Update app.py

app.py

@@ -351,6 +351,71 @@ def generate_map(location_names):
     
     map_html = m._repr_html_()
     return map_html
+#------------------------------------
+
+
+# Step 1: Define the Yelp Search Tool
+class YelpSearchTool(BaseTool):
+    name = "YelpSearch"
+    description = "A tool to search for restaurants in Birmingham, AL using Yelp."
+
+    def _run(self, query: str) -> str:
+        params = {
+            "engine": "yelp",
+            "find_desc": "Restaurant",
+            "find_loc": "Birmingham, AL, USA",
+            "api_key": os.getenv("SERP_API_KEY")
+        }
+
+        response = requests.get("https://serpapi.com/search.json", params=params)
+        if response.status_code == 200:
+            results = response.json().get("organic_results", [])
+            if not results:
+                return "No results found."
+            
+            result_str = "Top Restaurants:\n"
+            for result in results[:5]:  # Limit to top 5 results
+                name = result.get("title", "No name")
+                rating = result.get("rating", "No rating")
+                reviews = result.get("reviews", "No reviews")
+                address = result.get("address", "No address available")
+                result_str += f"Name: {name}\nRating: {rating}\nReviews: {reviews}\nAddress: {address}\n\n"
+
+            return result_str
+        else:
+            return f"Failed to fetch data from Yelp. Status code: {response.status_code}"
+
+    def _arun(self, query: str):
+        raise NotImplementedError("YelpSearchTool does not support async execution.")
+
+# Initialize the LLM and the Tool
+yelp_tool = YelpSearchTool()
+tools = [
+    Tool(
+        name="YelpSearch",
+        func=yelp_tool._run,
+        description="Search for restaurants in Birmingham using Yelp."
+    )
+]
+
+# Initialize the agent
+agent = initialize_agent(
+    tools=tools,
+    llm=llm,
+    agent="zero-shot-react-description",
+    verbose=True
+)
+
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+conversational_agent = ConversationalRetrievalChain.from_llm(
+    llm=llm,
+    retriever=agent,
+    memory=memory
+)
+
+
+#--------------------------------------------------------
+
 
 def fetch_local_news():
     api_key = os.environ['SERP_API']