Upload app.py with huggingface_hub

app.py

@@ -19,11 +19,7 @@ st.set_page_config(
     layout="wide"
 )
 
-#model, preprocess = open_clip.create_model_from_pretrained('hf-hub:laion/CLIP-ViT-g-14-laion2B-s12B-b42K')
-#tokenizer = open_clip.get_tokenizer('hf-hub:laion/CLIP-ViT-g-14-laion2B-s12B-b42K')
-
-#model, preprocess = open_clip.create_model_from_pretrained(clip_model_name)
-#tokenizer = open_clip.get_tokenizer(clip_model_name)
+debug = True
 
 #st.write("Available models:", open_clip.list_models())
 
@@ -50,22 +46,22 @@ def process_image(image, preprocess):
 
 def knn_get_score(knn, k, cat, vec):
     allvecs = knn[f'{cat}_vecs']
-    st.write('allvecs.shape', allvecs.shape)
+    if debug: st.write('allvecs.shape', allvecs.shape)
     scores = knn[f'{cat}_scores']
-    st.write('scores.shape', scores.shape)
+    if debug: st.write('scores.shape', scores.shape)
     # 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)
+    if debug: 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))
-    st.write('sortinds.shape', sortinds.shape)
+    if debug: st.write('sortinds.shape', sortinds.shape)
     # Get corresponding scores for the sorted vectors
     kscores = scores[sortinds][:k]
-    st.write('kscores.shape', kscores.shape)
+    if debug: st.write('kscores.shape', kscores.shape)
     # Get actual sorted similiarity scores
-    ksims = cos_sim_table[sortinds][:k]
-    st.write('ksims.shape', ksims.shape)
+    ksims = cos_sim_table[:, sortinds][:k]
+    if debug: st.write('ksims.shape', ksims.shape)
     # Apply normalization after exponential formula
     ksims = softmax(10**ksims)
     # Weighted sum
@@ -91,7 +87,7 @@ def main():
 
     with st.spinner('Loading KNN model... This may take a moment.'):
         knn = load_knn()
-    st.write(knn['walkability_vecs'].shape)
+    if debug: st.write(knn['walkability_vecs'].shape)
 
     file = st.file_uploader('Upload An Image')
 
@@ -112,7 +108,7 @@ def main():
                     
                     # Normalize vector
                     vec /= vec.norm(dim=-1, keepdim=True)
-                st.write(vec.shape)
+                if debug: st.write(vec.shape)
                 vec = vec.numpy()
                 k = 40
                 for cat in ['walkability']: