app.py

import os
import gradio as gr
import requests
import pandas as pd
from gaia_agent import GAIAAgent
from dotenv import load_dotenv

# Load environment variables
# load_dotenv()

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

# --- Agent Setup ---
hf_token = os.getenv("HF_TOKEN")
if not hf_token:
    raise RuntimeError("Set HF_TOKEN in your Space secrets")

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the GAIAAgent on them, submits all answers,
    and displays the results.
    """
    # --- Determine HF Space Runtime URL and Repo URL ---
    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 = GAIAAgent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None
    
    # Agent code location
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Local"
    print(f"Agent code location: {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 requests.exceptions.JSONDecodeError as e:
        print(f"Error decoding JSON response from questions endpoint: {e}")
        print(f"Response text: {response.text[:500]}")
        return f"Error decoding server response for 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 item in 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
        
        try:
            print(f"\n{'='*50}")
            print(f"Processing Task ID: {task_id}")
            print(f"Question: {question_text}")
            
            submitted_answer = agent(question_text)
            
            print(f"Answer: {submitted_answer}")
            print(f"{'='*50}\n")
            
            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
        except Exception as e:
            print(f"Error running agent on task {task_id}: {e}")
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
    
    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 requests.exceptions.HTTPError as e:
        error_detail = f"Server responded with status {e.response.status_code}."
        try:
            error_json = e.response.json()
            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
        except requests.exceptions.JSONDecodeError:
            error_detail += f" Response: {e.response.text[:500]}"
        
        status_message = f"Submission Failed: {error_detail}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
        
    except requests.exceptions.Timeout:
        status_message = "Submission Failed: The request timed out."
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
        
    except requests.exceptions.RequestException as e:
        status_message = f"Submission Failed: Network error - {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
        
    except Exception as e:
        status_message = f"An unexpected error occurred during submission: {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("# GAIA Benchmark Agent Evaluation")
    gr.Markdown(
        """
**Instructions:**
1. This app uses a smolagents CodeAgent with multiple tools (calculator, Wikipedia, web search).
2. Log in to your Hugging Face account using the button below.
3. Click 'Run Evaluation & Submit All Answers' to fetch GAIA questions, run your agent, and submit answers.

**Agent Tools:**
- Mathematical operations (add, subtract, multiply, divide, modulus)
- Wikipedia search
- Web search (DuckDuckGo)
- Web content fetcher

**Note:** Processing all questions may take several minutes depending on the number of questions and API response times.
        """
    )
    
    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 + " App Starting " + "-"*30)
    
    # Check for required environment variables
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")
    hf_token_startup = os.getenv("HF_TOKEN")
    
    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("ℹ️ 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("ℹ️ SPACE_ID environment variable not found (running locally?).")
    
    if hf_token_startup:
        print("✅ HF_TOKEN found")
    else:
        print("⚠️ HF_TOKEN not found - agent will not work without it!")
    
    print("-"*(60 + len(" App Starting ")) + "\n")
    print("Launching Gradio Interface for GAIA Agent Evaluation...")
    
    demo.launch(debug=True, share=False)