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Runtime error
| import streamlit as st | |
| from transformers import pipeline, DistilBertTokenizerFast | |
| st.title("Toxic Tweets") | |
| models = [ | |
| "notbhu/toxic-tweet-classifier", | |
| "distilbert-base-uncased-finetuned-sst-2-english", | |
| "cardiffnlp/twitter-roberta-base-sentiment", | |
| "Seethal/sentiment_analysis_generic_dataset", | |
| ] | |
| default_tweet = """🐰🌸🐣 Happy Easter 🌸🐰🐣! It's time to crack open some eggs 🥚 and celebrate with the Easter Bunny 🐰🐇. Hop 🐇 on over to church ⛪️ and get down on your knees 🧎♂️🙏 for some Easter blessings 🐰✝️🌷. Did you know that Jesus 🙏💒 died and rose again 💀🙌🌅? It's a time for rejoicing 🎉 and enjoying the company of loved ones 👨👩👧👦. So put on your Sunday best 👗 and get ready to hunt 🕵️♀️ for some Easter treats 🍫🥚🍭. Happy Easter, bunnies 🐰👯♀️! Don't forget to spread the love ❤️ and send this message to your favorite bunnies 💌🐇. | |
| """ | |
| st.image( | |
| "https://www.gannett-cdn.com/presto/2022/04/12/USAT/3a93e183-d87d-493a-97a9-cf75fb7b9d18-AP_Pennsylvania_Easter.jpg" | |
| ) | |
| tweet = st.text_area("Enter a tweet", value=default_tweet) | |
| model = st.selectbox("Select a model", models) | |
| button = st.button("Predict") | |
| def getLabel(label, model): | |
| labels = { | |
| "notbhu/toxic-tweet-classifier": { | |
| "LABEL_0": "toxic", | |
| "LABEL_1": "severe_toxic", | |
| "LABEL_2": "obscene", | |
| "LABEL_3": "threat", | |
| "LABEL_4": "insult", | |
| "LABEL_5": "identity_hate", | |
| }, | |
| "distilbert-base-uncased-finetuned-sst-2-english": { | |
| "POSITIVE": "POSITIVE", | |
| "NEGATIVE": "NEGATIVE", | |
| }, | |
| "cardiffnlp/twitter-roberta-base-sentiment": { | |
| "LABEL_0": "NEGATIVE", | |
| "LABEL_1": "NEUTRAL", | |
| "LABEL_2": "POSITIVE", | |
| }, | |
| "Seethal/sentiment_analysis_generic_dataset": { | |
| "LABEL_0": "NEGATIVE", | |
| "LABEL_1": "POSITIVE", | |
| }, | |
| } | |
| return labels[model][label] | |
| def predict(tweet, model): | |
| with st.spinner("Predicting..."): | |
| tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased") | |
| classifier = pipeline(model=model, tokenizer=tokenizer) | |
| try: | |
| result = classifier(tweet) | |
| label = result[0]["label"] | |
| score = result[0]["score"] | |
| label = getLabel(label, model) | |
| if label == "POSITIVE": | |
| st.balloons() | |
| st.info(f"Label: {label} \n\n Score: {score}") | |
| except Exception as e: | |
| st.error("Something went wrong") | |
| st.error(e) | |
| if button: | |
| if not tweet: | |
| st.warning("Please enter a tweet") | |
| else: | |
| predict(tweet, model) | |
| elif tweet: | |
| predict(tweet, model) |