Update app.py

app.py

@@ -9,68 +9,70 @@ 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.")
 
-
-if st.button('Load Model'):   
-    with st.spinner('Wait for it...'):
-        
-        import torch
-        import numpy as np
-        
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-turkish-uncased")
-        from transformers import AutoModel
-        model = BertForSequenceClassification.from_pretrained("NimaKL/spamd_model")
-        
-        token_id = []
-        attention_masks = []
-        
-        def preprocessing(input_text, tokenizer):
-          '''
-          Returns <class transformers.tokenization_utils_base.BatchEncoding> with the following fields:
-            - input_ids: list of token ids
-            - token_type_ids: list of token type ids
-            - attention_mask: list of indices (0,1) specifying which tokens should considered by the model (return_attention_mask = True).
-          '''
-          return tokenizer.encode_plus(
-                                input_text,
-                                add_special_tokens = True,
-                                max_length = 32,
-                                pad_to_max_length = True,
-                                return_attention_mask = True,
-                                return_tensors = 'pt'
-                           )
-        
-        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-        #Used for printing the name if the variables. Removing it will not intrupt the project.
-        def namestr(obj, namespace):
-            return [name for name in namespace if namespace[name] is obj]
-        
-        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)
+with col2:
+    st.title("Spamd: Turkish Spam Detector")
+    if st.button('Load Model'):   
+        with st.spinner('Wait for it...'):
+            
+            import torch
+            import numpy as np
+            
+            from transformers import AutoTokenizer
+            tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-turkish-uncased")
+            from transformers import AutoModel
+            model = BertForSequenceClassification.from_pretrained("NimaKL/spamd_model")
+            
+            token_id = []
+            attention_masks = []
+            
+            def preprocessing(input_text, tokenizer):
+              '''
+              Returns <class transformers.tokenization_utils_base.BatchEncoding> with the following fields:
+                - input_ids: list of token ids
+                - token_type_ids: list of token type ids
+                - attention_mask: list of indices (0,1) specifying which tokens should considered by the model (return_attention_mask = True).
+              '''
+              return tokenizer.encode_plus(
+                                    input_text,
+                                    add_special_tokens = True,
+                                    max_length = 32,
+                                    pad_to_max_length = True,
+                                    return_attention_mask = True,
+                                    return_tensors = 'pt'
+                               )
+            
+            device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+            #Used for printing the name if the variables. Removing it will not intrupt the project.
+            def namestr(obj, namespace):
+                return [name for name in namespace if namespace[name] is obj]
+            
+            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))
             
-            #st.write('Input', namestr(new_sentence, globals()),': \n', new_sentence) 
-            with col2:
-                text = st.text_input("Enter the text you'd like to analyze for spam.")
-                if text or st.button('Analyze'):
-                    predict(text)
-    st.success("Model Loaded!")
+                prediction = 'Spam' if np.argmax(output.logits.cpu().numpy()).flatten().item() == 1 else 'Normal'
+                pred = 'Predicted Class: '+ prediction
+                with col2:
+                    st.header(pred)
+                
+                #st.write('Input', namestr(new_sentence, globals()),': \n', new_sentence) 
+                with col2:
+                    text = st.text_input("Enter the text you'd like to analyze for spam.")
+                    if text or st.button('Analyze'):
+                        predict(text)
+        st.success("Model Loaded!")
+