Update app.py

app.py

@@ -341,48 +341,54 @@ chain_neo4j = (
 def generate_answer(message, choice, retrieval_mode, selected_model):
     logging.debug(f"generate_answer called with choice: {choice} and retrieval_mode: {retrieval_mode}")
 
-    if "hotel" in message.lower() or "hotels" in message.lower() and "birmingham" in message.lower():
-        response = fetch_google_hotels()
-        return response, extract_addresses(response)
-    
-    if "restaurant" in message.lower() or "restaurants" in message.lower() and "birmingham" in message.lower():
-        response = fetch_yelp_restaurants()
-        return response, extract_addresses(response)
-
-    if "flight" in message.lower() or "flights" in message.lower() and "birmingham" in message.lower():
-        response = fetch_google_flights()
-        return response, extract_addresses(response)
-
-    prompt_template = QA_CHAIN_PROMPT_1 if choice == "Details" else QA_CHAIN_PROMPT_2
-
-    if retrieval_mode == "VDB":
-        if selected_model == "GPT-4o":
-            # Use Langchain with GPT-4o
-            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"Vector response: {response}")
-            return response['result'], extract_addresses(response['result'])
-        elif selected_model == "Phi-3.5":
-            # Directly use the Phi-3.5 model for text generation
-            response = selected_model(message, **{
-                "max_new_tokens": 500,
-                "return_full_text": False,
-                "temperature": 0.0,
-                "do_sample": False,
-            })[0]['generated_text']
-            logging.debug(f"Phi-3.5 response: {response}")
+    try:
+        # Handle hotel-related queries
+        if "hotel" in message.lower() or "hotels" in message.lower() and "birmingham" in message.lower():
+            response = fetch_google_hotels()
             return response, extract_addresses(response)
-    elif retrieval_mode == "KGF":
-        response = chain_neo4j.invoke({"question": message})
-        logging.debug(f"Knowledge-Graph response: {response}")
-        return response, extract_addresses(response)
-    else:
-        return "Invalid retrieval mode selected.", []
+        
+        # Handle restaurant-related queries
+        if "restaurant" in message.lower() or "restaurants" in message.lower() and "birmingham" in message.lower():
+            response = fetch_yelp_restaurants()
+            return response, extract_addresses(response)
+
+        # Handle flight-related queries
+        if "flight" in message.lower() or "flights" in message.lower() and "birmingham" in message.lower():
+            response = fetch_google_flights()
+            return response, extract_addresses(response)
+
+        prompt_template = QA_CHAIN_PROMPT_1 if choice == "Details" else QA_CHAIN_PROMPT_2
+
+        if retrieval_mode == "VDB":
+            if selected_model == chat_model:
+                # Use Langchain with GPT-4o
+                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})
+                return response['result'], extract_addresses(response['result'])
+            elif selected_model == phi_pipe:
+                # Directly use the Phi-3.5 model for text generation
+                response = selected_model(message, **{
+                    "max_new_tokens": 500,
+                    "return_full_text": False,
+                    "temperature": 0.0,
+                    "do_sample": False,
+                })[0]['generated_text']
+                return response, extract_addresses(response)
+        elif retrieval_mode == "KGF":
+            response = chain_neo4j.invoke({"question": message})
+            return response, extract_addresses(response)
+        else:
+            return "Invalid retrieval mode selected.", []
+
+    except Exception as e:
+        logging.error(f"Error in generate_answer: {e}")
+        return "Sorry, I encountered an error while processing your request.", []
+
 
 
 
@@ -417,10 +423,8 @@ def bot(history, choice, tts_choice, retrieval_mode, model_choice):
     if not history:
         return history
     
-    if model_choice == "GPT-4o":
-        selected_model = gpt_model
-    elif model_choice == "Phi-3.5":
-        selected_model = phi_pipe
+    # Select the model
+    selected_model = chat_model if model_choice == "GPT-4o" else phi_pipe
     
     response, addresses = generate_answer(history[-1][0], choice, retrieval_mode, selected_model)
     history[-1][1] = ""