import-data.py

import torch
from datasets import load_dataset
from PIL import Image
from io import BytesIO
import requests
import open_clip
import chromadb

# 1. Setup CLIP
device = "cuda" if torch.cuda.is_available() else "cpu"
model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='openai')
model = model.to(device).eval()

# 2. Load Pokémon cards dataset
dataset = load_dataset("TheFusion21/PokemonCards", split="train")

# 3. Setup ChromaDB client
client = chromadb.PersistentClient(path="./chroma_db")
collection = client.get_or_create_collection(
    name="pokemon_cards_clip",
    metadata={"hnsw:space": "cosine"}
)

# 4. Insert embeddings into ChromaDB if not already indexed
if collection.count() == 0:
    print("📦 Indexing images into ChromaDB...")

    for i, entry in enumerate(dataset):
        try:
            url = entry.get("image_url")
            if not url:
                continue

            # Téléchargement et prétraitement de l'image
            response = requests.get(url, timeout=10)
            image = Image.open(BytesIO(response.content)).convert("RGB")
            image_input = preprocess(image).unsqueeze(0).to(device)

            # Extraction de l'embedding
            with torch.no_grad():
                image_features = model.encode_image(image_input)
                image_features /= image_features.norm(dim=-1, keepdim=True)

            # Ajout dans la collection
            collection.add(
                ids=[f"card-{i}"],
                embeddings=[image_features.squeeze().cpu().tolist()],
                metadatas=[{
                    "name": entry.get("name", ""),
                    "set": entry.get("set_name", ""),
                    "hp": entry.get("hp", ""),
                    "image_url": url,
                    "caption": entry.get("caption", "")
                }]
            )

            if i % 100 == 0:
                print(f"✅ {i} cartes indexées...")

        except Exception as e:
            print(f"❌ Erreur sur carte {i}: {e}")

    print("✅ Indexation terminée.")

else:
    print("✅ Les embeddings sont déjà présents dans ChromaDB.")