Update app.py

app.py

@@ -36,7 +36,7 @@ embeddings = emb_df.drop(columns=["image_id"]).values.astype(np.float32)
 sampled_indices = np.load("sampled_indices_3000.npy").astype(int).tolist()
 
 # Load dataset and select the sampled subset
-ds = load_dataset("JamieSJS/stanford-online-products", "corpus")["corpus"]
+ds = load_dataset("JamieSJS/stanford-online-products", "corpus", split="corpus")
 sampled_dataset = ds.select(sampled_indices)
 
 
@@ -73,7 +73,7 @@ def recommend(image):
         # Fetch images directly from the sampled dataset
         results = [sampled_dataset[int(i)]["image"] for i in top_idx]
 
-        # Optional: return a short message for visibility
+        # Return a short message for visibility
         msg = (
             f"Top-3 cosine similarity scores: "
             f"{top_scores[0]:.3f}, {top_scores[1]:.3f}, {top_scores[2]:.3f}"
@@ -94,10 +94,8 @@ demo = gr.Interface(
         gr.Textbox(label="Details"),
     ],
     title="CLIP Image Recommendation System",
-    description="Upload an image and receive visually similar product recommendations.",
-    allow_flagging="never",
+    suggests="Upload an image and receive visually similar product recommendations."
 )
 
-
 # Launch the application
 demo.launch(show_error=True, ssr_mode=False)