app.py

import gradio as gr
from upstash_vector import AsyncIndex
from transformers import AutoFeatureExtractor, AutoModel
from datasets import load_dataset

index = AsyncIndex.from_env()

model_ckpt = "google/vit-base-patch16-224-in21k"
extractor = AutoFeatureExtractor.from_pretrained(model_ckpt)
model = AutoModel.from_pretrained(model_ckpt)
hidden_dim = model.config.hidden_size
dataset = load_dataset("BounharAbdelaziz/Face-Aging-Dataset")

with gr.Blocks() as demo:
    gr.Markdown(
        """
        # Find Your Twins

        Upload your face and find the most similar people from [Face Aging Dataset](https://huggingface.co/datasets/BounharAbdelaziz/Face-Aging-Dataset) using Google's [VIT](https://huggingface.co/google/vit-base-patch16-224-in21k) model. The task of finding most similar vectors is powered by [Upstash Vector](https://upstash.com) 🚀. Check our blog post *here*.
        """
    )

    with gr.Tab("Basic"):
        with gr.Row():
            with gr.Column(scale=1):
                input_image = gr.Image(type="pil")
            with gr.Column(scale=3):
                output_image = gr.Gallery(height=800)


        @input_image.change(inputs=input_image, outputs=output_image)
        async def find_similar_faces(image):
            if image is None:
                return None
            inputs = extractor(images=image, return_tensors="pt")
            outputs = model(**inputs)
            embed = outputs.last_hidden_state[0][0]
            result = await index.query(vector=embed.tolist(), top_k=4)
            return [dataset["train"][int(vector.id)]["image"] for vector in result]


        gr.Examples(
            examples=[dataset["train"][6]["image"]],
            inputs=input_image,
            outputs=output_image,
            fn=find_similar_faces,
            cache_examples=False,
        )

    with gr.Tab("Advanced"):
        with gr.Row():
            with gr.Column(scale=1):
                adv_input_image = gr.Image(type="pil")
                adv_image_count = gr.Number(9, label="Image Count")

            with gr.Column(scale=3):
                adv_output_image = gr.Gallery(height=1000)


        @adv_input_image.upload(inputs=[adv_input_image, adv_image_count], outputs=[adv_output_image])
        async def find_similar_faces(image, count):
            inputs = extractor(images=image, return_tensors="pt")
            outputs = model(**inputs)
            embed = outputs.last_hidden_state[0][0]
            result = await index.query(
                vector=embed.tolist(), top_k=max(1, min(19, count))
            )
            return [dataset["train"][int(vector.id)]["image"] for vector in result]

if __name__ == "__main__":
    demo.launch(debug=True)