mdanish commited on
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fabd799
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1 Parent(s): cb18289

Upload app.py with huggingface_hub

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  1. app.py +4 -1
app.py CHANGED
@@ -54,12 +54,15 @@ def knn_get_score(knn, k, cat, vec):
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  # Compute cosine similiarity of vec against allvecs
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  # (both are already normalized)
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  cos_sim_table = vec @ allvecs.T
 
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  # Get sorted array indices by similiarity in descending order
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  sortinds = np.flip(np.argsort(cos_sim_table))
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  # Get corresponding scores for the sorted vectors
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  kscores = scores[sortinds][:k]
 
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  # Get actual sorted similiarity scores
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  ksims = np.expand_dims(cos_sim_table[sortinds][:k], axis=0)
 
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  # Apply normalization after exponential formula
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  ksims = softmax(10**ksims)
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  # Weighted sum
@@ -108,7 +111,7 @@ def main():
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  vec /= vec.norm(dim=-1, keepdim=True)
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  st.write(vec.shape)
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  k = 40
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- for cat in categories:
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  st.write(cat, 'rating =', knn_get_score(knn, k, cat, vec))
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  except Exception as e:
 
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  # Compute cosine similiarity of vec against allvecs
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  # (both are already normalized)
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  cos_sim_table = vec @ allvecs.T
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+ st.write('cos_sim_table.shape', cos_sim_table.shape)
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  # Get sorted array indices by similiarity in descending order
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  sortinds = np.flip(np.argsort(cos_sim_table))
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  # Get corresponding scores for the sorted vectors
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  kscores = scores[sortinds][:k]
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+ st.write('kscores.shape', kscores.shape)
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  # Get actual sorted similiarity scores
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  ksims = np.expand_dims(cos_sim_table[sortinds][:k], axis=0)
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+ st.write('ksims.shape', ksims.shape)
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  # Apply normalization after exponential formula
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  ksims = softmax(10**ksims)
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  # Weighted sum
 
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  vec /= vec.norm(dim=-1, keepdim=True)
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  st.write(vec.shape)
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  k = 40
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+ for cat in ['walkability']:
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  st.write(cat, 'rating =', knn_get_score(knn, k, cat, vec))
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  except Exception as e: