main.py

# Import necessary libraries (ensure all required imports are at the top)
import os
import pandas as pd
from fastapi import FastAPI, HTTPException, Body
from pydantic import BaseModel, Field
from typing import List, Dict, Any #<-- Make sure Any is imported
from datasets import load_dataset, Dataset, DatasetDict
from huggingface_hub import HfApi, hf_hub_download
from datetime import datetime, timezone
import logging
import uvicorn
import random

# --- Constants and Config ---
tool_threshold = 3
step_threshold = 5
HF_DATASET_ID = "agents-course/unit4-students-scores"

# --- Data Structures ---
# questions_for_api will now hold richer dictionaries
questions_for_api: List[Dict[str, Any]] = []
ground_truth_answers: Dict[str, str] = {}

# --- Logging Setup ---
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# --- Filtered Dataset Placeholder ---
# Note: Making filtered_dataset global might not be ideal in larger apps,
# but keeping it as is based on the original code.
filtered_dataset = None

# --- Modified load_questions Function ---
def load_questions():
    global filtered_dataset
    global questions_for_api
    global ground_truth_answers
    tempo_filtered=[]
    # Clear existing data
    questions_for_api.clear()
    ground_truth_answers.clear()

    logger.info("Starting to load and filter GAIA dataset...")
    try:
        # Load the 'validation' split specifically if that's intended
        dataset = load_dataset("gaia-benchmark/GAIA", "2023_level1", split='validation', trust_remote_code=True)
        logger.info("GAIA dataset validation split loaded.")
    except Exception as e:
        logger.error(f"Failed to load GAIA dataset validation split: {e}", exc_info=True)
        raise RuntimeError("Could not load the primary GAIA dataset.") from e

    # --- Filtering Logic (remains the same) ---
    for question in dataset: # Iterate directly over the loaded split
        metadata = question.get('Annotator Metadata')

        if metadata:
            num_tools_str = metadata.get('Number of tools')
            num_steps_str = metadata.get('Number of steps')

            if num_tools_str is not None and num_steps_str is not None:
                try:
                    num_tools = int(num_tools_str)
                    num_steps = int(num_steps_str)

                    if num_tools < tool_threshold and num_steps < step_threshold:
                        tempo_filtered.append(question)
                except ValueError:
                     logger.warning(f"Skipping Task ID: {question.get('task_id', 'N/A')} - Could not convert tool/step count: tools='{num_tools_str}', steps='{num_steps_str}'.")
            # else: # Optional: Log if numbers are missing
                # logger.debug(f"Skipping Task ID: {question.get('task_id', 'N/A')} - Missing tool/step count in metadata.")
        else:
             logger.warning(f"Skipping Task ID: {question.get('task_id', 'N/A')} - Missing 'Annotator Metadata'.")

    # Store the filtered list (optional, could process directly)
    filtered_dataset = tempo_filtered
    logger.info(f"Found {len(filtered_dataset)} questions matching the criteria (tools < {tool_threshold}, steps < {step_threshold}).")

    # --- Processing Logic (Modified) ---
    processed_count = 0
    for item in filtered_dataset:
        task_id = item.get('task_id')
        question_text = item.get('Question') # Keep original key for now
        final_answer = item.get('Final answer')

        # Validate required fields needed for processing/scoring
        if task_id and question_text and final_answer is not None:
            # Create a copy to avoid modifying the original item in filtered_dataset
            processed_item: Dict[str, Any] = item.copy()

            # Remove the fields we explicitly want to exclude
            processed_item.pop('Final answer', None)
            processed_item.pop('Annotator Annotation', None)
            # You could add more fields to pop here if needed later
            # processed_item.pop('Another field to remove', None)

            # Store the dictionary containing all remaining fields
            questions_for_api.append(processed_item)

            # Store the ground truth answer separately for scoring
            ground_truth_answers[str(task_id)] = str(final_answer)
            processed_count += 1
        else:
            # Log which required field was missing if possible
            missing = [k for k, v in {'task_id': task_id, 'Question': question_text, 'Final answer': final_answer}.items() if not v and v is not None]
            logger.warning(f"Skipping item due to missing required fields ({', '.join(missing)}): task_id={task_id}")

    logger.info(f"Successfully processed {processed_count} questions into API format.")

    if not questions_for_api:
         logger.error("CRITICAL: No valid questions loaded after filtering. API endpoints needing questions will fail.")
         # raise RuntimeError("Failed to load mandatory question data after filtering.")

# --- Pydantic Models ---
# Keep Question simple for potential internal use or basic validation,
# but the API will return Dict[str, Any]
class Question(BaseModel):
    task_id: str
    Question: str # Keep original casing if that's what in the data

# Keep other models as they are (AnswerItem, Submission, ScoreResponse, ErrorResponse)
# ... (rest of the Pydantic models remain the same) ...
class AnswerItem(BaseModel):
    task_id: str
    submitted_answer: str = Field(..., description="The agent's answer for the task_id")

class Submission(BaseModel):
    username: str = Field(..., description="Hugging Face username", min_length=1)
    agent_code: str = Field(..., description="The Python class code for the agent", min_length=10) # Basic check
    answers: List[AnswerItem] = Field(..., description="List of answers submitted by the agent")

class ScoreResponse(BaseModel):
    username: str
    score: float
    correct_count: int
    total_attempted: int
    message: str
    timestamp: str

class ErrorResponse(BaseModel):
    detail: str


# --- FastAPI Application ---
app = FastAPI(
    title="Agent Evaluation API",
    description="API to fetch questions and submit agent answers for scoring.",
)

# --- Startup Event ---
@app.on_event("startup")
async def startup_event():
    logger.info("Application startup: Loading questions...")
    try:
        load_questions()
        if not questions_for_api:
            logger.error("CRITICAL: No questions were loaded during startup.")
        else:
            logger.info(f"Successfully loaded {len(questions_for_api)} questions.")
    except Exception as e:
        logger.error(f"CRITICAL ERROR DURING STARTUP while loading questions: {e}", exc_info=True)
        # import sys
        # sys.exit(1) # Consider exiting if questions are critical

# --- Helper Function (update_huggingface_dataset remains the same) ---
# ... (update_huggingface_dataset function code) ...
def update_huggingface_dataset(username: str, score: float):
    """Loads the dataset, updates the score if higher, and pushes back."""
    try:
        # 1. Load the dataset
        logger.info(f"Loading dataset '{HF_DATASET_ID}'...")
        ds_dict = None
        try:
            # Use hf_hub_download to check if the parquet file exists, avoiding full dataset load error if empty
            # This assumes the dataset uses the default 'train' split and parquet format. Adjust if needed.
            hf_hub_download(repo_id=HF_DATASET_ID, filename="data/train-00000-of-00001.parquet", repo_type="dataset")
            ds_dict = load_dataset(HF_DATASET_ID)
            logger.info("Dataset loaded successfully.")
            if "train" not in ds_dict:
                 logger.warning(f"Dataset '{HF_DATASET_ID}' does not contain a 'train' split. Creating one.")
                 df = pd.DataFrame({'username': pd.Series(dtype='str'),
                                     'score': pd.Series(dtype='float'),
                                     'timestamp': pd.Series(dtype='str')})
            else:
                # Convert the 'train' split to a pandas DataFrame for easier manipulation
                 df = ds_dict['train'].to_pandas()

        except Exception as load_error: # Catch broad exception for file not found or other loading issues
            logger.warning(f"Could not load dataset '{HF_DATASET_ID}' or it might be empty/new ({load_error}). Creating structure.")
            # Create an empty DataFrame with the correct schema
            df = pd.DataFrame({'username': pd.Series(dtype='str'),
                                 'score': pd.Series(dtype='float'),
                                 'timestamp': pd.Series(dtype='str')})


        # Ensure columns exist, add if they don't
        for col, dtype in [('username', 'str'), ('score', 'float'), ('timestamp', 'str')]:
             if col not in df.columns:
                  logger.warning(f"Column '{col}' not found in dataset. Adding it.")
                  df[col] = pd.Series(dtype=dtype)


        # Convert score column to numeric, coercing errors
        df['score'] = pd.to_numeric(df['score'], errors='coerce')
        # Fill potential NaN values in score with 0.0 before comparison/aggregation
        df['score'] = df['score'].fillna(0.0)


        # 2. Find existing score for the user
        existing_entries = df[df['username'] == username]
        current_timestamp = datetime.now(timezone.utc).isoformat()
        needs_update = False

        if not existing_entries.empty:
            # User exists, find their highest score
            # Handle potential NaN scores from coercion or previous bad data (though fillna above should help)
            max_existing_score = existing_entries['score'].max()
            if score > max_existing_score:
                logger.info(f"New score {score} is higher than existing max {max_existing_score} for {username}. Updating.")
                # Remove old entries for this user
                df = df[df['username'] != username]
                # Add new entry
                new_entry = pd.DataFrame([{'username': username, 'score': score, 'timestamp': current_timestamp}])
                df = pd.concat([df, new_entry], ignore_index=True)
                needs_update = True
            else:
                logger.info(f"New score {score} is not higher than existing max {max_existing_score} for {username}. No update needed.")
        else:
            # User does not exist, add them
            logger.info(f"User {username} not found. Adding new entry.")
            new_entry = pd.DataFrame([{'username': username, 'score': score, 'timestamp': current_timestamp}])
            df = pd.concat([df, new_entry], ignore_index=True)
            needs_update = True

        # 3. Push updated data back to Hugging Face Hub if changes were made
        if needs_update:
            logger.info(f"Pushing updated dataset to '{HF_DATASET_ID}'...")
            # Convert potentially modified DataFrame back to a Dataset object
            # Ensure the schema matches if columns were added/modified.
            # Use 'train' split convention.
            # Make sure the dtypes are correct before creating the Dataset
            df['username'] = df['username'].astype(str)
            df['score'] = df['score'].astype(float)
            df['timestamp'] = df['timestamp'].astype(str)

            updated_ds = DatasetDict({'train': Dataset.from_pandas(df)})
            logger.info(f"Dataset to push: {updated_ds}") # Log the dataset structure
            # updated_ds.push_to_hub(HF_DATASET_ID) # Uncomment this line to enable leaderboard updates
            logger.warning("Dataset push to hub is currently commented out. Uncomment the line above to enable leaderboard updates.") # REMINDER
            logger.info("Dataset push simulated/attempted.")
            return True
        else:
            return False # No update was pushed

    except Exception as e:
        logger.error(f"Error interacting with Hugging Face dataset '{HF_DATASET_ID}': {e}", exc_info=True)
        # Re-raise the exception to be caught by the endpoint handler
        raise HTTPException(status_code=500, detail=f"Failed to update Hugging Face dataset: {e}")

# --- API Endpoints (Modified response_model) ---

@app.get("/questions",
         # Return a list of dictionaries with arbitrary keys/values
         response_model=List[Dict[str, Any]],
         summary="Get All Filtered Questions (Full Data)",
         description="Returns the complete list of questions with all associated data (excluding answer/annotation) filtered based on criteria.")
async def get_questions():
    """
    Provides the list of questions (with extended data) that agents should answer.
    """
    if not questions_for_api:
         logger.error("GET /questions requested but no questions are loaded.")
         raise HTTPException(status_code=404, detail="No questions available.")
    # questions_for_api now contains the richer dictionaries
    return questions_for_api

@app.get("/random-question",
         # Return a single dictionary with arbitrary keys/values
         response_model=Dict[str, Any],
         summary="Get One Random Question (Full Data)",
         description="Returns a single random question with all associated data (excluding answer/annotation) from the available filtered set.",
         responses={
             200: {"description": "A random question with its full data."},
             404: {"model": ErrorResponse, "description": "No questions available to choose from."}
         })
async def get_random_question():
    """
    Provides a single, randomly selected question with its extended data.
    """
    if not questions_for_api:
        logger.warning("GET /random-question requested but no questions are loaded.")
        raise HTTPException(status_code=404, detail="No questions available to choose from.")

    # Select and return a random question dictionary
    random_question = random.choice(questions_for_api)
    logger.info(f"Returning random question with task_id: {random_question.get('task_id', 'N/A')}")
    # random_question is already the richer dictionary
    return random_question

# --- Submit Endpoint (remains the same, uses ground_truth_answers) ---
@app.post("/submit",
          response_model=ScoreResponse,
          summary="Submit Agent Answers",
          description="Submit answers from an agent, calculate score, and update leaderboard on Hugging Face.",
          responses={
              200: {"description": "Submission successful, score calculated."},
              400: {"model": ErrorResponse, "description": "Invalid input data."},
              404: {"model": ErrorResponse, "description": "Task ID not found in submission or ground truth."},
              500: {"model": ErrorResponse, "description": "Server error (e.g., failed to update dataset)."}
          })
async def submit_answers(submission: Submission = Body(...)):
    """
    Receives agent submissions:
    - Validates input.
    - Checks presence of agent code (basic anti-cheat).
    - Calculates score based on submitted answers vs ground truth.
    - Updates the score on the Hugging Face dataset if it's a new high score for the user.
    """
    logger.info(f"Received submission from username: {submission.username}")

    # Basic check for agent code presence
    if not submission.agent_code or len(submission.agent_code.strip()) < 10:
        logger.warning(f"Submission rejected for {submission.username}: Agent code missing or too short.")
        raise HTTPException(status_code=400, detail="Agent code is required and must be sufficiently long.")

    if not submission.answers:
         logger.warning(f"Submission rejected for {submission.username}: No answers provided.")
         raise HTTPException(status_code=400, detail="No answers provided in the submission.")


    correct_count = 0
    total_attempted_in_payload = len(submission.answers)
    valid_attempted_count = 0 # Count attempts where task_id was valid
    processed_ids = set()

    for answer_item in submission.answers:
        task_id = str(answer_item.task_id) # Ensure string comparison
        submitted = str(answer_item.submitted_answer) # Ensure string comparison

        # Prevent duplicate task_id submissions in the same request
        if task_id in processed_ids:
             logger.warning(f"Duplicate task_id '{task_id}' in submission from {submission.username}. Skipping.")
             continue # Don't count this as an attempt for scoring
        processed_ids.add(task_id)


        # Check if task_id is valid (exists in our loaded ground truth)
        if task_id not in ground_truth_answers:
            logger.warning(f"Task ID '{task_id}' submitted by {submission.username} not found in ground truth list. Skipping this answer.")
            # Don't count this as a valid attempt for score calculation
            continue

        # If we reach here, the task_id is valid
        valid_attempted_count += 1
        ground_truth = ground_truth_answers[task_id]
        # Compare answers (case-insensitive, strip whitespace)
        if submitted.strip().lower() == ground_truth.strip().lower():
            correct_count += 1
            logger.debug(f"Correct answer for {task_id} from {submission.username}")
        else:
             logger.debug(f"Incorrect answer for {task_id} from {submission.username}. Submitted: '{submitted}', Expected: '{ground_truth}'")


    # Calculate score based on valid attempts AND total number of questions available
    if valid_attempted_count == 0:
        score = 0.0
        message = f"Submission received, but no valid/matching task IDs were found in the {total_attempted_in_payload} answers provided."
        logger.warning(f"No valid answers processed for {submission.username} out of {total_attempted_in_payload} submitted.")
    elif not ground_truth_answers: # Prevent division by zero if no questions loaded
         score = 0.0
         message = "Score cannot be calculated because no ground truth answers are loaded."
         logger.error(f"Cannot calculate score for {submission.username}: ground_truth_answers is empty.")
    else:
        # Score is based on correct answers divided by the TOTAL number of questions in the filtered set
        score = round((correct_count / len(ground_truth_answers)) * 100, 2)
        message = f"Score calculated successfully: {correct_count}/{len(ground_truth_answers)} total questions answered correctly ({valid_attempted_count} valid tasks attempted)."
        if valid_attempted_count < total_attempted_in_payload:
             message += f" ({total_attempted_in_payload - valid_attempted_count} submitted answers had invalid or duplicate task IDs)."
        logger.info(f"Score for {submission.username}: {score}% ({correct_count}/{len(ground_truth_answers)} correct, based on {valid_attempted_count} valid attempts)")


    # Update Hugging Face dataset
    try:
        updated = update_huggingface_dataset(submission.username, score)
        if updated:
             message += " High score updated on leaderboard."
             logger.info(f"Leaderboard updated for {submission.username}.")
        else:
             message += " Score did not improve previous record, leaderboard not updated."
             logger.info(f"Leaderboard not updated for {submission.username} as score was not higher.")

    except HTTPException as http_exc:
         # Propagate HTTPException from the helper function (e.g., 500 error)
         raise http_exc
    except Exception as e:
         # Catch any other unexpected errors during HF update
         logger.error(f"Unexpected error during dataset update for {submission.username}: {e}", exc_info=True)
         raise HTTPException(status_code=500, detail="An unexpected error occurred while updating the leaderboard.")


    return ScoreResponse(
        username=submission.username,
        score=score,
        correct_count=correct_count,
        # Return the count of *valid* attempts for clarity
        total_attempted=valid_attempted_count,
        message=message,
        timestamp=datetime.now(timezone.utc).isoformat()
    )

# --- Run the application ---
if __name__ == "__main__":
    logger.info("Starting FastAPI server for local development...")
    try:
        load_questions() # Load questions before starting server
        if not questions_for_api:
             logger.error("EXITING: Cannot start server without loaded questions.")
             # Optional: exit if questions are essential
             # import sys
             # sys.exit(1)
        else:
            local_port = int(os.getenv("PORT", "8000"))
            logger.info(f"Running Uvicorn locally on http://127.0.0.1:{local_port}")
            uvicorn.run(app, host="127.0.0.1", port=local_port, log_level="info")
    except Exception as e:
        logger.error(f"Failed to start server: {e}", exc_info=True)