Create app.py

app.py

@@ -0,0 +1,36 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
+from transformers import pipeline
+
+
+with open('labels.txt') as f:
+    LABEL2STR = f.readline().split()
+
+
+@st.cache(allow_output_mutation=True)
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("kirillbogatiy/model_topics")
+    model = AutoModelForSequenceClassification.from_pretrained("kirillbogatiy/model_topics")
+    pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
+    return pipe
+    
+    
+def pretty_output(predictions, thr=0.95):
+    cumulative_score = 0
+    st.write('Possible topics:')
+    for label, data in enumerate(sorted(predictions[0], key=lambda item: item['score'], reverse=True)):
+        score = data['score']
+        cumulative_score += score
+        st.write('{}: {} %'.format(LABEL2STR[label], round(100 * score, 2)))
+        if cumulative_score >= thr:
+            return
+      
+
+
+if __name__ == '__main__':
+    title = st.text_input('Input a title here:') 
+    abstract = st.text_input('Input an abstract here:')
+    pipe = load_model()
+    if title:
+        predictions = pipe('Title: {}\n\nAbstract: {}'.format(title, abstract))
+        pretty_output(predictions)