app.py

""" Enhanced LangGraph Agent Evaluation Runner - Final Version"""
import os
import gradio as gr
import requests
import pandas as pd
from langchain_core.messages import HumanMessage
from veryfinal import build_graph

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

# --- Enhanced Agent Definition ---
class EnhancedLangGraphAgent:
    """Enhanced LangGraph agent with proper response handling."""
    def __init__(self):
        print("Enhanced LangGraph Agent initialized.")
        try:
            self.graph = build_graph(provider="groq")
            print("LangGraph built successfully.")
        except Exception as e:
            print(f"Error building graph: {e}")
            self.graph = None

    def __call__(self, question: str) -> str:
        print(f"Processing: {question[:100]}...")
        
        if self.graph is None:
            return "Error: Agent not properly initialized"
        
        try:
            # Create messages and config
            messages = [HumanMessage(content=question)]
            config = {"configurable": {"thread_id": f"eval_{hash(question)}"}}
            
            # Invoke the graph
            result = self.graph.invoke({"messages": messages}, config)
            
            # Extract the final answer
            if result and "messages" in result and result["messages"]:
                final_message = result["messages"][-1]
                if hasattr(final_message, 'content'):
                    answer = final_message.content
                else:
                    answer = str(final_message)
                
                # Clean up the answer
                if "FINAL ANSWER:" in answer:
                    answer = answer.split("FINAL ANSWER:")[-1].strip()
                
                # Validate the answer
                if not answer or answer == question or len(answer.strip()) == 0:
                    return "Information not available"
                
                return answer.strip()
            else:
                return "Information not available"
                
        except Exception as e:
            print(f"Error processing question: {e}")
            return f"Error: {str(e)}"

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """Fetch questions, run agent, and submit answers."""
    space_id = os.getenv("SPACE_ID")
    
    if profile:
        username = f"{profile.username}"
        print(f"User logged in: {username}")
    else:
        print("User not logged in.")
        return "Please Login to Hugging Face with the button.", None

    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    # 1. Instantiate Agent
    try:
        agent = EnhancedLangGraphAgent()
        if agent.graph is None:
            return "Error: Failed to initialize agent properly", None
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None
    
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "No space ID available"

    # 2. Fetch Questions
    print(f"Fetching questions from: {questions_url}")
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
            return "Fetched questions list is empty or invalid format.", None
        print(f"Fetched {len(questions_data)} questions.")
    except Exception as e:
        return f"Error fetching questions: {e}", None

    # 3. Run Agent
    results_log = []
    answers_payload = []
    print(f"Running Enhanced LangGraph agent on {len(questions_data)} questions...")
    
    for i, item in enumerate(questions_data):
        task_id = item.get("task_id")
        question_text = item.get("question")
        
        if not task_id or question_text is None:
            continue
            
        print(f"Processing question {i+1}/{len(questions_data)}: {task_id}")
        
        try:
            submitted_answer = agent(question_text)
            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
            results_log.append({
                "Task ID": task_id, 
                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text, 
                "Submitted Answer": submitted_answer[:200] + "..." if len(submitted_answer) > 200 else submitted_answer
            })
        except Exception as e:
            error_msg = f"AGENT ERROR: {e}"
            answers_payload.append({"task_id": task_id, "submitted_answer": error_msg})
            results_log.append({
                "Task ID": task_id, 
                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text, 
                "Submitted Answer": error_msg
            })

    if not answers_payload:
        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    # 4. Submit
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    print(f"Submitting {len(answers_payload)} answers...")
    
    try:
        response = requests.post(submit_url, json=submission_data, timeout=60)
        response.raise_for_status()
        result_data = response.json()
        final_status = (
            f"Submission Successful!\n"
            f"User: {result_data.get('username')}\n"
            f"Overall Score: {result_data.get('score', 'N/A')}% "
            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
            f"Message: {result_data.get('message', 'No message received.')}"
        )
        return final_status, pd.DataFrame(results_log)
    except Exception as e:
        return f"Submission Failed: {e}", pd.DataFrame(results_log)

# --- Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# Enhanced LangGraph Agent - Final Version")
    gr.Markdown(
        """
        **Features:**
        - ✅ Proper LangGraph structure with tool integration
        - ✅ Multi-LLM support (Groq, Google, HuggingFace)
        - ✅ Enhanced search capabilities (Wikipedia, Tavily, ArXiv)
        - ✅ Mathematical tools for calculations
        - ✅ Vector store integration for similar questions
        - ✅ Proper response formatting and validation
        - ✅ Error handling and fallback mechanisms
        
        **Tools Available:**
        - Mathematical operations (add, subtract, multiply, divide, modulus)
        - Wikipedia search for encyclopedic information
        - Web search via Tavily for current information
        - ArXiv search for academic papers
        - Vector similarity search for related questions
        """
    )

    gr.LoginButton()
    run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)

    run_button.click(
        fn=run_and_submit_all,
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    print("\n" + "-"*30 + " Enhanced LangGraph Agent Starting " + "-"*30)
    demo.launch(debug=True, share=False)