app.py

import os
import gradio as gr
import requests
import pandas as pd
from typing import Dict, List
import asyncio

# custom imports
from agents import Agent
from tool import get_tools
from model import get_model

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
MODEL_ID = "groq/llama-3.3-70b-versatile"  # Groq's fastest model
RATE_LIMIT_DELAY = 1  # Groq has generous rate limits


# --- Async Question Processing ---
async def process_question(agent, question: str, task_id: str) -> Dict:
    """Process a single question and return both answer AND full log entry"""
    try:
        answer = agent(question)
        return {
            "submission": {"task_id": task_id, "submitted_answer": answer},
            "log": {"Task ID": task_id, "Question": question, "Submitted Answer": answer}
        }
    except Exception as e:
        error_msg = f"ERROR: {str(e)}"
        return {
            "submission": {"task_id": task_id, "submitted_answer": error_msg},
            "log": {"Task ID": task_id, "Question": question, "Submitted Answer": error_msg}
        }

async def run_questions_async(agent, questions_data: List[Dict]) -> tuple:
    """Process questions sequentially with minimal rate limiting"""
    submissions = []
    logs = []
    
    total = len(questions_data)
    for idx, q in enumerate(questions_data):
        print(f"Processing {idx+1}/{total}: {q['question'][:80]}...")
        
        # Add small delay between requests
        if idx > 0:
            await asyncio.sleep(RATE_LIMIT_DELAY)
        
        result = await process_question(agent, q["question"], q["task_id"])
        submissions.append(result["submission"])
        logs.append(result["log"])
    
    return submissions, logs


async def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the Agent on them, submits all answers,
    and displays the results.
    """
    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 = Agent(
            model=get_model("LiteLLMModel", MODEL_ID),
            tools=get_tools()
        )
    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"
    print(f"Agent code: {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.")
        estimated_time = len(questions_data) * RATE_LIMIT_DELAY / 60
        print(f"⏱️ Estimated time: {estimated_time:.1f} minutes")
    except Exception as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None

    # 3. Run Agent
    print(f"Running agent on {len(questions_data)} questions...")
    answers_payload, results_log = await run_questions_async(agent, questions_data)

    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
    }
    print(f"Submitting {len(answers_payload)} answers for user '{username}'...")

    # 5. Submit
    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\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\n"
            f"Message: {result_data.get('message', 'No message received.')}\n\n"
            f"Leaderboard: {api_url}/leaderboard"
        )
        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 ---
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🤖 GAIA Agent Evaluation")
    gr.Markdown(
        f"""
        **Instructions:**
        1. Log in to your Hugging Face account using the button below
        2. Click 'Run Evaluation & Submit' to test your agent
        3. The agent will use web search and other tools to answer questions
        
        **Current Setup:**
        - Model: Llama 3.3 70B (via Groq)
        - Tools: Web search, Wikipedia, calculation, and more
        - Rate Limiting: {RATE_LIMIT_DELAY}s between requests
        
        ⚠️ **Note:** Make sure you have set your GROQ_API_KEY in the Space secrets.
        """
    )

    gr.LoginButton()

    run_button = gr.Button("🚀 Run Evaluation & Submit", variant="primary")

    status_output = gr.Textbox(label="📊 Status / Results", lines=8, interactive=False)
    results_table = gr.DataFrame(label="📋 Questions and Answers", wrap=True, max_height=400)

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

if __name__ == "__main__":
    print("\n" + "="*70)
    print("🤖 GAIA Agent Starting")
    print("="*70)
    print(f"📝 Using Model: {MODEL_ID}")
    
    space_host = os.getenv("SPACE_HOST")
    space_id = os.getenv("SPACE_ID")

    if space_host:
        print(f"✅ Runtime URL: https://{space_host}.hf.space")
    if space_id:
        print(f"✅ Repo URL: https://huggingface.co/spaces/{space_id}")
    
    print("="*70 + "\n")
    demo.launch(debug=True, share=False)