Add application file

app.py

@@ -0,0 +1,55 @@
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import torch
+import streamlit as st
+
+model_path = "berkaysahiin/bert-base-uncased-jigsaw-toxic-classifier"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+classifier = pipeline(task='text-classification', model=model, tokenizer=tokenizer, return_all_scores=True)
+
+label_to_format = {
+    'toxic': 'Toxic',
+    'severe_toxic': 'Severe Toxic',
+    'obscene' : 'Obscene',
+    'threat' : 'Threat',
+    'identity_hate' : 'Identity Hate',
+    'insult' : 'Insult'
+}
+
+@st.cache_resource
+def test_sentence(sentence: str, thresh_hold=0.2, debug=False):
+    results = classifier(sentence)
+
+    if debug:
+        st.text('Debug is enabled, threshold value will be ignored\n')
+        for result in results[0]:
+            st.text(result)
+        return
+
+    may_include = []
+
+    for result in results[0]:
+        if result['score'] > thresh_hold:
+            formatted_score = "{:.2f}".format(result['score'])
+            formatted_label = label_to_format[result['label']]
+            may_include.append((formatted_label, formatted_score))
+    
+    if may_include:
+        for label, score in may_include:
+            capitalized_label = label.capitalize()
+            st.info(f'{capitalized_label} ({score})')
+    else:
+        st.info('Your sentence is totally fine')
+
+if __name__ == "__main__":
+    st.title('Hate Speech Labeler')
+    user_sentence = st.text_input('Enter your sentence to test it', value='I love NLP')
+    user_threshold = st.slider('Select threshold value', min_value=0.0, max_value=1.0, value=0.4)
+
+    if st.button('Test your sentence'):
+        if user_sentence:
+            st.success('Testing complete!')
+            test_sentence(sentence=user_sentence, thresh_hold=user_threshold)
+        else:
+            st.error('Please enter a sentence before you test it!')