# app.py
"""
This script provides the Gradio web interface to run the evaluation.
## MODIFICATION: This version is simplified to work with the new agent architecture.
It no longer performs file-type detection or prompt enhancement, as that responsibility
has been moved into the agent's 'multimodal_router'.
"""

import os
import re
import gradio as gr
import requests
import pandas as pd
# --- Import HumanMessage ---
from langchain_core.messages import HumanMessage

from agent import create_agent_executor

# --- Constants ---
# Ensure the URL is correctly formatted (remove trailing spaces)
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

# --- Helper function to parse the agent's output (remains the same) ---
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."

## MODIFICATION: The `detect_file_type` function has been removed.
## It is now redundant as this logic is handled inside the agent.

## MODIFICATION: The `create_enhanced_prompt` function has been removed.
## It was causing errors by trying to instruct the agent to use tools that no longer exist.
## The agent is now responsible for handling the raw input itself.

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}")
    
    # --- Fix SPACE_ID retrieval and URL construction ---
    # Ensure SPACE_ID environment variable is set correctly in your Hugging Face Space.
    space_id = os.getenv("SPACE_ID")
    if not space_id:
         # Fallback or error handling if SPACE_ID is not set
         # You might need to adjust this based on how your space is configured
         # For example, if running locally, you might not have SPACE_ID.
         # This is a placeholder; adjust as needed.
         # Consider using a default or making it configurable.
         space_id = "your-username/your-space-name" # Example placeholder
         print(f"Warning: SPACE_ID environment variable not found. Using placeholder: {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")
        
        ## MODIFICATION: Prompt creation is now much simpler.
        # We just 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:
            # --- FIX: Pass a list of HumanMessage objects ---
            # The agent expects MessagesState["messages"] to be a list of BaseMessage objects.
            input_state = {"messages": [HumanMessage(content=full_question_text)]}
            result = agent_executor.invoke(input_state)
            
            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, 
                "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 (remains the same)
    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 (remains largely the same) ---
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.")
    
    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,
        # MODIFICATION: Removed the 'File Type' column as it's no longer detected here.
        # Adjust column widths if necessary based on actual content/columns
        # column_widths=[80, 250, 200, 250] 
    )
    
    # We also remove "File Type" from the results_log being displayed
    # (Though it's not in the log anymore, this is a safe check)
    def display_wrapper(profile):
        status, df = run_and_submit_all(profile)
        # Ensure df is a DataFrame before attempting operations
        if isinstance(df, pd.DataFrame) and "File Type" in df.columns:
            df = df.drop(columns=["File Type"])
        return status, df

    run_button.click(fn=display_wrapper, outputs=[status_output, results_table])

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