Create app.py

app.py

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+import gradio as gr
+import torch
+import clip
+from PIL import Image
+
+# Load the CLIP model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model, preprocess = clip.load("ViT-B/32", device)
+
+# Define apparel categories and attributes
+categories = ["t-shirt", "jeans", "jacket", "dress", "shorts", "sweater", "skirt"]
+attributes = ["striped", "plain", "floral", "polka dot", "denim", "leather", "wool"]
+
+# Pre-compute embeddings for categories and attributes
+with torch.no_grad():
+    category_embeddings = model.encode_text(clip.tokenize(categories).to(device))
+    attribute_embeddings = model.encode_text(clip.tokenize(attributes).to(device))
+
+def predict_apparel_and_attributes(image):
+    # Process image and compute its embedding
+    image_input = preprocess(image).unsqueeze(0).to(device)
+    with torch.no_grad():
+        image_embedding = model.encode_image(image_input)
+    
+    # Calculate similarity scores
+    category_similarities = (image_embedding @ category_embeddings.T).squeeze(0)
+    attribute_similarities = (image_embedding @ attribute_embeddings.T).squeeze(0)
+    
+    # Get top category and attributes
+    top_category = categories[category_similarities.argmax().item()]
+    top_attributes = [attributes[i] for i in attribute_similarities.argsort(descending=True)[:3]]  # top 3 attributes
+
+    return top_category, ", ".join(top_attributes)
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=predict_apparel_and_attributes, 
+    inputs=gr.inputs.Image(label="Upload an apparel image"), 
+    outputs=[gr.outputs.Textbox(label="Apparel Category"), gr.outputs.Textbox(label="Apparel Attributes")]
+)
+iface.launch()