Update app.py

app.py

@@ -277,140 +277,30 @@ def generate_answer(message, choice, retrieval_mode):
     else:
         return "Invalid retrieval mode selected.", []
 
-# 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] = ""
-
-#     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
     
-    user_message = history[-1][0].lower()
+    response, addresses = generate_answer(history[-1][0], choice, retrieval_mode)
+    history[-1][1] = ""
 
-    # Check if the query is related to restaurants
-    if "restaurant" in user_message or "restaurants" in user_message:
-        # Use the LangChain agent to get restaurant info
-        response = next(agent_executor.stream({"input": user_message}))
-    else:
-        # Continue with the normal process if not a restaurant query
-        response, addresses = generate_answer(user_message, choice, retrieval_mode)
-        history[-1][1] = ""
-
-        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
+    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)
 
-    history.append([response, None])  # Ensure the response is added in the correct format
-
-
-from langchain.agents import tool
-from serpapi.google_search import GoogleSearch
-
-@tool
-def get_restaurant_info(term: str) -> str:
-    """Fetches and formats restaurant information from Yelp using the SERP API."""
-    params = {
-        "engine": "yelp",
-        "find_desc": term,
-        "find_loc": "Birmingham, AL, USA",  # Fixed location
-        "api_key": os.getenv("SERP_API")
-    }
-
-    search = GoogleSearch(params)
-    results = search.get_dict()
-    organic_results = results.get("organic_results", [])
-
-    if not organic_results:
-        return "No restaurant information found."
-
-    formatted_info = []
-    for result in organic_results:
-        formatted_info.append(
-            f"**Name:** {result.get('title', 'No name')}\n"
-            f"**Rating:** {result.get('rating', 'No rating')} stars\n"
-            f"**Reviews:** {result.get('reviews', 'No reviews')}\n"
-            f"**Phone:** {result.get('phone', 'N/A')}\n"
-            f"**Snippet:** {result.get('snippet', 'N/A')}\n"
-            f"**Services:** {result.get('service_options', 'N/A')}\n"
-            f"**Yelp URL:** [Link]({result.get('link', '#')})\n"
-        )
-    return "\n\n".join(formatted_info)
-
-
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain.agents import tool
-from langchain.agents.format_scratchpad.openai_tools import format_to_openai_tool_messages
-from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser
-from langchain_community.llms import OpenAI
-from langchain.agents import AgentExecutor
-
-# Define the tools
-tools = [get_restaurant_info]
-
-# Define the prompt
-prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", "You are a very powerful assistant, but you don't know current events."),
-        ("user", "{input}"),
-        MessagesPlaceholder(variable_name="agent_scratchpad"),
-    ]
-)
-
-# Define the LLM
-llm_with_tools = OpenAI(model="gpt-4o")
-
-# Create the agent
-agent = (
-    {
-        "input": lambda x: x["input"],
-        "agent_scratchpad": lambda x: format_to_openai_tool_messages(
-            x["intermediate_steps"]
-        ),
-    }
-    | prompt
-    | llm_with_tools
-    | OpenAIToolsAgentOutputParser()
-)
-
-# Create the agent executor
-agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+        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
 
 
 
@@ -1065,7 +955,7 @@ with gr.Blocks(theme='Pijush2023/scikit-learn-pijush') as demo:
         #     weather_output = gr.HTML(value=fetch_local_weather())
         #     news_output = gr.HTML(value=fetch_local_news())
         #     events_output = gr.HTML(value=fetch_local_events())
-              restaurant_output=gr.HTML(value=fetch_yelp_restaurants())
+            # restaurant_output=gr.HTML(value=fetch_yelp_restaurants())
         
 
         with gr.Column():