import gradio as gr
from transformers import CLIPProcessor, CLIPModel
from PIL import Image
import torch
import pickle
from pathlib import Path
import os
import spaces

# Load model/processor
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
model.eval()

DATASET_DIR = Path("dataset")
CACHE_FILE = "cache.pkl"

def get_embedding(image: Image.Image, device="cpu"):
    # Use CLIP's built-in preprocessing instead of custom resize
    inputs = processor(images=image, return_tensors="pt").to(device)
    model_device = model.to(device)
    with torch.no_grad():
        emb = model_device.get_image_features(**inputs)
    # L2 normalize the embeddings
    emb = emb / emb.norm(p=2, dim=-1, keepdim=True)
    return emb
    
def get_reference_embeddings():
    if os.path.exists(CACHE_FILE):
        with open(CACHE_FILE, "rb") as f:
            return pickle.load(f)

    embeddings = {}
    # Use GPU for preprocessing reference images too for consistency
    device = "cuda" if torch.cuda.is_available() else "cpu"
    
    for img_path in DATASET_DIR.glob("*.jpg"):
        img = Image.open(img_path).convert("RGB")
        emb = get_embedding(img, device=device)
        # Store on CPU to save GPU memory
        embeddings[img_path.name] = emb.cpu()
    
    with open(CACHE_FILE, "wb") as f:
        pickle.dump(embeddings, f)
    return embeddings

reference_embeddings = get_reference_embeddings()

@spaces.GPU
def search_similar(query_img):
    query_emb = get_embedding(query_img, device="cuda")
    results = []
    
    for name, ref_emb in reference_embeddings.items():
        # Move reference embedding to same device as query
        ref_emb_gpu = ref_emb.to("cuda")
        # Compute cosine similarity
        sim = torch.nn.functional.cosine_similarity(query_emb, ref_emb_gpu, dim=1).item()
        results.append((name, sim))
    
    results.sort(key=lambda x: x[1], reverse=True)
    return [(f"dataset/{name}", f"Score: {score:.4f}") for name, score in results[:5]]

@spaces.GPU
def add_image(name: str, image):
    path = DATASET_DIR / f"{name}.jpg"
    image.save(path)
    
    # Use GPU for consistency if available
    device = "cuda" if torch.cuda.is_available() else "cpu"
    emb = get_embedding(image, device=device)
    
    # Store on CPU to save memory
    reference_embeddings[f"{name}.jpg"] = emb.cpu()
    
    with open(CACHE_FILE, "wb") as f:
        pickle.dump(reference_embeddings, f)
    return f"Image {name} added to dataset."

search_interface = gr.Interface(fn=search_similar,
                                inputs=gr.Image(type="pil", label="Query Image"),
                                outputs=gr.Gallery(label="Top Matches", columns=5),
                                allow_flagging="never")

add_interface = gr.Interface(fn=add_image,
                             inputs=[gr.Text(label="Image Name"), gr.Image(type="pil", label="Product Image")],
                             outputs="text",
                             allow_flagging="never")

demo = gr.TabbedInterface([search_interface, add_interface], tab_names=["Search", "Add Product"])
demo.launch()