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README.md

@@ -1,13 +1,43 @@
 ---
-title: Visual Search
-emoji: 🦀
-colorFrom: pink
-colorTo: red
+title: Visual Product Search
+emoji: 🔍
+colorFrom: blue
+colorTo: purple
 sdk: gradio
-sdk_version: 6.5.1
+sdk_version: 4.44.0
 app_file: app.py
 pinned: false
 license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Visual Product Search API
+
+AI-powered visual search using **Jina CLIP v2** embeddings.
+
+## Features
+- Upload an image to find visually similar products
+- Uses Jina CLIP v2 for state-of-the-art image embeddings
+- Queries Pinecone vector database for similarity search
+
+## API Usage
+
+```python
+from gradio_client import Client
+
+client = Client("YOUR_USERNAME/visual-search")
+result = client.predict(
+    "path/to/image.jpg",
+    api_name="/predict"
+)
+print(result)
+```
+
+## Setup
+
+Set these secrets in HuggingFace Space settings:
+- `PINECONE_API_KEY`: Your Pinecone API key
+- `PINECONE_HOST`: Your Pinecone index host (without https://)
+
+## Model
+
+Uses [jinaai/jina-clip-v2](https://huggingface.co/jinaai/jina-clip-v2) - a multilingual multimodal embedding model.

app.py

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+"""
+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
+import gradio as gr
+import torch
+import numpy as np
+from PIL import Image
+
+# Pinecone config from HF Secrets
+PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY')
+PINECONE_HOST = os.environ.get('PINECONE_HOST')
+
+# Model (loaded on first use)
+model = None
+
+
+def load_model():
+    """Load Jina CLIP v2 model."""
+    global model
+    if model is None:
+        print("Loading Jina CLIP v2...")
+        from transformers import AutoModel
+        model = AutoModel.from_pretrained(
+            "jinaai/jina-clip-v2",
+            trust_remote_code=True
+        )
+        if torch.cuda.is_available():
+            model = model.cuda()
+        model.eval()
+        print("Model loaded!")
+    return model
+
+
+def get_embedding(image: Image.Image) -> list:
+    """Generate 512-dim embedding for an image."""
+    m = load_model()
+
+    with torch.no_grad():
+        emb = m.encode_image(image)
+        if hasattr(emb, 'cpu'):
+            emb = emb.cpu().numpy()
+        emb = emb.flatten()
+        emb = emb / np.linalg.norm(emb)  # L2 normalize
+        if len(emb) > 512:
+            emb = emb[:512]
+        return emb.tolist()
+
+
+def query_pinecone(embedding: list, top_k: int = 12) -> list:
+    """Query Pinecone for similar products."""
+    if not PINECONE_API_KEY or not PINECONE_HOST:
+        return []
+
+    import requests
+
+    resp = requests.post(
+        f"https://{PINECONE_HOST}/query",
+        headers={
+            "Api-Key": PINECONE_API_KEY,
+            "Content-Type": "application/json"
+        },
+        json={
+            "vector": embedding,
+            "topK": top_k,
+            "includeMetadata": True
+        },
+        timeout=15
+    )
+
+    if resp.status_code != 200:
+        return []
+
+    matches = resp.json().get('matches', [])
+    return [
+        {
+            '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.
+    """
+    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")
+    ```
+    """)
+
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+torch
+transformers>=4.45.0,<4.48.0
+pillow
+numpy
+requests
+einops
+timm
+gradio