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import streamlit as st | |
from transformers import BertTokenizer, BertForSequenceClassification | |
import torch | |
# Load the fine-tuned model and tokenizer | |
model_path = 'path/to/your/fine-tuned/model' | |
tokenizer = BertTokenizer.from_pretrained(model_path) | |
model = BertForSequenceClassification.from_pretrained(model_path) | |
# Set the model to evaluation mode | |
model.eval() | |
# Function to perform sentiment analysis | |
def analyze_sentiment(text): | |
inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True) | |
with torch.no_grad(): | |
outputs = model(**inputs) | |
logits = outputs.logits | |
predicted_class = logits.argmax().item() | |
return predicted_class | |
# Create a Streamlit app | |
st.title("Sentiment Analysis App") | |
# Get the user input | |
text = st.text_area("Enter text for sentiment analysis") | |
if st.button("Analyze Sentiment"): | |
if text: | |
sentiment_class = analyze_sentiment(text) | |
sentiment_labels = ['Negative', 'Neutral', 'Positive'] # Adjust as needed | |
sentiment = sentiment_labels[sentiment_class] | |
st.write(f"Sentiment: {sentiment}") | |
else: | |
st.warning("Please enter text for analysis.") | |