app.py

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

# Import your upgraded agent
from agent import GeminiAgent

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
# This is the security gate. Only this user can run submissions.
MY_HF_USERNAME = "benjipeng"

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the GeminiAgent on them, submits all answers,
    and displays the results. This function is restricted to a specific user and
    provides file context to the agent.
    """
    # --- Determine HF Space Runtime URL and Repo URL ---
    space_id = os.getenv("SPACE_ID")

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

    if username != MY_HF_USERNAME:
        print(f"Access denied for user: {username}. Allowed user is {MY_HF_USERNAME}.")
        return f"Error: This Space is configured for a specific user. Access denied for '{username}'.", None
    
    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    # 1. Instantiate Agent
    print("Instantiating agent...")
    try:
        agent = GeminiAgent()
    except Exception as e:
        error_msg = f"Error initializing agent: {e}"
        print(error_msg)
        return error_msg, None
    
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(f"Code link for submission: {agent_code}")

    # 2. Fetch Questions
    print(f"Fetching questions from: {questions_url}")
    try:
        response = requests.get(questions_url, timeout=20)
        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:
        error_msg = f"Error fetching questions: {e}"
        print(error_msg)
        return error_msg, None
    except requests.exceptions.JSONDecodeError as e:
         error_msg = f"Error decoding server response for questions: {e}"
         print(error_msg)
         print(f"Response text: {response.text[:500]}")
         return error_msg, None

    # 3. Run your Agent (with context injection)
    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")
        
        # This is the key improvement: check if a file is associated with the question
        has_file = item.get("file", None) is not None
        
        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue

        # Modify the question to give the agent context about the file's existence
        if has_file:
            modified_question = f"{question_text}\n\n[Agent Note: A file is attached to this question. Use the 'read_file_from_api' tool to access it if needed.]"
        else:
            modified_question = question_text

        try:
            # Pass BOTH the modified question and the task_id to the agent
            submitted_answer = agent(modified_question, task_id)
            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:
        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=120) # Increased timeout
        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.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

# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# Gemini ReAct Agent for GAIA")
    gr.Markdown(
        """
        **Instructions:**
        1.  Log in using the Hugging Face login button below.
        2.  Click 'Run Evaluation & Submit' to start the process.
        3.  The agent will fetch all 20 questions, reason about them step-by-step, use tools (like web search and a file reader), and submit the final answers for scoring.

        **Note:** This process can take several minutes. Please be patient.
        """
    )
    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)
    demo.launch(debug=True, share=False)