Update app.py

app.py

@@ -37,27 +37,27 @@ if st.button('Load Model', disabled=False):
             )
         device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
         st.success("Model Loaded!")
-            def predict(new_sentence):
-                # We need Token IDs and Attention Mask for inference on the new sentence
-                test_ids = []
-                test_attention_mask = []
-                # Apply the tokenizer
-                encoding = preprocessing(new_sentence, tokenizer)
-                # Extract IDs and Attention Mask
-                test_ids.append(encoding['input_ids'])
-                test_attention_mask.append(encoding['attention_mask'])
-                test_ids = torch.cat(test_ids, dim = 0)
-                test_attention_mask = torch.cat(test_attention_mask, dim = 0)
-                # Forward pass, calculate logit predictions
-                    with torch.no_grad():
-                        output = model(test_ids.to(device), token_type_ids = None, attention_mask = test_attention_mask.to(device))
-                        prediction = 'Spam' if np.argmax(output.logits.cpu().numpy()).flatten().item() == 1 else 'Normal'
-                        pred = 'Predicted Class: '+ prediction
-                        with col2:
-                            st.header(pred)
-                            text = st.text_input("Enter the text you'd like to analyze for spam.")
-                            if text or st.button('Analyze'):
-                                predict(text)
+        def predict(new_sentence):
+            # We need Token IDs and Attention Mask for inference on the new sentence
+            test_ids = []
+            test_attention_mask = []
+            # Apply the tokenizer
+            encoding = preprocessing(new_sentence, tokenizer)
+            #Extract IDs and Attention Mask
+            test_ids.append(encoding['input_ids'])
+            test_attention_mask.append(encoding['attention_mask'])
+            test_ids = torch.cat(test_ids, dim = 0)
+            test_attention_mask = torch.cat(test_attention_mask, dim = 0)
+            #Forward pass, calculate logit predictions
+            with torch.no_grad():
+                output = model(test_ids.to(device), token_type_ids = None, attention_mask = test_attention_mask.to(device))
+                prediction = 'Spam' if np.argmax(output.logits.cpu().numpy()).flatten().item() == 1 else 'Normal'
+                pred = 'Predicted Class: '+ prediction
+                with col2:
+                    st.header(pred)
+                    text = st.text_input("Enter the text you'd like to analyze for spam.")
+                    if text or st.button('Analyze'):
+                        predict(text)