Update app.py

app.py

@@ -302,6 +302,110 @@ def bot(history, choice, tts_choice, retrieval_mode):
 
     history.append([response, None])  # Ensure the response is added in the correct format
 
+# Langchain imports
+from langchain.agents import tool
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain_core.tools import OpenAIToolsAgentOutputParser
+from langchain_core.tools import format_to_openai_tool_messages
+from langchain import OpenAI
+
+
+
+# Step 1: Define the restaurant tool
+@tool
+def fetch_restaurant_info(query: str) -> str:
+    """
+    Fetches restaurant-related information from SERP API based on the given query.
+    """
+    from serpapi.google_search import GoogleSearch
+
+    # Define parameters for SERP API
+    params = {
+        "engine": "yelp",
+        "find_desc": query,
+        "find_loc": "Birmingham, AL, USA",
+        "api_key": os.getenv("SERP_API")
+    }
+
+    # Fetch data from SERP API
+    search = GoogleSearch(params)
+    results = search.get_dict()
+    organic_results = results.get("organic_results", [])
+
+    # Prepare the output in plain text
+    if organic_results:
+        response = ""
+        for result in organic_results:
+            name = result.get("title", "No name")
+            rating = result.get("rating", "No rating")
+            reviews = result.get("reviews", "No reviews")
+            phone = result.get("phone", "Not Available")
+            snippet = result.get("snippet", "Not Available")
+            services = result.get("service_options", "Not Known")
+
+            if isinstance(services, list):
+                services = ", ".join(services)
+            elif isinstance(services, dict):
+                services = ", ".join([f"{key}: {value}" for key, value in services.items()])
+
+            link = result.get("link", "#")
+            response += f"Name: {name}\nRating: {rating}\nReviews: {reviews}\nPhone: {phone}\nSnippet: {snippet}\nServices: {services}\nLink: {link}\n\n"
+        
+        return response
+    else:
+        return "No restaurant information found."
+
+# Step 2: Integrate the tool with the agent
+tools = [fetch_restaurant_info]
+
+# Define the prompt template
+MEMORY_KEY = "chat_history"
+prompt = ChatPromptTemplate.from_messages(
+    [
+        (
+            "system",
+            "You are a very powerful assistant, but you only respond with restaurant information when asked about restaurants.",
+        ),
+        MessagesPlaceholder(variable_name=MEMORY_KEY),
+        ("user", "{input}"),
+        MessagesPlaceholder(variable_name="agent_scratchpad"),
+    ]
+)
+
+# Define the chat history
+chat_history = []
+
+# Create the agent with the tool
+llm = OpenAI(api_key=os.getenv("OPENAI_API_KEY"), temperature=0, model="gpt-4o")
+agent = (
+    {
+        "input": lambda x: x["input"],
+        "agent_scratchpad": lambda x: format_to_openai_tool_messages(x["intermediate_steps"]),
+        "chat_history": lambda x: x["chat_history"],
+    }
+    | prompt
+    | llm
+    | OpenAIToolsAgentOutputParser(tools=tools)
+)
+
+agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+
+# Step 3: Update the chatbot function
+def chatbot_response(user_input, history, choice, tts_choice, retrieval_mode):
+    # Check if the user input is related to restaurants
+    if "restaurant" in user_input.lower():
+        result = agent_executor.invoke({"input": user_input, "chat_history": history})
+        history.append([user_input, result["output"]])
+        return history, None
+    else:
+        # Use the existing logic for non-restaurant-related queries
+        response, addresses = generate_answer(user_input, choice, retrieval_mode)
+        history.append([user_input, response])
+        return history, None
+
+
+
 
 
 def add_message(history, message):