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 both text and image inputs
# def generate_embeddings(input_text=None, input_image_url=None):
#     try:
#         if input_image_url:
#             # Process image with accompanying text
#             response = requests.get(input_image_url, stream=True)
#             response.raise_for_status()
#             image = Image.open(response.raw)

#             # Use a default text if none is provided
#             if not input_text:
#                 input_text = "this is an image"

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

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

#             image_embedding = outputs.logits_per_image.cpu().numpy().tolist()
#             return {
#                 "type": "image_embedding",
#                 "input_image_url": input_image_url,
#                 "input_text": input_text,
#                 "embedding": image_embedding
#             }

#         elif input_text:
#             # Process text input only
#             inputs = processor(
#                 text=[input_text],
#                 images=None,
#                 return_tensors="pt",
#                 padding=True
#             )
#             with torch.no_grad():
#                 outputs = model(**inputs)

#             text_embedding = outputs.logits_per_text.cpu().numpy().tolist()
#             return {
#                 "type": "text_embedding",
#                 "input_text": input_text,
#                 "embedding": text_embedding
#             }
#         else:
#             return {"error": "Please provide either a text query or an image URL."}

#     except Exception as e:
#         return {"error": str(e)}

# # Create the Gradio interface
# interface = gr.Interface(
#     fn=generate_embeddings,
#     inputs=[
#         gr.Textbox(label="Text Query (Optional)", placeholder="e.g., red dress (used with image or for text embedding)"),
#         gr.Textbox(label="Image URL", placeholder="e.g., https://example.com/image.jpg (used with or without text query)")
#     ],
#     outputs="json",
#     title="FashionCLIP Combined Embedding API",
#     description="Provide a text query and/or an image URL to compute embeddings for vector search."
# )

# # Launch the app
# if __name__ == "__main__":
#     interface.launch()
# print(generate_embeddings("red dress"))



import os
import gradio as gr
from encoder import FashionCLIPEncoder
from process import batch_process_images

# Initialize encoder
encoder = FashionCLIPEncoder()

# Constants
BATCH_SIZE = 30  # Define batch size for processing

# Gradio Interface
iface = gr.Interface(
    fn=lambda image_urls: batch_process_images(image_urls, encoder, BATCH_SIZE),
    inputs=gr.Textbox(
        lines=5,
        placeholder="Enter image URLs separated by commas",
        label="Batch Image URLs",
    ),
    outputs=gr.JSON(label="Embedding Results"),
    title="Batch Fashion CLIP Embedding API",
    description="Enter multiple image URLs (separated by commas) to generate embeddings for the batch. Each embedding preview includes the first 5 values.",
    examples=[
        ["https://cdn.shopify.com/s/files/1/0522/2239/4534/files/CT21355-22_1024x1024.webp, https://cdn.shopify.com/s/files/1/0522/2239/4534/files/00907857-C6B0-4D2A-8AEA-688BDE1E67D7_1024x1024.jpg"]
    ],
)

# Launch Gradio App
if __name__ == "__main__":
    iface.launch()