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import streamlit as st
import pandas as pd
import torch
from transformers import RobertaTokenizer, RobertaForSequenceClassification
# Load model
model_name = "syedkhalid076/RoBERTa-Sentimental-Analysis-Model"
tokenizer = RobertaTokenizer.from_pretrained(model_name)
model = RobertaForSequenceClassification.from_pretrained(model_name)
model.eval()
# Function to predict sentiment for a single sentence
def predict_sentiment(sentence):
inputs = tokenizer(sentence, return_tensors="pt", max_length=512, truncation=True)
outputs = model(**inputs)
logits = outputs.logits.detach().cpu().numpy()
sentiment = "positive" if logits[0][1] > logits[0][0] else "negative"
return sentiment
# Function to process CSV file and predict sentiment for each row
def process_csv(file):
df = pd.read_csv(file)
df['Sentiment'] = df['Text'].apply(predict_sentiment)
return df
# Streamlit app
def main():
st.title("Sentiment Analysis App")
st.write("Write a sentence or upload a CSV file to analyze sentiment.")
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")
option = st.radio("Choose input type:", ("Write a sentence", "Upload a CSV file"))
if option == "Write a sentence":
sentence = st.text_input("Enter a sentence:")
if st.button("Analyze"):
sentiment = predict_sentiment(sentence)
st.write("Sentiment:", sentiment)
elif option == "Upload a CSV file":
file = st.file_uploader("Upload CSV file", type=['csv'])
if file is not None:
df = process_csv(file)
st.write(df)
if __name__ == '__main__':
main()
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