main.py

import os
import pandas as pd
from fastapi import FastAPI, HTTPException, Body
from fastapi.responses import FileResponse
from pydantic import BaseModel, Field
from typing import List, Dict, Any, Optional, Union
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
import mimetypes
# --- Constants and Config ---
HF_DATASET_ID = "agents-course/unit4-students-scores"


logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

task_file_paths: Dict[str, str] = {}
tool_threshold = 3
step_threshold = 6
questions_for_api: List[Dict[str, Any]] = []
ground_truth_answers: Dict[str, str] = {}
filtered_dataset = None

ALLOWED_CACHE_BASE = os.path.abspath("/app/.cache")

class ErrorResponse(BaseModel):
    detail: str


def load_questions():
    """
    Loads the GAIA dataset, filters questions based on tool/step counts,
    populates 'questions_for_api' with data for the API (excluding sensitive/internal fields),
    stores ground truth answers, and maps task IDs to their local file paths on the server.
    """
    global filtered_dataset
    global questions_for_api
    global ground_truth_answers
    global task_file_paths  

    tempo_filtered = []
    questions_for_api.clear()
    ground_truth_answers.clear()
    task_file_paths.clear() 

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

    # --- Filtering Logic based on Annotator Metadata ---
    for item in dataset:
        metadata = item.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(item) 
                except ValueError:
                    logger.warning(f"Skipping Task ID: {item.get('task_id', 'N/A')} - Could not convert tool/step count in metadata: tools='{num_tools_str}', steps='{num_steps_str}'.")
            else:
                 logger.warning(f"Skipping Task ID: {item.get('task_id', 'N/A')} - 'Number of tools' or 'Number of steps' missing in Metadata.")
        else:
             logger.warning(f"Skipping Task ID: {item.get('task_id', 'N/A')} - Missing 'Annotator Metadata'.")

    filtered_dataset = tempo_filtered 
    logger.info(f"Found {len(filtered_dataset)} questions matching the criteria (tools < {tool_threshold}, steps < {step_threshold}).")

    processed_count = 0
    for item in filtered_dataset:
        # Extract data from the dataset item
        task_id = item.get('task_id')
        original_question_text = item.get('Question')
        final_answer = item.get('Final answer')
        local_file_path = item.get('file_path') 
        file_name = item.get('file_name')      

        if task_id and original_question_text and final_answer is not None:

            # 1. Create the dictionary to be exposed via the API
            #    (Includes 'file_name' for info, but excludes 'file_path')
            processed_item = {
                "task_id": str(task_id),
                "question": str(original_question_text),
                "Level": item.get("Level"),
                "file_name": file_name, 
            }
            processed_item = {k: v for k, v in processed_item.items() if v is not None}

            questions_for_api.append(processed_item)

            # 2. Store the ground truth answer separately
            ground_truth_answers[str(task_id)] = str(final_answer)

            # 3. Store the file path mapping if file details exist and are valid
            if local_file_path and file_name:
                 # Log if the path from the dataset isn't absolute (might indicate issues)
                 if not os.path.isabs(local_file_path):
                     logger.warning(f"Task {task_id}: Path '{local_file_path}' from dataset is not absolute. This might cause issues finding the file on the server.")
                    

                 if os.path.exists(local_file_path) and os.path.isfile(local_file_path):
                     task_file_paths[str(task_id)] = local_file_path
                     logger.debug(f"Stored file path mapping for task_id {task_id}: {local_file_path}")
                 else:
                     logger.warning(f"File path '{local_file_path}' for task_id {task_id} does NOT exist or is not a file on server. Mapping skipped.")
            elif task_id:
              
                 if not local_file_path and not file_name:
                     logger.debug(f"Task {task_id}: No 'file_path' or 'file_name' found in dataset item. No file mapping stored.")
                 elif not local_file_path:
                     logger.debug(f"Task {task_id}: 'file_path' is missing in dataset item (file_name: '{file_name}'). No file mapping stored.")
                 else: # Not file_name
                      logger.debug(f"Task {task_id}: 'file_name' is missing in dataset item (file_path: '{local_file_path}'). No file mapping stored.")


            processed_count += 1
        else:
            logger.warning(f"Skipping item processing due to missing essential fields: task_id={task_id}, has_question={original_question_text is not None}, has_answer={final_answer is not None}")

    logger.info(f"Successfully processed {processed_count} questions for the API.")
    logger.info(f"Stored file path mappings for {len(task_file_paths)} tasks.")

    if not questions_for_api:
         logger.error("CRITICAL: No valid questions were loaded after filtering and processing. API endpoints like /questions will fail.")
        



class Question(BaseModel):
    task_id: str
    question: str
    Level: Optional[str] = None
    file_name: Optional[str] = None 

   
# --- The rest of your 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")
    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

class AnswerItem(BaseModel):
    task_id: str
    submitted_answer: Union[str, int, float]  = Field(..., description="The agent's answer for the task_id. Accepts str, int and float")

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)
    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)
       


@app.get("/files/{task_id}",
         summary="Get Associated File by Task ID",
         description="Downloads the file associated with the given task_id, if one exists and is mapped.",
         responses={
             200: {
                 "description": "File content.",
                 "content": {"*/*": {}} 
             },
             403: {"model": ErrorResponse, "description": "Access denied (e.g., path traversal attempt)."},
             404: {"model": ErrorResponse, "description": "Task ID not found, no file associated, or file missing on server."},
             500: {"model": ErrorResponse, "description": "Server error reading file."}
         })
async def get_task_file(task_id: str):
    """
    Serves the file associated with a specific task ID.
    Includes security checks to prevent accessing arbitrary files.
    """
    logger.info(f"Request received for file associated with task_id: {task_id}")

    if task_id not in task_file_paths:
        logger.warning(f"File request failed: task_id '{task_id}' not found in file path mapping.")
        raise HTTPException(status_code=404, detail=f"No file path associated with task_id {task_id}.")

    # --- ASSIGNMENT HAPPENS HERE ---
    local_file_path = task_file_paths[task_id]
    logger.debug(f"Mapped task_id '{task_id}' to local path: {local_file_path}")

    # --- CRUCIAL SECURITY CHECK ---
    try:
       
        abs_file_path = os.path.abspath(local_file_path)
        abs_base_path = ALLOWED_CACHE_BASE # Already absolute

        if not abs_file_path.startswith(abs_base_path):
            logger.error(f"SECURITY ALERT: Path traversal attempt denied for task_id '{task_id}'. Path '{local_file_path}' resolves outside base '{abs_base_path}'.")
            raise HTTPException(status_code=403, detail="File access denied.")

        if not os.path.exists(abs_file_path) or not os.path.isfile(abs_file_path):
             logger.error(f"File not found on server for task_id '{task_id}' at expected path: {abs_file_path}")
             raise HTTPException(status_code=404, detail=f"File associated with task_id {task_id} not found on server disk.")

    except HTTPException as http_exc:
         raise http_exc
    except Exception as path_err:
         logger.error(f"Error resolving or checking path '{local_file_path}' for task_id '{task_id}': {path_err}", exc_info=True)
         raise HTTPException(status_code=500, detail="Server error validating file path.")


    mime_type, _ = mimetypes.guess_type(abs_file_path) 
    media_type = mime_type if mime_type else "application/octet-stream" 

    file_name_for_download = os.path.basename(abs_file_path)

    logger.info(f"Serving file '{file_name_for_download}' (type: {media_type}) for task_id '{task_id}' from path: {abs_file_path}")

    return FileResponse(path=abs_file_path, media_type=media_type, filename=file_name_for_download)
    
def update_huggingface_dataset(username: str, score: float, code_link: str):
    """
    Loads the dataset, updates the score and code link if the score is higher,
    and pushes back to the Hugging Face Hub.

    Args:
        username: The username of the participant.
        score: The new score achieved by the participant.
        code_link: The link to the code submission associated with this score.

    Returns:
        True if the dataset was updated and pushed, False otherwise.

    Raises:
        HTTPException: If there's an error interacting with the dataset.
    """
    try:
        # Define the expected schema including the 'code' column
        expected_columns = {
            'username': 'str',
            'score': 'float',
            'timestamp': 'str',
            'code': 'str' # Added the code column
        }

        # 1. Attempt to load the dataset
        logger.info(f"Attempting to load dataset '{HF_DATASET_ID}'...")
        ds_dict = None
        df = None
        try:
           
            ds_dict = load_dataset(HF_DATASET_ID, trust_remote_code=True) # Added trust_remote_code=True if needed
            logger.info("Dataset loaded successfully.")
            if "train" in ds_dict:
                df = ds_dict['train'].to_pandas()
            else:
                 logger.warning(f"Dataset '{HF_DATASET_ID}' loaded but no 'train' split found. Creating structure.")
                 #df = pd.DataFrame({col: pd.Series(dtype=dtype) for col, dtype in expected_columns.items()})

        except Exception as load_error:
            logger.error(f"CRITICAL: Could not load dataset '{HF_DATASET_ID}'. Error: {load_error}", exc_info=True)
            raise HTTPException(
                status_code=500,
                detail=f"Failed to load required dataset '{HF_DATASET_ID}': {load_error}"
            )
      
        for col, dtype in expected_columns.items():
            if col not in df.columns:
                logger.warning(f"Column '{col}' not found in loaded data. Adding it.")
              
                df[col] = pd.Series(dtype=dtype)

       
        df['score'] = pd.to_numeric(df['score'], errors='coerce').fillna(0.0)
        
        df['username'] = df['username'].astype(str).fillna('')
        df['timestamp'] = df['timestamp'].astype(str).fillna('')
        df['code'] = df['code'].astype(str).fillna('') 

        # 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
            max_existing_score = existing_entries['score'].max() # Already numeric
            if score > max_existing_score:
                logger.info(f"New score {score} is higher than existing max {max_existing_score} for {username}. Updating entry.")
                df = df[df['username'] != username].copy() 
                new_entry = pd.DataFrame([{
                    'username': username,
                    'score': score,
                    'timestamp': current_timestamp,
                    'code': code_link # Add the code link here
                }])
                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 with score {score}.")
            new_entry = pd.DataFrame([{
                'username': username,
                'score': score,
                'timestamp': current_timestamp,
                'code': code_link # Add the code link here
            }])
            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"Preparing to push updated dataset to '{HF_DATASET_ID}'...")

            df = df[list(expected_columns.keys())]
            for col, dtype in expected_columns.items():
                 if dtype == 'str':
                     df[col] = df[col].astype(str).fillna('')
                 elif dtype == 'float':
                     df[col] = pd.to_numeric(df[col], errors='coerce').fillna(0.0) # Ensure float conversion

            logger.info(f"Final DataFrame columns and types:\n{df.dtypes}")
            logger.info(f"Sample data before push:\n{df.head().to_string()}")

            updated_ds = Dataset.from_pandas(df)
            final_ds_dict = DatasetDict({'train': updated_ds})

            logger.info(f"Dataset structure to push: {final_ds_dict}")

            final_ds_dict.push_to_hub(HF_DATASET_ID)
            logger.warning("Dataset push to hub is currently commented out in the code.")
            return True
        else:
            logger.info("No changes needed, dataset not pushed.")
            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)
       
        raise HTTPException(status_code=500, detail=f"Failed to update Hugging Face dataset: {e}")


@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
    processed_ids = set()

    for answer_item in submission.answers:
        task_id = str(answer_item.task_id) 
        submitted = str(answer_item.submitted_answer) 

    
        if task_id in processed_ids:
             logger.warning(f"Duplicate task_id '{task_id}' in submission from {submission.username}. Skipping.")
             continue 
        processed_ids.add(task_id)


        
        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.")
            continue

       
        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}'")

    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, submission.agent_code)
        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)