app.py

""" Basic Agent Evaluation Runner"""
import os
import inspect
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"

# --- Basic Agent Definition ---
class BasicAgent:
    """A langgraph agent."""
    def __init__(self):
        print("BasicAgent initialized.")
        try:
            self.graph = build_graph(provider="groq")  # Using Groq as default
            print("Graph built successfully.")
        except Exception as e:
            print(f"Error building graph: {e}")
            self.graph = None

    def __call__(self, question: str) -> str:
        print(f"Agent received question: {question}")
        
        if self.graph is None:
            return "Error: Agent not properly initialized"
        
        # Wrap the question in a HumanMessage from langchain_core
        messages = [HumanMessage(content=question)]
        config = {"configurable": {"thread_id": f"eval_{hash(question)}"}}
        
        try:
            result = self.graph.invoke({"messages": messages}, config)
            
            # Handle different response formats
            if isinstance(result, dict):
                if 'messages' in result and result['messages']:
                    answer = result['messages'][-1].content
                elif 'final_answer' in result:
                    answer = result['final_answer']
                else:
                    answer = str(result)
            else:
                answer = str(result)
            
            # Extract final answer if present
            if "FINAL ANSWER:" in answer:
                return answer.split("FINAL ANSWER:")[-1].strip()
            else:
                return answer.strip()
                
        except Exception as e:
            error_msg = f"Error: {str(e)}"
            print(error_msg)
            return error_msg

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    # --- Determine HF Space Runtime URL and Repo URL ---
    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code

    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 = BasicAgent()
        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"
    print(f"Agent code URL: {agent_code}")

    # 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:
             print("Fetched questions list is empty.")
             return "Fetched questions list is empty or invalid format.", None
        print(f"Fetched {len(questions_data)} questions.")
    except requests.exceptions.RequestException as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None
    except Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        return f"An unexpected error occurred fetching questions: {e}", None

    # 3. Run your Agent
    results_log = []
    answers_payload = []
    print(f"Running 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:
            print(f"Skipping item with missing task_id or question: {item}")
            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}"
             print(f"Error running agent on task {task_id}: {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:
        print("Agent did not produce any answers to submit.")
        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    # 4. Prepare Submission 
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
    print(status_update)

    # 5. Submit
    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    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.')}"
        )
        print("Submission successful.")
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
    except Exception as e:
        status_message = f"Submission Failed: {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df

# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# LangGraph Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1. Log in to your Hugging Face account using the button below.
        2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
        
        **Agent Features:**
        - Uses FAISS vector database for similar question retrieval
        - Includes mathematical calculation tools
        - Web search capabilities (Tavily, Wikipedia, ArXiv)
        - Rate limiting for free tier models
        - Best free models: Groq Llama 3.3 70B, Gemini 2.0 Flash, NVIDIA Llama 3.1 70B
        """
    )

    gr.LoginButton()

    run_button = gr.Button("Run Evaluation & Submit All Answers")

    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 + " App Starting " + "-"*30)
    demo.launch(debug=True, share=False)