app.py

import requests
import json

# Configuration
HF_USERNAME = "YOUR_HF_USERNAME"  # Replace with your Hugging Face username
AGENT_CODE_URL = f"https://huggingface.co/spaces/{HF_USERNAME}/YOUR_SPACE_NAME/tree/main"
API_BASE_URL = "https://agents-course-unit4-scoring.hf.space"

def fetch_questions():
    """Retrieves the 20 GAIA level 1 validation questions from the API."""
    print("Fetching questions from the API...")
    response = requests.get(f"{API_BASE_URL}/questions")
    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"Failed to fetch questions: {response.text}")

def download_task_file(task_id):
    """Downloads associated files if the task requires it."""
    # The API documentation specifies: GET /files/{task_id}
    # Use this if your agent needs to read PDFs, images, or CSVs locally
    url = f"{API_BASE_URL}/files/{task_id}"
    response = requests.get(url)
    if response.status_code == 200:
        file_path = f"./{task_id}_file" # You'll want to parse the header for exact extension
        with open(file_path, "wb") as f:
            f.write(response.content)
        return file_path
    return None

def dummy_agent_fallback(question_text):
    """
    Placeholder for your real agent call.
    REPLACE THIS with your actual LLM/Agent call!
    """
    # Strict prompt constraint to force exact matches
    system_prompt = (
        "You are a precise QA bot. Answer the question using your tools. "
        "Output ONLY the final string or number representing the answer. "
        "Do not include 'The answer is...', do not include 'FINAL ANSWER', "
        "and do not include any conversational filler."
    )
    # Your agent logic goes here (e.g., agent.run(question_text))
    return "example_answer" 

def submit_answers(submission_payload):
    """Submits the payload to the final leaderboard API."""
    print("Submitting answers to the leaderboard...")
    headers = {"Content-Type": "application/json"}
    response = requests.post(f"{API_BASE_URL}/submit", json=submission_payload, headers=headers)
    
    if response.status_code == 200:
        print("🎉 Submission Successful!")
        print("API Response:", response.json())
    else:
        print(f"❌ Submission Failed ({response.status_code}):", response.text)

def main():
    questions = fetch_questions()
    submitted_answers = []
    
    # Loop through all 20 questions
    for item in questions:
        task_id = item.get("task_id")
        question = item.get("question")
        has_file = item.get("has_file", False)
        
        print(f"\nProcessing Task ID: {task_id}")
        
        # If the task has an associated file, download it first
        if has_file:
            file_path = download_task_file(task_id)
            print(f"Downloaded file for task to: {file_path}")
            
        # Run your agent framework here
        answer = dummy_agent_fallback(question)
        print(f"Agent Output: {answer}")
        
        # Format for API specification
        submitted_answers.append({
            "task_id": task_id,
            "submitted_answer": str(answer).strip()
        })
        
    # Build final payload
    payload = {
        "username": HF_USERNAME,
        "agent_code": AGENT_CODE_URL,
        "answers": submitted_answers
    }
    
    # Post results
    submit_answers(payload)

if __name__ == "__main__":
    main()