Fix CLIrrr2 model issue in appetete333.py

app.py

@@ -31,7 +31,7 @@ print("Loading CLIP model (this may take 20-60s the first time)...")
 MODEL_ID = "sentence-transformers/clip-ViT-B-32-multilingual-v1"
 clip_model = SentenceTransformer(MODEL_ID)
 
-# Detect correct embedding size dynamically
+# Dynamically detect vector size
 VECTOR_SIZE = clip_model.get_sentence_embedding_dimension()
 
 genai_client = genai.Client(api_key=GEMINI_API_KEY) if GEMINI_API_KEY else None
@@ -42,7 +42,7 @@ if not QDRANT_URL:
 qclient = QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY)
 COLLECTION = "lost_found_items"
 
-# Create collection if missing
+# Ensure collection exists with correct vector size
 try:
     if not qclient.collection_exists(COLLECTION):
         qclient.create_collection(
@@ -56,11 +56,12 @@ except Exception as e:
 # Helpers
 # -------------------------
 def embed_text(text: str):
-    return clip_model.encode(text, convert_to_numpy=True)
+    return clip_model.encode([text], convert_to_numpy=True)[0]
 
 def embed_image_pil(pil_img: Image.Image):
     pil_img = pil_img.convert("RGB")
-    return clip_model.encode(pil_img, convert_to_numpy=True)
+    np_img = np.array(pil_img)
+    return clip_model.encode([np_img], convert_to_numpy=True)[0]
 
 def gen_tags_from_image_file(image_bytes: io.BytesIO) -> str:
     if genai_client is None:
@@ -96,7 +97,6 @@ def add_item(mode: str, uploaded_image, text_description: str):
 
             vec = embed_image_pil(uploaded_image).tolist()
             payload["has_image"] = True
-
             payload["tags"] = gen_tags_from_image_file(img_bytes)
             payload["image_b64"] = base64.b64encode(img_bytes.getvalue()).decode("utf-8")
         else: