from fastapi import APIRouter, HTTPException
from fastapi.responses import StreamingResponse, JSONResponse
from huggingface_hub import hf_hub_download, snapshot_download
import os
import tempfile
import shutil
import zipfile
import io
import logging
import json
from datasets import load_dataset

router = APIRouter(tags=["download"])

@router.get("/download-dataset/{session_id}")
async def download_dataset(session_id: str):
    """
    Télécharge le dataset HuggingFace associé à une session et le renvoie au client
    
    Args:
        session_id: Identifiant de la session
        
    Returns:
        Fichier ZIP contenant le dataset
    """
    try:
        # Créer un répertoire temporaire pour stocker les fichiers du dataset
        with tempfile.TemporaryDirectory() as temp_dir:
            # Identifiant du repo HuggingFace
            repo_id = f"yourbench/yourbench_{session_id}"
            
            try:
                # Télécharger le snapshot du dataset depuis HuggingFace
                logging.info(f"Téléchargement du dataset {repo_id}")
                snapshot_path = snapshot_download(
                    repo_id=repo_id,
                    repo_type="dataset",
                    local_dir=temp_dir,
                    token=os.environ.get("HF_TOKEN")
                )
                
                logging.info(f"Dataset téléchargé dans {snapshot_path}")
                
                # Créer un fichier ZIP en mémoire
                zip_io = io.BytesIO()
                with zipfile.ZipFile(zip_io, 'w', zipfile.ZIP_DEFLATED) as zip_file:
                    # Parcourir tous les fichiers du dataset et les ajouter au ZIP
                    for root, _, files in os.walk(snapshot_path):
                        for file in files:
                            file_path = os.path.join(root, file)
                            arc_name = os.path.relpath(file_path, snapshot_path)
                            zip_file.write(file_path, arcname=arc_name)
                
                # Remettre le curseur au début du stream
                zip_io.seek(0)
                
                # Renvoyer le ZIP au client
                filename = f"yourbench_{session_id}_dataset.zip"
                return StreamingResponse(
                    zip_io,
                    media_type="application/zip",
                    headers={"Content-Disposition": f"attachment; filename={filename}"}
                )
                
            except Exception as e:
                logging.error(f"Erreur lors du téléchargement du dataset: {str(e)}")
                raise HTTPException(
                    status_code=500,
                    detail=f"Erreur lors du téléchargement du dataset: {str(e)}"
                )
    except Exception as e:
        logging.error(f"Erreur générale: {str(e)}")
        raise HTTPException(
            status_code=500, 
            detail=f"Erreur lors du téléchargement: {str(e)}"
        )

@router.get("/download-questions/{session_id}")
async def download_questions(session_id: str):
    """
    Télécharge les questions générées pour une session au format JSON
    
    Args:
        session_id: Identifiant de la session
        
    Returns:
        Fichier JSON contenant les questions générées
    """
    try:
        # Identifiant du repo HuggingFace
        dataset_repo_id = f"yourbench/yourbench_{session_id}"
        
        # Initialize questions list
        all_questions = []
        
        # Try to load single-shot questions
        try:
            single_dataset = load_dataset(dataset_repo_id, 'single_shot_questions')
            if single_dataset and len(single_dataset['train']) > 0:
                for idx in range(len(single_dataset['train'])):
                    all_questions.append({
                        "id": str(idx),
                        "question": single_dataset['train'][idx].get("question", ""),
                        "answer": single_dataset['train'][idx].get("self_answer", "No answer available"),
                        "type": "single_shot"
                    })
                logging.info(f"Loaded {len(all_questions)} single-shot questions")
        except Exception as e:
            logging.error(f"Error loading single-shot questions: {str(e)}")
        
        # Try to load multi-hop questions
        try:
            multi_dataset = load_dataset(dataset_repo_id, 'multi_hop_questions')
            if multi_dataset and len(multi_dataset['train']) > 0:
                start_idx = len(all_questions)
                for idx in range(len(multi_dataset['train'])):
                    all_questions.append({
                        "id": str(start_idx + idx),
                        "question": multi_dataset['train'][idx].get("question", ""),
                        "answer": multi_dataset['train'][idx].get("self_answer", "No answer available"),
                        "type": "multi_hop"
                    })
                logging.info(f"Loaded {len(multi_dataset['train'])} multi-hop questions")
        except Exception as e:
            logging.error(f"Error loading multi-hop questions: {str(e)}")
        
        # If we couldn't load any questions, the dataset might not exist
        if len(all_questions) == 0:
            raise HTTPException(status_code=404, detail="Aucune question trouvée pour cette session")
        
        # Convert questions to JSON
        questions_json = json.dumps({
            "session_id": session_id,
            "questions": all_questions
        }, ensure_ascii=False, indent=2)
        
        # Create a BytesIO object with the JSON data
        json_bytes = io.BytesIO(questions_json.encode('utf-8'))
        json_bytes.seek(0)
        
        # Return the JSON file for download
        filename = f"yourbench_{session_id}_questions.json"
        return StreamingResponse(
            json_bytes,
            media_type="application/json",
            headers={"Content-Disposition": f"attachment; filename={filename}"}
        )
            
    except HTTPException:
        # Re-raise HTTP exceptions
        raise
    except Exception as e:
        logging.error(f"Erreur lors de la récupération des questions: {str(e)}")
        raise HTTPException(
            status_code=500, 
            detail=f"Erreur lors du téléchargement des questions: {str(e)}"
        )