Update app.py

app.py

@@ -4,20 +4,192 @@ import requests
 import inspect
 import pandas as pd
 
+# LangChain & LangGraph imports
+from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, SystemMessage
+from langchain_core.tools import tool
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langgraph.prebuilt import create_react_agent
+from langchain_openai import ChatOpenAI
+
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
+# --- Tool Definitions ---
+@tool
+def search_wikipedia(query: str) -> str:
+    """Search Wikipedia for information. Use this for factual questions about people, places, events, etc."""
+    try:
+        import wikipediaapi
+        wiki = wikipediaapi.Wikipedia(
+            language='en',
+            user_agent='MyLangGraphAgent/1.0 (contact: itay@razum.com)'
+        )
+        page = wiki.page(query)
+        if page.exists():
+            return page.summary[:500]
+        else:
+            return f"No Wikipedia page found for '{query}'. Try a different search term."
+    except Exception as e:
+        return f"Error searching Wikipedia: {str(e)}"
+
+@tool
+def web_search(query: str) -> str:
+    """Search the web for general information. Use this when Wikipedia is not sufficient."""
+    try:
+        from duckduckgo_search import DDGS
+        with DDGS() as ddgs:
+            results = list(ddgs.text(query, max_results=3))
+            if results:
+                formatted = "\n\n".join([
+                    f"Title: {r['title']}\nSnippet: {r['body']}\nURL: {r['href']}"
+                    for r in results
+                ])
+                return formatted
+            return "No results found."
+    except Exception as e:
+        return f"Error performing web search: {str(e)}"
+
+@tool
+def calculator(expression: str) -> str:
+    """Evaluate mathematical expressions. Example: '15 + 27' or '2 * (3 + 4)'."""
+    try:
+        result = eval(expression, {"__builtins__": {}}, {})
+        return str(result)
+    except Exception as e:
+        return f"Error calculating: {str(e)}"
+
+@tool
+def reverse_text(text: str) -> str:
+    """Reverse a string of text. Useful for decoding reversed messages."""
+    return text[::-1]
+
+@tool
+def visit_page(url: str, query: str = "") -> str:
+    """Visit a specific URL and extract/summarize content based on a query.
+    
+    Args:
+        url: The URL to visit and extract content from
+        query: Optional focus query to guide what information to extract
+    
+    Returns:
+        Summarized content relevant to the query, or full page summary if no query provided
+    """
+    try:
+        from bs4 import BeautifulSoup
+        
+        headers = {
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
+        }
+        response = requests.get(url, headers=headers, timeout=10)
+        response.raise_for_status()
+        
+        soup = BeautifulSoup(response.content, 'html.parser')
+        
+        for script in soup(["script", "style", "nav", "footer", "header"]):
+            script.decompose()
+        
+        text = soup.get_text(separator=' ', strip=True)
+        lines = (line.strip() for line in text.splitlines())
+        chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
+        text = ' '.join(chunk for chunk in chunks if chunk)
+        
+        max_length = 3000
+        if len(text) > max_length:
+            text = text[:max_length]
+        
+        return f"Content from {url}:\n\n{text[:1000]}..."
+            
+    except requests.Timeout:
+        return f"Error: Timeout while trying to access {url}"
+    except requests.RequestException as e:
+        return f"Error fetching {url}: {str(e)}"
+    except Exception as e:
+        return f"Error processing {url}: {str(e)}"
+
+# --- LangGraph Agent Definition ---
+# ----- THIS IS WHERE YOU CAN BUILD WHAT YOU WANT ------
+class LangGraphAgent:
+    """LangGraph ReAct agent with tools for question answering."""
+    
     def __init__(self):
-        print("BasicAgent initialized.")
+        print("Initializing LangGraph Agent...")
+        
+        # Configure LangSmith tracing if API key is available
+        if os.getenv("LANGCHAIN_API_KEY"):
+            os.environ["LANGCHAIN_TRACING_V2"] = "true"
+            os.environ["LANGCHAIN_PROJECT"] = "agent-benchmark-production"
+            print("🔍 LangSmith tracing enabled")
+        
+        # Set up LLM
+        api_key = os.getenv("OPENAI_API_KEY")
+        if not api_key:
+            raise ValueError("OPENAI_API_KEY environment variable not set!")
+        
+        self.llm = ChatOpenAI(
+            model="gpt-4o-mini",
+            temperature=0,
+            api_key=api_key
+        )
+        
+        # Define tools
+        self.tools = [search_wikipedia, web_search, calculator, reverse_text, visit_page]
+        
+        # System prompt
+        system_message = SystemMessage(
+            content="""You are a helpful AI assistant that can answer questions by using various tools.
+
+When answering questions:
+1. Think step-by-step about what information you need
+2. Use the appropriate tools to gather information
+   - search_wikipedia: For factual questions about people, places, events
+   - web_search: For general web searches
+   - calculator: For mathematical calculations
+   - reverse_text: For reversing text strings
+   - visit_page: For extracting content from specific URLs
+3. Provide clear, concise, and accurate answers
+4. If you're not sure, say so rather than making up information
+5. The answer should be short - for example, if asked how many awards someone won, answer with just the number
+6. Write only the final answer without extra explanation
+7. Simplify complex tasks into subtasks and think about which tool to use for each"""
+        )
+        
+        # Create prompt template
+        prompt = ChatPromptTemplate.from_messages([
+            system_message,
+            MessagesPlaceholder(variable_name="messages")
+        ])
+        
+        # Create ReAct agent
+        self.agent = create_react_agent(
+            self.llm,
+            self.tools,
+            prompt=prompt
+        )
+        
+        print(f"✅ Agent initialized with {len(self.tools)} tools")
+        print("   Model: gpt-4o-mini")
+    
     def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+        """Process a question and return an answer."""
+        print(f"Agent processing question: {question[:100]}...")
+        
+        try:
+            result = self.agent.invoke({
+                "messages": [HumanMessage(content=question)]
+            })
+            
+            # Extract final answer
+            final_message = result["messages"][-1]
+            answer = final_message.content
+            
+            print(f"Agent answer: {answer[:100]}...")
+            return answer
+            
+        except Exception as e:
+            error_msg = f"Error processing question: {str(e)}"
+            print(f"❌ {error_msg}")
+            return error_msg
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -40,7 +212,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 
     # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = BasicAgent()
+        agent = LangGraphAgent()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None