Update app.py

app.py

@@ -3,68 +3,58 @@ from transformers import pipeline
 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)
-placeholder = st.empty()
-placeholder2 = st.empty()
-with col2:
-    text = placeholder.text_input("Enter the text you'd like to analyze for spam.", disabled=True, key="1")
-    aButton = placeholder2.button('Analyze', disabled=True, key="1")
+
 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', disabled=False):   
-    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):
+ 
+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,
+    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')
-        with col1:
-            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
-                return pred
-            with col2:
-                placeholder.text_input("Enter the text you'd like to analyze for spam.", disabled=False, key="2")
-                placeholder2.button('Analyze', disabled=False, key="2")       
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    with col1:
+        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
+                return pred      
             if text or aButton:
-                placeholder.text_input("Enter the text you'd like to analyze for spam.", disabled=False, key="3")
-                placeholder2.button('Analyze', disabled=False, key="3") 
+                st.text_input("Enter the text you'd like to analyze for spam.")
+                st.button('Analyze') 
                 with col2:
                     st.header(predict(text))