app.py

import gradio as gr
from transformers import AutoProcessor, AutoModelForZeroShotImageClassification
from PIL import Image
import requests
import torch

# Load the FashionCLIP processor and model
processor = AutoProcessor.from_pretrained("patrickjohncyh/fashion-clip")
model = AutoModelForZeroShotImageClassification.from_pretrained("patrickjohncyh/fashion-clip")

# Define the function to process image and text
def process_image_and_text(product_title, image_url):
    try:
        # Fetch and process the image
        response = requests.get(image_url, stream=True)
        response.raise_for_status()
        image = Image.open(response.raw)

        # Prepare inputs for the model
        inputs = processor(
            text=[product_title],
            images=image,
            return_tensors="pt",
            padding=True
        )

        # Perform inference
        with torch.no_grad():
            outputs = model(**inputs)

        # Extract similarity score and embeddings
        similarity_score = outputs.logits_per_image[0].item()
        text_embedding = outputs.logits_per_text.cpu().numpy().tolist()
        image_embedding = outputs.logits_per_image.cpu().numpy().tolist()

        return {
            "similarity_score": similarity_score,
            "text_embedding": text_embedding,
            "image_embedding": image_embedding
        }
    except Exception as e:
        return {"error": str(e)}

# Create the Gradio interface
interface = gr.Interface(
    fn=process_image_and_text,
    inputs=[
        gr.Textbox(label="Product Title", placeholder="e.g., ring for men"),
        gr.Textbox(label="Image URL", placeholder="e.g., https://example.com/image.jpg")
    ],
    outputs="json",
    title="FashionCLIP API",
    description="Provide a product title and an image URL to compute similarity score and embeddings."
)

# Launch the app
if __name__ == "__main__":
    interface.launch()