Update app.py

app.py

@@ -304,41 +304,79 @@ chain_neo4j = (
 
 
 
+# def generate_answer(message, choice, retrieval_mode):
+#     logging.debug(f"generate_answer called with choice: {choice} and retrieval_mode: {retrieval_mode}")
+
+#     # Check if the question is about hotels
+#     if "hotel" in message.lower() and "birmingham" in message.lower():
+#         response = fetch_google_hotels()
+#         return response, extract_addresses(response)
+    
+#     # Check if the question is about restaurants
+#     if "restaurant" in message.lower() and "birmingham" in message.lower():
+#         response = fetch_yelp_restaurants()
+#         return response, extract_addresses(response)
+#     # Check if the question is about flights
+#     if "flight" in message.lower() and ("JFK" in message or "BHM" in message):
+#         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":
+#         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 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.", []
+
 def generate_answer(message, choice, retrieval_mode):
     logging.debug(f"generate_answer called with choice: {choice} and retrieval_mode: {retrieval_mode}")
 
-    # Check if the question is about hotels
+    intro = ""
+    response = ""
     if "hotel" in message.lower() and "birmingham" in message.lower():
+        intro = "Here are the top Hotels in Birmingham:\n"
         response = fetch_google_hotels()
-        return response, extract_addresses(response)
-    
-    # Check if the question is about restaurants
-    if "restaurant" in message.lower() and "birmingham" in message.lower():
+    elif "restaurant" in message.lower() and "birmingham" in message.lower():
+        intro = "Here are the top Restaurants in Birmingham:\n"
         response = fetch_yelp_restaurants()
-        return response, extract_addresses(response)
-    # Check if the question is about flights
-    if "flight" in message.lower() and ("JFK" in message or "BHM" in message):
+    elif "flight" in message.lower() and ("JFK" in message or "BHM" in message):
+        intro = "Here are some available flights for today:\n"
         response = fetch_google_flights()
-        return response, extract_addresses(response)
+    else:
+        prompt_template = QA_CHAIN_PROMPT_1 if choice == "Details" else QA_CHAIN_PROMPT_2
+        if retrieval_mode == "VDB":
+            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}")
+            response = response['result']
+        elif retrieval_mode == "KGF":
+            response = chain_neo4j.invoke({"question": message})
+            logging.debug(f"Knowledge-Graph response: {response}")
+        else:
+            response = "Invalid retrieval mode selected."
 
-    prompt_template = QA_CHAIN_PROMPT_1 if choice == "Details" else QA_CHAIN_PROMPT_2
+    # Assign numbers to each item in the response
+    response_lines = response.splitlines()
+    numbered_response = "\n".join([f"{i+1}. {line}" for i, line in enumerate(response_lines)])
 
-    if retrieval_mode == "VDB":
-        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 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.", []
+    return intro + numbered_response, extract_addresses(response)
 
 
 
@@ -346,20 +384,48 @@ def generate_answer(message, choice, retrieval_mode):
 
 
 
+
+# def bot(history, choice, tts_choice, retrieval_mode):
+#     if not history:
+#         return history
+    
+#     response, addresses = generate_answer(history[-1][0], choice, retrieval_mode)
+#     history[-1][1] = ""
+
+#     # Detect if the response is from Yelp (i.e., HTML formatted response)
+#     if "<table>" in response:
+#         for chunk in response.splitlines():
+#             history[-1][1] += chunk + "\n"
+#             time.sleep(0.1)  # Adjust the delay as needed
+#             yield history, None
+#         return
+
+#     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)
+
+#         for character in response:
+#             history[-1][1] += character
+#             time.sleep(0.05)
+#             yield history, None
+
+#         audio_path = audio_future.result()
+#         yield history, audio_path
+
+#     history.append([response, None])  # Ensure the response is added in the correct format
+
 def bot(history, choice, tts_choice, retrieval_mode):
     if not history:
         return history
     
     response, addresses = generate_answer(history[-1][0], choice, retrieval_mode)
-    history[-1][1] = ""
+    response = f"<div style='font-size: 14px;'>{response}</div>"  # Adjust the font size
 
-    # Detect if the response is from Yelp (i.e., HTML formatted response)
-    if "<table>" in response:
-        for chunk in response.splitlines():
-            history[-1][1] += chunk + "\n"
-            time.sleep(0.1)  # Adjust the delay as needed
-            yield history, None
-        return
+    history[-1][1] = ""
 
     with concurrent.futures.ThreadPoolExecutor() as executor:
         if tts_choice == "Alpha":
@@ -385,7 +451,6 @@ def bot(history, choice, tts_choice, retrieval_mode):
 
 
 
-
 def add_message(history, message):
     history.append((message, None))
     return history, gr.Textbox(value="", interactive=True, placeholder="Enter message or upload file...", show_label=False)