app.py

import os
import gradio as gr
import requests
import pandas as pd
from dotenv import load_dotenv
from functions import *
from langchain_core.messages import HumanMessage
import traceback
import time

load_dotenv()

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

def run_and_submit_all(profile: gr.OAuthProfile | None):
    space_id = os.getenv("SPACE_ID")

    if not profile:
        print("User not logged in.")
        return "Please Login to Hugging Face with the button.", None
    username = profile.username
    print(f"User logged in: {username}")

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

    try:
        graph = build_graph()
        agent = graph.invoke
    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 "Repo URL not available"
    print(f"Agent code repo: {agent_code}")

    # Fetch questions
    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 Exception as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None

    results_log = []
    answers_payload = []

    print(f"\n{'='*60}")
    print(f"Running agent on {len(questions_data)} questions...")
    print(f"{'='*60}\n")
    
    # Add delay between questions to avoid rate limiting
    question_delay = 3.0  # seconds between questions
    
    for idx, item in enumerate(questions_data, 1):
        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
        
        # Add delay between questions (except for the first one)
        if idx > 1:
            print(f"Waiting {question_delay}s before next question to avoid rate limits...")
            time.sleep(question_delay)
        
        print(f"\n--- Question {idx}/{len(questions_data)} ---")
        print(f"Task ID: {task_id}")
        print(f"Question: {question_text}")
        
        try:
            # Add timeout for each question
            start_time = time.time()
            input_messages = [HumanMessage(content=question_text)]
            
            # Invoke the agent with the question
            result = agent({"messages": input_messages})
            
            # Extract the answer from the result
            answer = "UNKNOWN"
            if "messages" in result and result["messages"]:
                # Look for the last AI message with content
                for msg in reversed(result["messages"]):
                    if hasattr(msg, "content") and isinstance(msg.content, str) and msg.content.strip():
                        # Skip planner outputs
                        if not any(msg.content.upper().startswith(prefix) for prefix in ["SEARCH:", "CALCULATE:", "DEFINE:", "WIKIPEDIA:", "REVERSE:", "DIRECT:"]):
                            answer = msg.content.strip()
                            break
            
            elapsed_time = time.time() - start_time
            print(f"Answer: {answer}")
            print(f"Time taken: {elapsed_time:.2f}s")
            
            answers_payload.append({"task_id": task_id, "submitted_answer": answer})
            results_log.append({
                "Task ID": task_id,
                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text,
                "Submitted Answer": answer,
                "Time (s)": f"{elapsed_time:.2f}"
            })
            
        except Exception as e:
            print(f"Error running agent on task {task_id}: {e}")
            print(f"Traceback: {traceback.format_exc()}")
            
            # Still submit UNKNOWN for errors
            answers_payload.append({"task_id": task_id, "submitted_answer": "UNKNOWN"})
            results_log.append({
                "Task ID": task_id,
                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text,
                "Submitted Answer": f"ERROR: {str(e)[:50]}",
                "Time (s)": "N/A"
            })

    print(f"\n{'='*60}")
    print(f"Completed processing all questions")
    print(f"{'='*60}\n")

    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)

    # Summary before submission
    unknown_count = sum(1 for ans in answers_payload if ans["submitted_answer"] == "UNKNOWN")
    print(f"\nSummary before submission:")
    print(f"Total questions: {len(answers_payload)}")
    print(f"UNKNOWN answers: {unknown_count}")
    print(f"Attempted answers: {len(answers_payload) - unknown_count}")

    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    print(f"\nSubmitting {len(answers_payload)} answers for user '{username}'...")

    try:
        response = requests.post(submit_url, json=submission_data, timeout=60)
        response.raise_for_status()
        result_data = response.json()
        
        score = result_data.get('score', 0)
        correct_count = result_data.get('correct_count', 0)
        total_attempted = result_data.get('total_attempted', 0)
        
        final_status = (
            f"Submission Successful!\n"
            f"User: {result_data.get('username')}\n"
            f"Overall Score: {score}% "
            f"({correct_count}/{total_attempted} correct)\n"
            f"Message: {result_data.get('message', 'No message received.')}"
        )
        
        print("\n" + "="*60)
        print("SUBMISSION RESULTS:")
        print(f"Score: {score}%")
        print(f"Correct: {correct_count}/{total_attempted}")
        print("="*60)
        
        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

# Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("# Enhanced GAIA Agent Evaluation Runner")
    gr.Markdown(
        """

        This enhanced agent is optimized for GAIA benchmark questions with improved:

        - Planning logic for better tool selection

        - Search capabilities with more comprehensive results

        - Mathematical expression parsing

        - Answer extraction from search results

        - Error handling and logging

        

        Target: >50% accuracy on GAIA questions

        """
    )

    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)

    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")

    if space_host_startup:
        print(f"   SPACE_HOST found: {space_host_startup}")
        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
    else:
        print("SPACE_HOST environment variable not found (running locally?).")

    if space_id_startup:
        print(f"   SPACE_ID found: {space_id_startup}")
        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
    else:
        print("SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")

    print("-"*(60 + len(" App Starting ")) + "\n")

    print("Launching Gradio Interface for Enhanced GAIA Agent Evaluation...")
    demo.launch(debug=True, share=False)