Update app.py
Browse files
app.py
CHANGED
@@ -6,12 +6,13 @@ st.set_page_config(layout='wide', initial_sidebar_state='expanded')
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col1, col2= st.columns(2)
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placeholder = st.empty()
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placeholder2 = st.empty()
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with col1:
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st.title("Spamd: Turkish Spam Detector")
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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.")
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with col2:
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text = placeholder.text_input("Enter the text you'd like to analyze for spam.", disabled=True, key="1")
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aButton = placeholder2.button('Analyze', disabled=True, key="1")
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if st.button('Load Model', disabled=False):
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with st.spinner('Wait for it...'):
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import torch
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@@ -61,6 +62,8 @@ if st.button('Load Model', disabled=False):
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placeholder.text_input("Enter the text you'd like to analyze for spam.", disabled=False, key="2")
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placeholder2.button('Analyze', disabled=False, key="2")
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if text or aButton:
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with col2:
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st.header(predict(text))
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col1, col2= st.columns(2)
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placeholder = st.empty()
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placeholder2 = st.empty()
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with col2:
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text = placeholder.text_input("Enter the text you'd like to analyze for spam.", disabled=True, key="1")
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aButton = placeholder2.button('Analyze', disabled=True, key="1")
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with col1:
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st.title("Spamd: Turkish Spam Detector")
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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.")
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if st.button('Load Model', disabled=False):
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with st.spinner('Wait for it...'):
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import torch
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placeholder.text_input("Enter the text you'd like to analyze for spam.", disabled=False, key="2")
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placeholder2.button('Analyze', disabled=False, key="2")
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if text or aButton:
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placeholder.text_input("Enter the text you'd like to analyze for spam.", disabled=False, key="3")
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placeholder2.button('Analyze', disabled=False, key="3")
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with col2:
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st.header(predict(text))
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