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import streamlit as st |
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import pandas as pd |
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import torch |
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from transformers import RobertaTokenizer, RobertaForSequenceClassification |
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model_name = "syedkhalid076/RoBERTa-Sentimental-Analysis-Model" |
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tokenizer = RobertaTokenizer.from_pretrained(model_name) |
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model = RobertaForSequenceClassification.from_pretrained(model_name) |
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model.eval() |
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def predict_sentiment(sentence): |
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inputs = tokenizer(sentence, return_tensors="pt", max_length=512, truncation=True) |
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outputs = model(**inputs) |
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logits = outputs.logits.detach().cpu().numpy() |
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sentiment = "positive" if logits[0][1] > logits[0][0] else "negative" |
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return sentiment |
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def process_csv(file): |
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df = pd.read_csv(file) |
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df['Sentiment'] = df['Text'].apply(predict_sentiment) |
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return df |
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def main(): |
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st.title("Sentiment Analysis App") |
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st.write("Write a sentence or upload a CSV file to analyze sentiment.") |
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st.write("NOTE: If uploading a CSV file, please rename your column's name with the text/sentence to 'Text', where 'T' is in upper-case and the rest in lower-case") |
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option = st.radio("Choose input type:", ("Write a sentence", "Upload a CSV file")) |
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if option == "Write a sentence": |
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sentence = st.text_input("Enter a sentence:") |
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if st.button("Analyze"): |
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sentiment = predict_sentiment(sentence) |
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st.write("Sentiment:", sentiment) |
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elif option == "Upload a CSV file": |
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file = st.file_uploader("Upload CSV file", type=['csv']) |
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if file is not None: |
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df = process_csv(file) |
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st.write(df) |
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if __name__ == '__main__': |
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main() |
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