Update main.py

main.py

@@ -15,10 +15,10 @@ os.environ["SENTENCE_TRANSFORMERS_HOME"] = "./cache"
 
 app = FastAPI()
 
-# Enable CORS (for frontend HTML to connect)
+# Enable CORS (so frontend on Netlify can call backend on HF)
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"],
+    allow_origins=["*"],  # for now allow all, can restrict to Netlify domain
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
@@ -42,24 +42,46 @@ def root():
 
 
 @app.post("/search_text")
-def search_text(query: str = Form(...), top_k: int = 5):
+def search_text(query: str = Form(...), top_k: int = 5, min_score: float = 0.0):
     """
     Search products using text query.
     """
     query_emb = model.encode([query], convert_to_numpy=True)
     distances, indices = index.search(query_emb, top_k)
-    results = [products[i] for i in indices[0]]
-    return {"query": query, "results": results}
 
+    results = []
+    for score, idx in zip(distances[0], indices[0]):
+        if score >= min_score:  # filter by threshold
+            item = products[idx]
+            item["score"] = float(score)
+            results.append(item)
 
-@app.post("/search_image")
-async def search_image(file: UploadFile = File(...), top_k: int = 5):
+    return {"matches": results}
+
+
+@app.post("/match")  # 👈 Renamed to match frontend
+async def search_image(file: UploadFile = File(None), image_url: str = Form(None), top_k: int = 5, min_score: float = 0.0):
     """
-    Search products using image query.
+    Search products using image query (upload or URL).
     """
-    image_bytes = await file.read()
-    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    if file:
+        image_bytes = await file.read()
+        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    elif image_url:
+        import requests
+        response = requests.get(image_url)
+        image = Image.open(io.BytesIO(response.content)).convert("RGB")
+    else:
+        return {"error": "No image provided"}
+
     image_emb = model.encode([image], convert_to_numpy=True)
     distances, indices = index.search(image_emb, top_k)
-    results = [products[i] for i in indices[0]]
-    return {"results": results}
+
+    results = []
+    for score, idx in zip(distances[0], indices[0]):
+        if score >= min_score:
+            item = products[idx]
+            item["score"] = float(score)
+            results.append(item)
+
+    return {"matches": results}