src/embeddings.py

import torch
import numpy as np
from typing import List
from transformers import CLIPModel, CLIPProcessor

class CLIPEmbedder:
    def __init__(self, model_name: str = "openai/clip-vit-base-patch32", device: str = "cpu"):
        self.device = device
        self.model_name = model_name
        
        print(f"→ Loading CLIP model: {model_name}")
        
        # Load from transformers with correct identifier
        self.model = CLIPModel.from_pretrained(model_name).to(device)
        self.processor = CLIPProcessor.from_pretrained(model_name)
        
        # Set model to eval mode
        self.model.eval()
        
        print(f"✓ CLIP model loaded on {device}")
    
    def encode_text(self, texts: List[str]) -> np.ndarray:
        """Encode text using CLIP"""
        with torch.no_grad():
            # Process texts
            inputs = self.processor(
                text=texts,
                return_tensors="pt",
                padding=True,
                truncation=True,
                max_length=77
            ).to(self.device)
            
            # Get text embeddings
            text_features = self.model.get_text_features(**inputs)
            
            # Normalize embeddings
            text_features = text_features / text_features.norm(dim=-1, keepdim=True)
        
        return text_features.cpu().numpy()
    
    def encode_single_text(self, text: str) -> np.ndarray:
        """Encode single text"""
        return self.encode_text([text])[0]
    
    def __call__(self, texts: List[str]) -> np.ndarray:
        """Make embedder callable"""
        return self.encode_text(texts)