caching added

app.py

@@ -6,13 +6,6 @@ from time import time
 # device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 device = 'cpu'
 
-st.markdown("### Privet, mir!")
-st.markdown("<img width=200px src='https://i.pinimg.com/736x/11/33/19/113319f0ffe91f4bb0f468914b9916da.jpg'>", unsafe_allow_html=True)
-
-text = st.text_area("ENTER TEXT HERE")
-start_time = time()
-st.markdown("INFERENCE STARTS ...")
-
 # dict for decoding / enclding labels
 labels = {'cs.NE': 0, 'cs.CL': 1, 'cs.AI': 2, 'stat.ML': 3, 'cs.CV': 4, 'cs.LG': 5}
 labels_decoder = {'cs.NE': 'Neural and Evolutionary Computing', 'cs.CL': 'Computation and Language', 'cs.AI': 'Artificial Intelligence', 
@@ -37,6 +30,7 @@ class BertClassifier(nn.Module):
         final_layer = self.relu(linear_output)
         return final_layer
 
+@st.cache(suppress_st_warning=True)
 def build_model():
     model = BertClassifier(n_classes=len(labels))
     st.markdown("Model created")
@@ -45,8 +39,6 @@ def build_model():
     st.markdown("Model weights loaded")
     return model
     
-model = build_model()
-    
 def inference(txt, mode=None):
     # infers classes for text topic based on the trained model from above
     # has separate mode 'print' for just output
@@ -62,11 +54,22 @@ def inference(txt, mode=None):
     out = out/out.sum() * 100
     res = [(l, o) for l, o in zip (list(labels.keys()), out.tolist())]   
     return res   
-        
-res = inference(text, mode=None)    
-st.markdown("INFERENCE RESULT:")    
+    
+model = build_model()
+    
+st.markdown("### Privet, mir!")
+st.markdown("<img width=200px src='https://i.pinimg.com/736x/11/33/19/113319f0ffe91f4bb0f468914b9916da.jpg'>", unsafe_allow_html=True)
+
+text = st.text_area("ENTER TEXT HERE")
+start_time = time()
+st.markdown("INFERENCE STARTS ...")
+    
+
+res = inference(text, mode=None)
+res.sort(key = lambda x : - x[1])
+st.markdown("<b>INFERENCE RESULT:</b>")    
 for lbl, score in res:
     if score >=1:
-        st.markdown(f"[{lbl:<7}] {labels_decoder[lbl]:<35}  {score:.1f}%")
+        st.markdown(f"[        {lbl:<7}] {labels_decoder[lbl]:<35}  {score:.1f}%")
 
 st.markdown(f"cycle time = {time() - start_time:.2f} s.")