Update app.py

app.py

@@ -277,31 +277,137 @@ 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
     
-    response, addresses = generate_answer(history[-1][0], choice, retrieval_mode)
-    history[-1][1] = ""
+    user_message = history[-1][0].lower()
 
-    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)
+    # 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
 
-        for character in response:
-            history[-1][1] += character
-            time.sleep(0.05)
-            yield history, None
+    history.append([response, None])  # Ensure the response is added in the correct format
 
-        audio_path = audio_future.result()
-        yield history, audio_path
 
-    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.llms import OpenAI
+
+# 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-3.5-turbo")
+
+# 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)