Create app.py

app.py

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+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()