fix image similarity detection

src/streamlit_app.py

@@ -174,30 +174,31 @@ if query:
             st.warning("OpenAI API key not found. Please set OPENAI_API_KEY as a secret environment variable.")
 
 if uploaded_file is not None:
-    print(uploaded_file)
-    st.write(f'uploading file {uploaded_file.name}')
-    query_image = Image.open(uploaded_file).convert("RGB")
-    st.image(query_image, caption="Your uploaded image", use_container_width=True)
-
-    # Embed uploaded image
-    inputs = clip_processor(images=query_image, return_tensors="pt")
-    with torch.no_grad():
-        query_feat = clip_model.get_image_features(**inputs)
-    query_feat /= query_feat.norm(p=2, dim=-1, keepdim=True)
-
-    # Compute cosine similarity
-    similarities = (dataset_image_features @ query_feat.T).squeeze(1)  # [num_dataset_images]
-
-    top_k = 3
-    top_results = torch.topk(similarities, k=top_k)
-
-    st.write(f"Top {top_k} similar medical cases:")
-
-    for rank, idx in enumerate(top_results.indices):
-        score = top_results.values[rank].item()
-        similar_img = data[int(idx)]['image']
-        st.image(similar_img, caption=f"Similarity: {score:.3f}", use_container_width=True)
-        st.markdown(f"**Case description:** {data[int(idx)]['complaints']}")
+    with st.spinner("Searching medical cases..."):
+        print(uploaded_file)
+        st.write(f'uploading file {uploaded_file.name}')
+        query_image = Image.open(uploaded_file).convert("RGB")
+        st.image(query_image, caption="Your uploaded image", use_container_width=True)
+
+        # Embed uploaded image
+        inputs = clip_processor(images=query_image, return_tensors="pt")
+        with torch.no_grad():
+            query_feat = clip_model.get_image_features(**inputs)
+        query_feat /= query_feat.norm(p=2, dim=-1, keepdim=True)
+
+        # Compute cosine similarity
+        similarities = (dataset_image_features @ query_feat.T).squeeze(1)  # [num_dataset_images]
+
+        top_k = 3
+        top_results = torch.topk(similarities, k=top_k)
+
+        st.write(f"Top {top_k} similar medical cases:")
+
+        for rank, idx in enumerate(top_results.indices):
+            score = top_results.values[rank].item()
+            similar_img = data[int(idx)]['image']
+            st.image(similar_img, caption=f"Similarity: {score:.3f}", use_container_width=True)
+            st.markdown(f"**Case description:** {data[int(idx)]['complaints']}")
 else:
     st.write("no image")