
+#                 return response['result'], extract_addresses(response['result']) 
 
             
-            elif selected_model == phi_pipe:
-                logging.debug("Selected model: LM-2")
-                retriever = phi_retriever
-                context_documents = retriever.get_relevant_documents(message)
-                context = "\n".join([doc.page_content for doc in context_documents])
-                logging.debug(f"Retrieved context for LM-2: {context}")
-
-                # Use the correct template variable
-                prompt = phi_custom_template.format(context=context, question=message)
-                logging.debug(f"Generated LM-2 prompt: {prompt}")
-
-                response = selected_model(prompt, **{
-                    "max_new_tokens": 250,
-                    "return_full_text": True,
-                    "temperature": 0.0,
-                    "do_sample": False,
-                })
-
-                if response:
-                    generated_text = response[0]['generated_text']
-                    logging.debug(f"LM-2 Response: {generated_text}")
-                    cleaned_response = clean_response(generated_text)
-                    return cleaned_response, extract_addresses(cleaned_response)
-                else:
-                    logging.error("LM-2 did not return any response.")
-                    return "No response generated.", []
-
-        elif retrieval_mode == "KGF":
-            logging.debug("Using KGF retrieval mode")
-            response = chain_neo4j.invoke({"question": message})
-            logging.debug(f"KGF response: {response}")
-            return response, extract_addresses(response)
-        else:
-            logging.error("Invalid retrieval mode selected.")
-            return "Invalid retrieval mode selected.", []
-
-    except Exception as e:
-        logging.error(f"Error in generate_answer: {str(e)}")
-        logging.error(traceback.format_exc())
-        return "Sorry, I encountered an error while processing your request.", []
-
-# def generate_answer(message, choice, retrieval_mode, selected_model):
-#     # Logic for Phi-3.5
-#     if selected_model == phi_pipe:  # LM-2 Phi-3.5 selected
-#         retriever = phi_retriever
-#         context_documents = retriever.get_relevant_documents(message)
-#         context = "\n".join([doc.page_content for doc in context_documents])
-
-#         # Use the correct template for Phi-3.5
-#         prompt = phi_custom_template.format(context=context, question=message)
-        
-#         response = selected_model(prompt, **{
-#             "max_new_tokens": 400,
-#             "return_full_text": True,
-#             "temperature": 0.7,
-#             "do_sample": True,
-#         })
-
-#         if response:
-#             generated_text = response[0]['generated_text']
-#             cleaned_response = clean_response(generated_text)
-#             # return cleaned_response, extract_addresses(cleaned_response)
-#             return cleaned_response
+            
+#             elif selected_model == phi_pipe:
+#                 logging.debug("Selected model: LM-2")
+#                 retriever = phi_retriever
+#                 context_documents = retriever.get_relevant_documents(message)
+#                 context = "\n".join([doc.page_content for doc in context_documents])
+#                 logging.debug(f"Retrieved context for LM-2: {context}")
+
+#                 # Use the correct template variable
+#                 prompt = phi_custom_template.format(context=context, question=message)
+#                 logging.debug(f"Generated LM-2 prompt: {prompt}")
+
+#                 response = selected_model(prompt, **{
+#                     "max_new_tokens": 250,
+#                     "return_full_text": True,
+#                     "temperature": 0.0,
+#                     "do_sample": False,
+#                 })
+
+#                 if response:
+#                     generated_text = response[0]['generated_text']
+#                     logging.debug(f"LM-2 Response: {generated_text}")
+#                     cleaned_response = clean_response(generated_text)
+#                     return cleaned_response, extract_addresses(cleaned_response)
+#                 else:
+#                     logging.error("LM-2 did not return any response.")
+#                     return "No response generated.", []
+
+#         elif retrieval_mode == "KGF":
+#             logging.debug("Using KGF retrieval mode")
+#             response = chain_neo4j.invoke({"question": message})
+#             logging.debug(f"KGF response: {response}")
+#             return response, extract_addresses(response)
 #         else:
-#             return "No response generated.", []
+#             logging.error("Invalid retrieval mode selected.")
+#             return "Invalid retrieval mode selected.", []
+
+#     except Exception as e:
+#         logging.error(f"Error in generate_answer: {str(e)}")
+#         logging.error(traceback.format_exc())
+#         return "Sorry, I encountered an error while processing your request.", []
+
+def generate_answer(message, choice, retrieval_mode, selected_model, selected_file):
+    # Ensure a file is selected
+    if not selected_file:
+        return "Please choose a file to proceed."
+
+    # Modify the Phi-3.5 prompt to include the selected file
+    if selected_model == phi_pipe:
+        retriever = phi_retriever
+        context_documents = retriever.get_relevant_documents(message)
+        context = "\n".join([doc.page_content for doc in context_documents])
+
+        prompt = phi_custom_template.format(context=context, question=message, document_name=selected_file)
+        response = selected_model(prompt, **{
+            "max_new_tokens": 250,
+            "return_full_text": True,
+            "temperature": 0.0,
+            "do_sample": False,
+        })
+
+        if response:
+            generated_text = response[0]['generated_text']
+            cleaned_response = clean_response(generated_text)
+            return cleaned_response
+        else:
+            return "No response generated.", []
 
 
 
@@ -1480,58 +1501,67 @@ def fetch_google_flights(departure_id="JFK", arrival_id="BHM", outbound_date=cur
 # # Function to insert the prompt into the textbox when clicked
 # def insert_prompt(current_text, prompt):
 #     return prompt[0] if prompt else current_text
+# List of files for dropdown
 
+files_list = [
+    "176769_000219051794_V2.pdf",
+    "177115_000230633979_V2.pdf",
+    "177273_000219963851_V2.pdf",
+    "177429_000219726050_V2.pdf",
+    "177495_000219962533_V2.pdf",
+    "178041_000197000852_V2.pdf"
+]
 
 
 
 
 
-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()
 
-            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")
+#             )
 
 
 
@@ -1540,17 +1570,17 @@ with gr.Blocks(theme='gradio/soft') as demo:
 
             
 
-            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"
+#             )
 
 
 
@@ -1558,31 +1588,31 @@ with gr.Blocks(theme='gradio/soft') as demo:
 
 
 
-            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])
             
 
 
@@ -1590,8 +1620,59 @@ with gr.Blocks(theme='gradio/soft') as demo:
 
            
 
-demo.queue()
-demo.launch(show_error=True)
+# demo.queue()
+# demo.launch(show_error=True)
+
+
+with gr.Blocks(theme='gradio/soft') as demo:
+    with gr.Row():
+        with gr.Column():
+            state = gr.State()
+
+            # 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")
+
+            # 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])
+
+            audio_output = gr.Audio(interactive=False, autoplay=True)
+
+            def stop_audio():
+                audio_output.stop()
+                return None
+
+            # Modify retriever sequence to include file selection
+            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")
+                # Pass the selected file to the bot response generation
+                .then(fn=generate_answer_with_file, inputs=[chatbot, choice, retrieval_mode, model_choice, file_dropdown], outputs=[chatbot], api_name="api_generate_bot_response_with_file")
+                .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_answer_with_file, inputs=[chatbot, choice, retrieval_mode, model_choice, file_dropdown], outputs=[chatbot], api_name="api_generate_bot_response_with_file"
+            ).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.launch()