Update app.py

app.py

@@ -4,12 +4,14 @@ 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)
-flag = False
+flag = True
 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=True):   
+with col2:
+        text = st.text_input("Enter the text you'd like to analyze for spam.", disabled=flag)
+        st.button('Analyze')
+if st.button('Load Model', disabled=False):   
     with st.spinner('Wait for it...'):
         import torch
         import numpy as np
@@ -55,10 +57,9 @@ if st.button('Load Model', disabled=True):
                 prediction = 'Spam' if np.argmax(output.logits.cpu().numpy()).flatten().item() == 1 else 'Normal'
                 pred = 'Predicted Class: '+ prediction
                 return pred
-        flag = True      
-if flag:              
+        flag = False      
+if not flag:       
     with col2:
-        text = st.text_input("Enter the text you'd like to analyze for spam.")
         if text or st.button('Analyze'):
             st.header(predict(text))