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app.py

@@ -11,6 +11,7 @@ knnpath = '20241204-ams-no-env-open_clip_ViT-H-14-378-quickgelu.npz'
 clip_model_name = 'ViT-H-14-378-quickgelu'
 pretrained_name = 'dfn5b'
 
+categories = ['walkability', 'bikeability', 'pleasantness', 'greenness', 'safety']
 
 # Set page config
 st.set_page_config(
@@ -47,6 +48,25 @@ def process_image(image, preprocess):
     processed_image = preprocess(image).unsqueeze(0)
     return processed_image
 
+def knn_get_score(knn, k, cat, vec):
+    allvecs = knn[f'{cat}_vecs']
+    scores = knn[f'{cat}_scores']
+    # Compute cosine similiarity of vec against allvecs
+    # (both are already normalized)
+    cos_sim_table = vec @ allvecs.T
+    # Get sorted array indices by similiarity in descending order
+    sortinds = np.flip(np.argsort(cos_sim_table))
+    # Get corresponding scores for the sorted vectors
+    kscores = scores[sortinds][:k]
+    # Get actual sorted similiarity scores
+    ksims = np.expand_dims(cos_sim_table[sortinds][:k], axis=0)
+    # Apply normalization after exponential formula
+    ksims = softmax(10**ksims)
+    # Weighted sum
+    kweightedscore = np.sum(kscores * ksims)
+    return kweightedscore
+
+
 @st.cache_resource
 def load_knn():
     return np.load(knnpath)
@@ -73,7 +93,7 @@ def main():
         try:
             image = Image.open(file)
 
-            st.image(image, caption="Uploaded Image", use_column_width=True)
+            st.image(image, caption="Uploaded Image", width=640)
                 
             # Process image
             with st.spinner('Processing image...'):
@@ -87,6 +107,10 @@ def main():
                     # Normalize vector
                     vec /= vec.norm(dim=-1, keepdim=True)
                 st.write(vec.shape)
+                k = 40
+                for cat in categories:
+                    st.write(cat, 'rating =', knn_get_score(knn, k, cat, vec))
+
         except Exception as e:
             st.error(f"Error processing image: {str(e)}")