Update app.py

app.py

@@ -4,7 +4,9 @@ from textblob import TextBlob
 from transformers import BertForSequenceClassification, AdamW, BertConfig
 st.set_page_config(layout='wide', initial_sidebar_state='expanded')
 col1, col2= st.columns(2)
-
+with col2:
+    text = st.text_input("Enter the text you'd like to analyze for spam.")
+    aButton = st.button('Analyze') 
 with col1:
     st.title("Spamd: Turkish Spam Detector")
     st.markdown("Message spam detection tool for Turkish language. Due the small size of the dataset, I decided to go with transformers         technology Google BERT. Using the Turkish pre-trained model BERTurk, I imporved the accuracy of the tool by 18 percent compared to the previous model which used fastText.")
@@ -34,30 +36,28 @@ def preprocessing(input_text, tokenizer):
         return_tensors = 'pt'
             )
     device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    with col1:
-        st.success("Model Loaded!")
-    def predict(new_sentence):
+    
+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
-            return pred      
-        if text or aButton:
-            st.text_input("Enter the text you'd like to analyze for spam.")
-            st.button('Analyze') 
-            with col2:
-                st.header(predict(text))
+    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
+        return pred      
+
+if text or aButton:
+    with col2:
+        st.header(predict(text))