"""
app.py

This script provides the Gradio web interface to run the evaluation.
This version has been corrected to be "file-aware" by checking for a 'file_url'
in the task data and appending it to the agent's prompt.
"""

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

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 (This is correct) ---
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") # Using Google for better tool use
    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}) ---")
        
        # --- THIS IS THE CRITICAL FIX ---
        # 1. Check if a 'file_url' key exists in the task data.
        file_url = item.get("file_url")
        full_question_text = question_text

        # 2. If a URL exists, append it to the question text.
        if file_url:
            print(f"File found for this task: {file_url}")
            # This gives the agent the context it needs to call the right tool.
            full_question_text = f"{question_text}\n\n[Attachment URL: {file_url}]"
        
        print(f"Full Prompt for Agent:\n{full_question_text}")
        # --- END CRITICAL FIX ---

        try:
            # 3. Pass the full, potentially enriched, question text to the agent.
            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})
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
        except Exception as e:
             print(f"!! AGENT ERROR on task {task_id}: {e}")
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT RUNTIME ERROR: {e}"})

    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')}\nOverall Score: {result_data.get('score', 'N/A')}%")
        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 (This part is correct) ---
with gr.Blocks() as demo:
    gr.Markdown("# Agent Evaluation Runner")
    # ... (rest of the Gradio code is fine)
    gr.LoginButton()
    run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
    status_output = gr.Textbox(label="Run Status / Submission Result", lines=4, interactive=False)
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True, row_count=10)
    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])

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