app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

# Load model
MODEL = "cardiffnlp/twitter-roberta-base-sentiment-latest"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
sentiment_model = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

# Function for Gradio
def analyze_sentiment(text):
    result = sentiment_model(text)[0]
    return f"Sentiment: {result['label']} | Confidence: {result['score']:.2f}"

# Example texts
examples = [
    ["I absolutely love this new phone, the camera is stunning!"],
    ["I hate the way this app keeps crashing."],
    ["It’s fine, nothing special but not terrible either."],
] 

# Gradio UI
demo = gr.Interface(
    fn=analyze_sentiment,
    inputs=gr.Textbox(lines=3, placeholder="Type a sentence here..."),
    outputs="text",
    examples=examples,
    title="Tweet Sentiment Analyzer",
    description="A simple Hugging Face Space to analyze sentiment in tweets using CardiffNLP's RoBERTa model."
)

if __name__ == "__main__":
    demo.launch()