Update app.py

app.py

@@ -467,154 +467,154 @@ 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": 400,
-#                     "return_full_text": True,
-#                     "temperature": 0.7,
-#                     "do_sample": True,
-#                 })
-
-#                 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.", []
+            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": 400,
+                    "return_full_text": True,
+                    "temperature": 0.7,
+                    "do_sample": True,
+                })
+
+                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.", []
+    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)
+# 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
-        else:
-            return "No response generated.", []
+#         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
+#         else:
+#             return "No response generated.", []
 
 
 
@@ -631,58 +631,58 @@ def add_message(history, message):
 def print_like_dislike(x: gr.LikeData):
     print(x.index, x.value, x.liked)
 
-# def extract_addresses(response):
-#     if not isinstance(response, str):
-#         response = str(response)
-#     address_patterns = [
-#         r'([A-Z].*,\sBirmingham,\sAL\s\d{5})',
-#         r'(\d{4}\s.*,\sBirmingham,\sAL\s\d{5})',
-#         r'([A-Z].*,\sAL\s\d{5})',
-#         r'([A-Z].*,.*\sSt,\sBirmingham,\sAL\s\d{5})',
-#         r'([A-Z].*,.*\sStreets,\sBirmingham,\sAL\s\d{5})',
-#         r'(\d{2}.*\sStreets)',
-#         r'([A-Z].*\s\d{2},\sBirmingham,\sAL\s\d{5})',
-#         r'([a-zA-Z]\s Birmingham)',
-#         r'([a-zA-Z].*,\sBirmingham,\sAL)',
-#         r'(.*),(Birmingham, AL,USA)$'
-#         r'(^Birmingham,AL$)',
-#         r'((.*)(Stadium|Field),.*,\sAL$)',
-#         r'((.*)(Stadium|Field),.*,\sFL$)',
-#         r'((.*)(Stadium|Field),.*,\sMS$)',
-#         r'((.*)(Stadium|Field),.*,\sAR$)',
-#         r'((.*)(Stadium|Field),.*,\sKY$)',
-#         r'((.*)(Stadium|Field),.*,\sTN$)',
-#         r'((.*)(Stadium|Field),.*,\sLA$)',
-#         r'((.*)(Stadium|Field),.*,\sFL$)'
-
-#     ]
-#     addresses = []
-#     for pattern in address_patterns:
-#         addresses.extend(re.findall(pattern, response))
-#     return addresses
-
-# all_addresses = []
-
-# def generate_map(location_names):
-#     global all_addresses
-#     all_addresses.extend(location_names)
-
-#     api_key = os.environ['GOOGLEMAPS_API_KEY']
-#     gmaps = GoogleMapsClient(key=api_key)
-
-#     m = folium.Map(location=[33.5175, -86.809444], zoom_start=12)
-
-#     for location_name in all_addresses:
-#         geocode_result = gmaps.geocode(location_name)
-#         if geocode_result:
-#             location = geocode_result[0]['geometry']['location']
-#             folium.Marker(
-#                 [location['lat'], location['lng']],
-#                 tooltip=f"{geocode_result[0]['formatted_address']}"
-#             ).add_to(m)
-
-#     map_html = m._repr_html_()
-#     return map_html
+def extract_addresses(response):
+    if not isinstance(response, str):
+        response = str(response)
+    address_patterns = [
+        r'([A-Z].*,\sBirmingham,\sAL\s\d{5})',
+        r'(\d{4}\s.*,\sBirmingham,\sAL\s\d{5})',
+        r'([A-Z].*,\sAL\s\d{5})',
+        r'([A-Z].*,.*\sSt,\sBirmingham,\sAL\s\d{5})',
+        r'([A-Z].*,.*\sStreets,\sBirmingham,\sAL\s\d{5})',
+        r'(\d{2}.*\sStreets)',
+        r'([A-Z].*\s\d{2},\sBirmingham,\sAL\s\d{5})',
+        r'([a-zA-Z]\s Birmingham)',
+        r'([a-zA-Z].*,\sBirmingham,\sAL)',
+        r'(.*),(Birmingham, AL,USA)$'
+        r'(^Birmingham,AL$)',
+        r'((.*)(Stadium|Field),.*,\sAL$)',
+        r'((.*)(Stadium|Field),.*,\sFL$)',
+        r'((.*)(Stadium|Field),.*,\sMS$)',
+        r'((.*)(Stadium|Field),.*,\sAR$)',
+        r'((.*)(Stadium|Field),.*,\sKY$)',
+        r'((.*)(Stadium|Field),.*,\sTN$)',
+        r'((.*)(Stadium|Field),.*,\sLA$)',
+        r'((.*)(Stadium|Field),.*,\sFL$)'
+
+    ]
+    addresses = []
+    for pattern in address_patterns:
+        addresses.extend(re.findall(pattern, response))
+    return addresses
+
+all_addresses = []
+
+def generate_map(location_names):
+    global all_addresses
+    all_addresses.extend(location_names)
+
+    api_key = os.environ['GOOGLEMAPS_API_KEY']
+    gmaps = GoogleMapsClient(key=api_key)
+
+    m = folium.Map(location=[33.5175, -86.809444], zoom_start=12)
+
+    for location_name in all_addresses:
+        geocode_result = gmaps.geocode(location_name)
+        if geocode_result:
+            location = geocode_result[0]['geometry']['location']
+            folium.Marker(
+                [location['lat'], location['lng']],
+                tooltip=f"{geocode_result[0]['formatted_address']}"
+            ).add_to(m)
+
+    map_html = m._repr_html_()
+    return map_html
 
 from diffusers import DiffusionPipeline
 import torch
@@ -1100,32 +1100,32 @@ def handle_retrieval_mode_change(choice):
 
 
 
-# def handle_model_choice_change(selected_model):
-#     if selected_model == "LM-2":
-#         # Disable retrieval mode and select style when LM-2 is selected
-#         return gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=False)
-#     elif selected_model == "LM-1":
-#         # Enable retrieval mode and select style for LM-1
-#         return gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)
-#     else:
-#         # Default case: allow interaction
-#         return gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)
-
 def handle_model_choice_change(selected_model):
-    if selected_model == "LM-2":  # When LM-2 (Phi-3.5) is selected
+    if selected_model == "LM-2":
         # Disable retrieval mode and select style when LM-2 is selected
-        return (
-            gr.update(interactive=False),  # Disable retrieval mode
-            gr.update(interactive=False),  # Disable style (Details/Conversational)
-            gr.update(interactive=False)   # Disable the model choice itself
-        )
+        return gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=False)
+    elif selected_model == "LM-1":
+        # Enable retrieval mode and select style for LM-1
+        return gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)
     else:
-        # Disable GPT-4o, GPT-4o-mini, and KGF, only Phi-3.5 works
-        return (
-            gr.update(interactive=True),  # Allow retrieval mode for other models
-            gr.update(interactive=True),  # Allow style options for other models
-            gr.update(interactive=True)   # Allow other models to be selected
-        )
+        # Default case: allow interaction
+        return gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)
+
+# def handle_model_choice_change(selected_model):
+#     if selected_model == "LM-2":  # When LM-2 (Phi-3.5) is selected
+#         # Disable retrieval mode and select style when LM-2 is selected
+#         return (
+#             gr.update(interactive=False),  # Disable retrieval mode
+#             gr.update(interactive=False),  # Disable style (Details/Conversational)
+#             gr.update(interactive=False)   # Disable the model choice itself
+#         )
+#     else:
+#         # Disable GPT-4o, GPT-4o-mini, and KGF, only Phi-3.5 works
+#         return (
+#             gr.update(interactive=True),  # Allow retrieval mode for other models
+#             gr.update(interactive=True),  # Allow style options for other models
+#             gr.update(interactive=True)   # Allow other models to be selected
+#         )
 
 #Flux Coding