app.py

import streamlit as st
from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer

def main():
    # Load the model and tokenizer
    model_name = "microsoft/resnet-50"
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    
    # Initialize the pipeline
    sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
    
    st.title("Sentiment Analysis with HuggingFace Spaces")
    st.write("Enter a sentence to analyze its sentiment:")
    
    user_input = st.text_input("")
    if user_input:
        try:
            # Debugging: Print the tokenized input
            tokenized_input = tokenizer(user_input, return_tensors="pt")
            st.write("Tokenized Input:", tokenized_input)
            
            result = sentiment_pipeline(user_input)
            sentiment = result["label"]
            confidence = result["score"]

            st.write(f"Sentiment: {sentiment}")
            st.write(f"Confidence: {confidence:.2f}")
        except Exception as e:
            st.write(f"Error: {e}")

if __name__ == "__main__":
    main()