app.py

import os
import sys
import gradio as gr
import numpy as np
from PIL import Image
from embedding import get_device, get_model_and_processor, to_embedding
from datasets import Dataset, load_dataset

def initialize_model():
    device = get_device()
    model, processor = get_model_and_processor("patrickjohncyh/fashion-clip", device)
    ref_dataset = load_dataset("HadrienCr/embeddeDior", split="train")
    ref_dataset = ref_dataset.add_faiss_index("embeddings")
    return model, processor, ref_dataset, device

def search(
    image: np.ndarray,
    reference_dataset: Dataset,
    model,
    processor,
    device: str,
    num_neighbors: int = 4,
):
    """a function that embeds a new image and returns the most probable results"""
    scores, retrieved_examples = reference_dataset.get_nearest_examples(
        "embeddings",
        to_embedding(np.expand_dims(image, 0), processor, model, device),
        k=num_neighbors,
    )
    return retrieved_examples

def process_image(image, num_results, remove_bg, model, processor, ref_dataset, device):
    if image is None:
        return []  # Return an empty list if no image is provided
    
    # Ensure the input image is a numpy array
    if isinstance(image, Image.Image):
        image = np.array(image)
    
    # Handle background removal
    if remove_bg:
        from rembg import remove
        image = remove(image)[:,:,0:3]
    # Perform the search
    results = search(
        image,
        ref_dataset,
        model,
        processor,
        device,
        num_neighbors=num_results
    )
    images = results['image']
    paths = results['path']
    # Prepare the output
    output_images = []

    for img, path in zip(images, paths):
        output_images.append((np.array(img), os.path.basename(path)))
    
    return output_images  # Return the list of tuples


def main():
    print("Initializing model and loading reference dataset...")
    model, processor, ref_dataset, device = initialize_model()
    print("Initialization complete!")

    # Path to the examples folder
    examples_folder = "examples/"
    example_files = [
        os.path.join(examples_folder, fname) 
        for fname in os.listdir(examples_folder) 
        if fname.lower().endswith(('png', 'jpg', 'jpeg'))
    ]

    with gr.Blocks() as demo:
        gr.Markdown("# Image Retrieval System")
        gr.Markdown("Upload an image to find similar images in the reference dataset.")
        
        with gr.Row():
            with gr.Column(scale=1):
                input_image = gr.Image(
                    label="Upload Image",
                    type="pil"  # Changed to PIL format
                )
                num_results = gr.Slider(
                    minimum=1, 
                    maximum=10, 
                    value=5, 
                    step=1, 
                    label="Number of results"
                )
                remove_bg = gr.Checkbox(label="Remove Background")
                submit_btn = gr.Button("Search")
            
            with gr.Column(scale=2):
                gallery = gr.Gallery(
                    label="Retrieved Images",
                    show_label=True,
                    columns=3,
                    object_fit="contain"
                )

        # Add the Examples component
        gr.Examples(
            examples=example_files,
            inputs=input_image,
            label="Example Images"
        )

        submit_btn.click(
            fn=lambda img, num, bg: process_image(img, num, bg, model, processor, ref_dataset, device),
            inputs=[input_image, num_results, remove_bg],
            outputs=gallery
        )

    demo.launch(share=True)

if __name__ == "__main__":
    main()