app.py

"""
app.py
This script provides the Gradio web interface to run the evaluation.
This version is simplified to work with the new agent architecture and has
the correct Gradio wiring for the Hugging Face login functionality.
"""

import os
import re
import gradio as gr
import requests
import pandas as pd
from urllib.parse import urlparse

from agent import create_agent_executor

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

# --- Helper function to parse the agent's output ---
def parse_final_answer(agent_response: str) -> str:
    match = re.search(r"FINAL ANSWER:\s*(.*)", agent_response, re.IGNORECASE | re.DOTALL)
    if match: return match.group(1).strip()
    lines = [line for line in agent_response.split('\n') if line.strip()]
    if lines: return lines[-1].strip()
    return "Could not parse a final answer."

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the agent on them, submits all answers,
    and displays the results.
    """
    if not profile:
        return "Please log in to Hugging Face with the button above to submit.", None
    
    username = profile.username
    print(f"User logged in: {username}")
    
    space_id = os.getenv("SPACE_ID")
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    questions_url = f"{DEFAULT_API_URL}/questions"
    submit_url = f"{DEFAULT_API_URL}/submit"
    
    # 1. Instantiate Agent
    print("Initializing your custom agent...")
    try:
        agent_executor = create_agent_executor(provider="groq")  
    except Exception as e:
        return f"Fatal Error: Could not initialize agent. Check logs. Details: {e}", None

    # 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()
        print(f"Fetched {len(questions_data)} questions.")
    except Exception as e:
        return f"Error fetching questions: {e}", pd.DataFrame()

    # 3. Run your Agent
    results_log, answers_payload = [], []
    print(f"Running agent on {len(questions_data)} questions...")
    
    for i, item in enumerate(questions_data):
        task_id = item.get("task_id")
        question_text = item.get("question")
        if not task_id or question_text is None: 
            continue
        
        print(f"\n--- Running Task {i+1}/{len(questions_data)} (ID: {task_id}) ---")
        
        file_url = item.get("file_url")
        
        # We simply combine the question and the URL into one string.
        # The agent's multimodal_router will handle the rest.
        if file_url:
            full_question_text = f"{question_text}\n\nHere is the relevant file: {file_url}"
            print(f"File provided: {file_url}")
        else:
            full_question_text = question_text
        
        print(f"Raw Prompt for Agent:\n{full_question_text}")

        try:
            result = agent_executor.invoke({"messages": [("user", full_question_text)]})
            raw_answer = result['messages'][-1].content
            submitted_answer = parse_final_answer(raw_answer)
            
            print(f"Raw LLM Response: '{raw_answer}'")
            print(f"PARSED FINAL ANSWER: '{submitted_answer}'")

            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
            # The log for the DataFrame no longer includes a 'File Type' column
            results_log.append({
                "Task ID": task_id, 
                "Question": question_text, 
                "File URL": file_url or "None",
                "Submitted Answer": submitted_answer
            })
            
        except Exception as e:
            print(f"!! AGENT ERROR on task {task_id}: {e}")
            error_msg = f"AGENT RUNTIME ERROR: {e}"
            answers_payload.append({"task_id": task_id, "submitted_answer": error_msg})
            results_log.append({
                "Task ID": task_id, 
                "Question": question_text, 
                "File URL": file_url or "None",
                "Submitted Answer": error_msg
            })

    if not answers_payload:
        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    # 4. Prepare and 5. Submit
    submission_data = {"username": username, "agent_code": agent_code, "answers": answers_payload}
    print(f"\nSubmitting {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()
        final_status = (f"Submission Successful!\nUser: {result_data.get('username')}\n"
                       f"Overall Score: {result_data.get('score', 'N/A')}%\n"
                       f"Processed {len([r for r in results_log if 'ERROR' not in r['Submitted Answer']])} successful tasks")
        return final_status, pd.DataFrame(results_log)
    except Exception as e:
        status_message = f"Submission Failed: {e}"
        print(status_message)
        return status_message, pd.DataFrame(results_log)

# --- Gradio UI ---
with gr.Blocks(title="Multimodal Agent Evaluation") as demo:
    gr.Markdown("# Multimodal Agent Evaluation Runner")
    gr.Markdown("This agent can process images, YouTube videos, audio files, and perform web searches.")
    
    ## MODIFICATION: Assign the LoginButton to a variable so we can use it as an input.
    login_button = gr.LoginButton()
    
    run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
    status_output = gr.Textbox(label="Run Status / Submission Result", lines=6, interactive=False)
    results_table = gr.DataFrame(
        label="Questions and Agent Answers", 
        wrap=True, 
        row_count=10,
        column_widths=[80, 250, 200, 250]
    )
    
    ## MODIFICATION: Wire the login_button as an input to the click event.
    # This correctly passes the OAuth profile to the run_and_submit_all function.
    run_button.click(
        fn=run_and_submit_all, 
        inputs=login_button, 
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    print("\n" + "-"*30 + " Multimodal App Starting " + "-"*30)
    demo.launch()