Update main.py

main.py

@@ -16,16 +16,16 @@ os.environ["SENTENCE_TRANSFORMERS_HOME"] = "./cache"
 
 app = FastAPI()
 
-# Enable CORS (for frontend)
+# Enable CORS (so Netlify frontend can call)
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"],  # you can restrict to your Netlify domain later
+    allow_origins=["*"],  # can restrict to Netlify domain later
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
 )
 
-# Load products directly from products.json
+# Load products
 with open("products.json", "r", encoding="utf-8") as f:
     products = json.load(f)
 
@@ -49,12 +49,12 @@ 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)
+    query_emb = model.encode([query], convert_to_numpy=True, normalize_embeddings=True)
+    sims, indices = index.search(query_emb, top_k)
 
     results = []
-    for dist, idx in zip(distances[0], indices[0]):
-        score = float(1 - dist)  # convert distance to similarity (optional)
+    for sim, idx in zip(sims[0], indices[0]):
+        score = float(sim)  # already cosine similarity (0–1)
         if score >= min_score:
             item = products[idx].copy()
             item["score"] = score
@@ -82,12 +82,12 @@ async def search_image(
     else:
         return {"error": "No image provided"}
 
-    image_emb = model.encode([image], convert_to_numpy=True)
-    distances, indices = index.search(image_emb, top_k)
+    image_emb = model.encode([image], convert_to_numpy=True, normalize_embeddings=True)
+    sims, indices = index.search(image_emb, top_k)
 
     results = []
-    for dist, idx in zip(distances[0], indices[0]):
-        score = float(1 - dist)  # convert distance to similarity
+    for sim, idx in zip(sims[0], indices[0]):
+        score = float(sim)  # cosine similarity
         if score >= min_score:
             item = products[idx].copy()
             item["score"] = score