commit13

app.py

@@ -472,16 +472,25 @@ def search_fashion(query: str, alpha: float):
         imgs_with_captions.append((padded, caption))
 
     return imgs_with_captions
-def search_by_image_only(uploaded_image, top_k=12):
-    if uploaded_image is None:
-        return []
 
-    uploaded_image = uploaded_image.convert("RGB")
-    dense_vec = model.encode(uploaded_image).tolist()
+\from PIL import Image, ImageOps
+import numpy as np
+
+def search_by_image(uploaded_image, alpha=0.5):
+    """
+    Given a PIL image from Gradio, find visually similar products.
+    """
+    # Preprocess as CLIP expects
+    processed = clip_processor(images=uploaded_image, return_tensors="pt").to(device)
+
+    with torch.no_grad():
+        image_vec = clip_model.get_image_features(**processed)
+        image_vec = image_vec.cpu().numpy().flatten().tolist()
 
+    # since your Pinecone is purely visual, we query on visual vector
     result = index.query(
-        vector=dense_vec,
-        top_k=top_k,
+        top_k=12,
+        vector=image_vec,
         include_metadata=True
     )
 
@@ -493,12 +502,13 @@ def search_by_image_only(uploaded_image, top_k=12):
         if not isinstance(img, Image.Image):
             img = Image.fromarray(np.array(img))
         padded = ImageOps.pad(img, (256, 256), color="white")
-        caption = meta.get("productDisplayName", "Unknown Product")
+        caption = str(meta.get("productDisplayName", "Unknown Product"))
         imgs_with_captions.append((padded, caption))
 
     return imgs_with_captions
 
 
+
 # ------------------- Gradio UI -------------------
 custom_css = """
 .search-btn {