"""
Agent Evaluation Runner
======================
This module implements a framework for evaluating LLM agents against a set of questions
and submitting the results to a scoring server.

Main components:
- BasicAgent: The agent implementation that processes questions
- Evaluation functions: For running and submitting results
- Gradio interface: For user interaction
"""

import os
import logging
from typing import Tuple, List, Dict, Any, Optional

import gradio as gr
import requests
import pandas as pd
from langchain_core.messages import HumanMessage

from agent import build_graph

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(levelname)s - %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S"
)
logger = logging.getLogger(__name__)

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
REQUEST_TIMEOUT = 60  # seconds


class BasicAgent:
    """
    A LangGraph-based agent that answers questions using a graph-based workflow.
    
    This agent takes natural language questions, processes them through a 
    predefined graph workflow, and returns the answer.
    
    Attributes:
        graph: The LangGraph workflow that processes the questions
    """
    
    def __init__(self):
        """Initialize the agent with a graph-based workflow."""
        logger.info("Initializing BasicAgent")
        self.graph = build_graph()
        
    def __call__(self, question: str) -> str:
        """
        Process a question and return an answer.
        
        Args:
            question: The natural language question to process
            
        Returns:
            The agent's answer to the question
        """
        logger.info(f"Processing question (first 50 chars): {question[:50]}...")
        
        # Wrap the question in a HumanMessage from langchain_core
        messages = [HumanMessage(content=question)]
        
        # Process through the graph
        messages = self.graph.invoke({"messages": messages})
        
        # Extract and clean the answer
        answer = messages['messages'][-1].content
        
        # Remove the "FINAL ANSWER:" prefix if present
        return answer[14:] if answer.startswith("FINAL ANSWER:") else answer


def fetch_questions(api_url: str) -> List[Dict[str, Any]]:
    """
    Fetch questions from the evaluation server.
    
    Args:
        api_url: Base URL of the evaluation API
        
    Returns:
        List of question data dictionaries
        
    Raises:
        requests.exceptions.RequestException: If there's an error fetching questions
    """
    questions_url = f"{api_url}/questions"
    logger.info(f"Fetching questions from: {questions_url}")
    
    response = requests.get(questions_url, timeout=REQUEST_TIMEOUT)
    response.raise_for_status()
    
    questions_data = response.json()
    if not questions_data:
        raise ValueError("Fetched questions list is empty or invalid format")
    
    logger.info(f"Successfully fetched {len(questions_data)} questions")
    return questions_data


def run_agent_on_questions(
    agent: BasicAgent,
    questions_data: List[Dict[str, Any]]
) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
    """
    Run the agent on a list of questions.
    
    Args:
        agent: The agent to run
        questions_data: List of question data dictionaries
        
    Returns:
        Tuple of (answers_payload, results_log)
    """
    results_log = []
    answers_payload = []
    
    logger.info(f"Running agent on {len(questions_data)} questions...")
    
    for item in questions_data:
        task_id = item.get("task_id")
        question_text = item.get("question")
        
        if not task_id or question_text is None:
            logger.warning(f"Skipping item with missing task_id or question: {item}")
            continue
            
        try:
            submitted_answer = agent(question_text)
            
            # Prepare answer for submission
            answers_payload.append({
                "task_id": task_id,
                "submitted_answer": submitted_answer
            })
            
            # Log result for display
            results_log.append({
                "Task ID": task_id,
                "Question": question_text,
                "Submitted Answer": submitted_answer
            })
            
        except Exception as e:
            logger.error(f"Error running agent on task {task_id}: {e}", exc_info=True)
            
            # Log error in results
            results_log.append({
                "Task ID": task_id,
                "Question": question_text,
                "Submitted Answer": f"AGENT ERROR: {e}"
            })
    
    return answers_payload, results_log


def submit_answers(
    api_url: str,
    username: str,
    agent_code: str,
    answers_payload: List[Dict[str, Any]]
) -> Dict[str, Any]:
    """
    Submit answers to the evaluation server.
    
    Args:
        api_url: Base URL of the evaluation API
        username: Hugging Face username
        agent_code: URL to the agent code repository
        answers_payload: List of answer dictionaries
        
    Returns:
        Response data from the server
        
    Raises:
        requests.exceptions.RequestException: If there's an error during submission
    """
    submit_url = f"{api_url}/submit"
    
    # Prepare submission data
    submission_data = {
        "username": username.strip(),
        "agent_code": agent_code,
        "answers": answers_payload
    }
    
    logger.info(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    
    # Submit answers
    response = requests.post(submit_url, json=submission_data, timeout=REQUEST_TIMEOUT)
    response.raise_for_status()
    
    result_data = response.json()
    logger.info("Submission successful")
    
    return result_data


def run_and_submit_all(profile: Optional[gr.OAuthProfile] = None) -> Tuple[str, pd.DataFrame]:
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    
    Args:
        profile: Gradio OAuth profile containing user information
        
    Returns:
        Tuple of (status_message, results_dataframe)
    """
    # Check if user is logged in
    if not profile:
        logger.warning("User not logged in")
        return "Please Login to Hugging Face with the button.", None
    
    username = profile.username
    logger.info(f"User logged in: {username}")
    
    # Get the space ID for linking to code
    space_id = os.getenv("SPACE_ID")
    api_url = DEFAULT_API_URL
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    
    try:
        # 1. Instantiate Agent
        agent = BasicAgent()
        
        # 2. Fetch Questions
        questions_data = fetch_questions(api_url)
        
        # 3. Run Agent on Questions
        answers_payload, results_log = run_agent_on_questions(agent, questions_data)
        
        if not answers_payload:
            logger.warning("Agent did not produce any answers to submit")
            return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
        
        # 4. Submit Answers
        result_data = submit_answers(api_url, username, agent_code, answers_payload)
        
        # 5. Format and Return Results
        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.')}"
        )
        
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
        
    except requests.exceptions.HTTPError as e:
        # Handle HTTP errors with detailed error information
        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}"
        logger.error(status_message)
        
        results_df = pd.DataFrame(results_log if 'results_log' in locals() else [])
        return status_message, results_df
        
    except requests.exceptions.Timeout:
        status_message = f"Submission Failed: The request timed out after {REQUEST_TIMEOUT} seconds"
        logger.error(status_message)
        
        results_df = pd.DataFrame(results_log if 'results_log' in locals() else [])
        return status_message, results_df
        
    except Exception as e:
        status_message = f"An unexpected error occurred: {str(e)}"
        logger.error(status_message, exc_info=True)
        
        results_df = pd.DataFrame(results_log if 'results_log' in locals() else [])
        return status_message, results_df


def create_gradio_interface() -> gr.Blocks:
    """
    Create and configure the Gradio interface.
    
    Returns:
        Configured Gradio Blocks interface
    """
    with gr.Blocks() as demo:
        gr.Markdown("# Agent Evaluation Runner")
        gr.Markdown(
            """
            ## Instructions
            
            1. **Clone this space** and modify the code to define your agent's logic, tools, and dependencies
            2. **Log in to your Hugging Face account** using the button below (required for submission)
            3. **Run Evaluation** to fetch questions, run your agent, and submit answers
            
            ## Important Notes
            
            - The evaluation process may take several minutes to complete
            - This agent framework is intentionally minimal to allow for your own improvements
            - Consider implementing caching or async processing for better performance
            """
        )

        gr.LoginButton()

        run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary")

        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]
        )
        
    return demo


def check_environment() -> None:
    """
    Check and log environment variables at startup.
    """
    logger.info("-" * 30 + " App Starting " + "-" * 30)
    
    # Check for SPACE_HOST
    space_host = os.getenv("SPACE_HOST")
    if space_host:
        logger.info(f"✅ SPACE_HOST found: {space_host}")
        logger.info(f"   Runtime URL should be: https://{space_host}.hf.space")
    else:
        logger.info("ℹ️  SPACE_HOST environment variable not found (running locally?).")
    
    # Check for SPACE_ID
    space_id = os.getenv("SPACE_ID")
    if space_id:
        logger.info(f"✅ SPACE_ID found: {space_id}")
        logger.info(f"   Repo URL: https://huggingface.co/spaces/{space_id}")
        logger.info(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id}/tree/main")
    else:
        logger.info("ℹ️  SPACE_ID environment variable not found (running locally?).")
    
    logger.info("-" * (60 + len(" App Starting ")) + "\n")


if __name__ == "__main__":
    # Check environment at startup
    check_environment()
    
    # Create and launch Gradio interface
    logger.info("Launching Gradio Interface for Agent Evaluation...")
    demo = create_gradio_interface()
    demo.launch(debug=True, share=False)