app.py

import os
import gradio as gr
import requests
import pandas as pd
import json

# Import your upgraded agent
from agent import GeminiAgent

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
MY_HF_USERNAME = "benjipeng"
ANSWERS_FILE = "answers.json"

# --- Logic for Running the Agent ---
def run_agent_only(profile: gr.OAuthProfile | None):
    """
    Fetches questions, runs the agent on them, and saves the answers to a file.
    This is the long-running part of the process.
    """
    if not profile or profile.username != MY_HF_USERNAME:
        yield "Error: Please log in as the correct user (`benjipeng`) to run the agent.", pd.DataFrame()
        return

    print("Starting agent run...")
    yield "Fetching questions...", pd.DataFrame()

    try:
        response = requests.get(f"{DEFAULT_API_URL}/questions", timeout=20)
        response.raise_for_status()
        questions_data = response.json()
    except Exception as e:
        yield f"Error fetching questions: {e}", pd.DataFrame()
        return
    
    yield f"Fetched {len(questions_data)} questions. Initializing agent...", pd.DataFrame()
    agent = GeminiAgent()
    
    all_answers = []
    results_log = []

    for i, item in enumerate(questions_data):
        task_id = item.get("task_id")
        question_text = item.get("question")
        has_file = item.get("file", None) is not None
        
        status_message = f"Processing question {i+1}/{len(questions_data)} (Task ID: {task_id})..."
        yield status_message, pd.DataFrame(results_log)
        
        modified_question = f"{question_text}\n\n[Agent Note: A file is attached.]" if has_file else question_text
        
        try:
            submitted_answer = agent(modified_question, task_id)
        except Exception as e:
            submitted_answer = f"AGENT ERROR: {e}"
        
        all_answers.append({"task_id": task_id, "submitted_answer": submitted_answer})
        results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})

        # Save progress incrementally
        with open(ANSWERS_FILE, 'w') as f:
            json.dump(all_answers, f, indent=2)

    yield f"Agent run complete. All {len(all_answers)} answers saved to {ANSWERS_FILE}. Ready to submit.", pd.DataFrame(results_log)


# --- Logic for Submitting Answers ---
def submit_saved_answers(profile: gr.OAuthProfile | None):
    """
    Reads the answers from the saved file and submits them to the scoring server.
    This is the fast part of the process.
    """
    if not profile or profile.username != MY_HF_USERNAME:
        return "Error: Please log in as the correct user (`benjipeng`) to submit."

    space_id = os.getenv("SPACE_ID")
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    username = profile.username

    try:
        with open(ANSWERS_FILE, 'r') as f:
            answers_payload = json.load(f)
    except FileNotFoundError:
        return f"Error: Answers file '{ANSWERS_FILE}' not found. Please run the agent first."
    except json.JSONDecodeError:
        return f"Error: Could not read the answers file. It might be corrupted."

    if not answers_payload:
        return "Error: Answers file is empty."
        
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    
    submit_url = f"{DEFAULT_API_URL}/submit"
    print(f"Submitting {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()
        return (
            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.')}"
        )
    except requests.exceptions.HTTPError as e:
        return f"Submission Failed: Server responded with status {e.response.status_code}. Detail: {e.response.text}"
    except Exception as e:
        return f"An unexpected error occurred during submission: {e}"

# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# Gemini ReAct Agent for GAIA (Two-Step Submission)")
    gr.Markdown(
        """
        **Step 1: Run Agent & Save Answers**
        - This is the long process that can take 10-20 minutes.
        - The agent will answer all 20 questions and save the results to a file.
        - You will see the progress in the status box and the table below.
        
        **Step 2: Submit Saved Answers**
        - Once Step 1 is complete, click this button.
        - This will be very fast and will send your saved answers to be scored.
        """
    )
    
    gr.LoginButton()

    with gr.Row():
        run_button = gr.Button("Step 1: Run Agent & Save Answers")
        submit_button = gr.Button("Step 2: Submit Saved 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, interactive=False)

    run_button.click(
        fn=run_agent_only,
        inputs=None, # LoginButton profile is passed implicitly
        outputs=[status_output, results_table]
    )
    
    submit_button.click(
        fn=submit_saved_answers,
        inputs=None, # LoginButton profile is passed implicitly
        outputs=[status_output]
    )

if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    demo.launch(debug=True, share=False)