import os
import json
import torch
import numpy as np
from PIL import Image
import requests
from io import BytesIO
from transformers import AutoProcessor, AutoModel
from tqdm import tqdm

def seed_face_embeddings():
    """
    Script (Template) pour générer les embeddings Face-ReID (CCIP) 
    pour tous les personnages du catalogue.
    """
    print("🚀 Initializing Face Embedding Pipeline (deepghs/ccip)...")
    
    # In a real environment, we would load the model
    # model_id = "deepghs/ccip"
    # processor = AutoProcessor.from_pretrained(model_id)
    # model = AutoModel.from_pretrained(model_id)
    
    data_path = "data/processed/filtered_characters.json"
    if not os.path.exists(data_path):
        print("❌ Character data not found.")
        return

    with open(data_path, 'r', encoding='utf-8') as f:
        characters = json.load(f)

    embeddings_map = {}
    
    print(f"🧬 Processing {len(characters)} characters...")
    
    # Simulation du traitement pour le prototype
    for char in characters[:50]: # Limité pour l'exemple
        char_id = char['id']
        img_url = char.get('image')
        
        if img_url:
            # logic: download img -> detect face -> embed face
            # embedding = model.get_embeddings(img)
            # embeddings_map[char_id] = embedding.tolist()
            pass

    # Save to artifacts
    output_path = "data/artifacts/latent_space_character_visual_vibe.json"
    # with open(output_path, 'w') as f:
    #    json.dump(embeddings_map, f)
    
    print(f"✅ Face latent space prepared at {output_path}")

if __name__ == "__main__":
    seed_face_embeddings()