Simplify app.py - use gr.Interface instead of gr.Blocks

hf-space/app.py

@@ -1,10 +1,5 @@
 """
 Visual Search API - HuggingFace Space
-
-Provides image embedding endpoint using Jina CLIP v2.
-Queries Pinecone for similar products.
-
-Deploy to HuggingFace Spaces with ZeroGPU (free).
 """
 
 import os
@@ -31,8 +26,6 @@ def load_model():
             "jinaai/jina-clip-v2",
             trust_remote_code=True
         )
-        if torch.cuda.is_available():
-            model = model.cuda()
         model.eval()
         print("Model loaded!")
     return model
@@ -47,7 +40,7 @@ def get_embedding(image: Image.Image) -> list:
         if hasattr(emb, 'cpu'):
             emb = emb.cpu().numpy()
         emb = emb.flatten()
-        emb = emb / np.linalg.norm(emb)  # L2 normalize
+        emb = emb / np.linalg.norm(emb)
         if len(emb) > 512:
             emb = emb[:512]
         return emb.tolist()
@@ -83,80 +76,40 @@ def query_pinecone(embedding: list, top_k: int = 12) -> list:
             'handle': m.get('metadata', {}).get('handle', m.get('id')),
             'title': m.get('metadata', {}).get('title', ''),
             'score': m.get('score', 0),
-            'image_url': m.get('metadata', {}).get('image_url', '')
         }
         for m in matches
     ]
 
 
-def search(image: Image.Image) -> dict:
-    """
-    Main search function.
-    Returns embedding and similar products.
-    """
+def search(image):
+    """Main search function."""
     if image is None:
-        return {"error": "No image provided"}
-
-    # Get embedding
-    embedding = get_embedding(image)
-
-    # Query Pinecone
-    products = query_pinecone(embedding)
-
-    return {
-        "embedding": embedding,
-        "products": products
-    }
-
-
-def search_simple(image: Image.Image) -> str:
-    """Simple search returning product handles."""
-    if image is None:
-        return "No image"
-
-    embedding = get_embedding(image)
-    products = query_pinecone(embedding)
-
-    if not products:
-        return "No similar products found"
-
-    return "\n".join([
-        f"{i+1}. {p['title']} ({p['handle']}) - {p['score']:.2f}"
-        for i, p in enumerate(products)
-    ])
-
-
-# Gradio Interface
-with gr.Blocks(title="Visual Search API") as demo:
-    gr.Markdown("# Visual Product Search")
-    gr.Markdown("Upload an image to find similar products.")
-
-    with gr.Row():
-        with gr.Column():
-            image_input = gr.Image(type="pil", label="Upload Image")
-            search_btn = gr.Button("Search", variant="primary")
-
-        with gr.Column():
-            output = gr.Textbox(label="Results", lines=15)
-
-    search_btn.click(
-        fn=search_simple,
-        inputs=[image_input],
-        outputs=[output]
-    )
-
-    gr.Markdown("---")
-    gr.Markdown("### API Endpoint")
-    gr.Markdown("""
-    Use the `/api/predict` endpoint for programmatic access:
-
-    ```python
-    from gradio_client import Client
-
-    client = Client("YOUR_SPACE_URL")
-    result = client.predict(image_path, api_name="/predict")
-    ```
-    """)
-
-
-# HF Spaces handles the launch automatically - do not call demo.launch()
+        return "No image provided"
+
+    try:
+        embedding = get_embedding(image)
+        products = query_pinecone(embedding)
+
+        if not products:
+            return "No similar products found"
+
+        result = "\n".join([
+            f"{i+1}. {p['title']} ({p['handle']}) - score: {p['score']:.3f}"
+            for i, p in enumerate(products)
+        ])
+        return result
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+# Simple Gradio interface
+demo = gr.Interface(
+    fn=search,
+    inputs=gr.Image(type="pil", label="Upload Image"),
+    outputs=gr.Textbox(label="Similar Products", lines=15),
+    title="Visual Product Search",
+    description="Upload an image to find similar products."
+)
+
+if __name__ == "__main__":
+    demo.launch()