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app.py

@@ -54,12 +54,15 @@ def knn_get_score(knn, k, cat, vec):
     # Compute cosine similiarity of vec against allvecs
     # (both are already normalized)
     cos_sim_table = vec @ allvecs.T
+    st.write('cos_sim_table.shape', cos_sim_table.shape)
     # 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]
+    st.write('kscores.shape', kscores.shape)
     # Get actual sorted similiarity scores
     ksims = np.expand_dims(cos_sim_table[sortinds][:k], axis=0)
+    st.write('ksims.shape', ksims.shape)
     # Apply normalization after exponential formula
     ksims = softmax(10**ksims)
     # Weighted sum
@@ -108,7 +111,7 @@ def main():
                     vec /= vec.norm(dim=-1, keepdim=True)
                 st.write(vec.shape)
                 k = 40
-                for cat in categories:
+                for cat in ['walkability']:
                     st.write(cat, 'rating =', knn_get_score(knn, k, cat, vec))
 
         except Exception as e: