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from transformers import AutoTokenizer, AutoModelForSequenceClassification
import streamlit as st
import torch
# Load tokenizer and model from Hugging Face Hub
tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
# Streamlit UI setup
st.title("Sentiment Analysis App using GenAI Models")
# Text input from the user
user_input = st.text_area("Enter text to analyze sentiment:")
# Prediction button
if st.button("Analyze"):
if user_input:
# Tokenize the user input
inputs = tokenizer(user_input, return_tensors="pt")
# Perform inference
with torch.no_grad():
outputs = model(**inputs)
# Interpret the results
predicted_class = torch.argmax(outputs.logits, dim=1).item()
sentiment = ["Negative", "Neutral", "Positive"][predicted_class] # Assuming 3 classes
st.write(f"**Predicted Sentiment:** {sentiment}")
else:
st.warning("Please enter some text to analyze.")
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