README.md

---
title: Visual Product Matcher
emoji: 🛍️
colorFrom: blue
colorTo: pink
sdk: docker
app_port: 7860
---

# 🛍️ Visual Product Matcher – StyleSense

🚀 **Visual Product Matcher** is a FastAPI-based backend deployed on Hugging Face Spaces.  
It uses **CLIP (ViT-B/32)** embeddings + **FAISS** to find visually similar products from a catalog.  

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## 🔑 Endpoints

### `GET /`
Health check – returns a status message.

**Example Response:**
```json
{
  "message": "🚀 Visual Product Matcher API is running!"
}
POST /search_text
Search products by text query.

Form Data Params:

query (str, required) → Your search text

top_k (int, default=5) → Number of results

min_score (float, default=0.0) → Similarity threshold

Example:

bash
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curl -X POST https://Yashashvibhardwaj-Visual_product_matcher.hf.space/search_text \
  -F "query=red sneakers" \
  -F "top_k=5"
POST /match
Search products by image (upload or URL).

Form Data Params:

file (UploadFile, optional) → Image file

image_url (str, optional) → Image URL

top_k (int, default=5) → Number of results

min_score (float, default=0.0) → Similarity threshold

Example with URL:

bash
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curl -X POST https://Yashashvibhardwaj-Visual_product_matcher.hf.space/match \
  -F "image_url=https://images.unsplash.com/photo-1542291026-7eec264c27ff" \
  -F "top_k=5"
Example with file:

bash
Copy code
curl -X POST https://Yashashvibhardwaj-Visual_product_matcher.hf.space/match \
  -F "file=@example.jpg" \
  -F "top_k=5"
⚙️ Tech Stack
FastAPI – API framework

Sentence-Transformers (CLIP ViT-B/32) – embeddings

FAISS – similarity search

Docker – containerized deployment

Hugging Face Spaces – hosting