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| import streamlit as st | |
| from streamlit import session_state | |
| # Load model directly | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| from transformers import pipeline | |
| tokenizer = AutoTokenizer.from_pretrained("themeetjani/tweet-classification") | |
| model = AutoModelForSequenceClassification.from_pretrained("themeetjani/tweet-classification") | |
| classifier = pipeline("text-classification", model= model, tokenizer = tokenizer, truncation=True, max_length=512) | |
| st.set_page_config(page_title="Classification", page_icon="📈") | |
| if 'tweet_class' not in session_state: | |
| session_state['tweet_class']= "" | |
| def classify(tweet): | |
| predicted_classes= session_state['tweet_class']= classifier(tweet, top_k=1) | |
| print (tweet) | |
| print (predicted_classes) | |
| session_state['tweet_class'] = predicted_classes[0]['label'] | |
| st.title("Tweet Classifier") | |
| tweet= st.text_area(label= "Please write the tweet bellow", | |
| placeholder="What does the tweet say?") | |
| st.text_area("result", value=session_state['tweet_class']) | |
| st.button("Classify", on_click=classify, args=[tweet]) | |