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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=20,
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() |