app.py

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

# Load model and tokenizer
model_name = "cardiffnlp/twitter-roberta-base-sentiment-latest"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    sentiments = ['Negative', 'Neutral', 'Positive']
    result = {sentiments[i]: float(predictions[0][i]) for i in range(len(sentiments))}
    return result

# Create Gradio interface
iface = gr.Interface(fn=predict_sentiment,
                     inputs=gr.Textbox(lines=2, placeholder="Type your sentence here..."),
                     outputs=gr.Label(),
                     title="Sentiment Analysis",
                     description="Analyze the sentiment of your text. Powered by CardiffNLP's RoBERTa model.",
                     article="<p style='text-align: center'>Enter a sentence to get its sentiment. The model categorizes sentiments into Negative, Neutral, and Positive.</p>",
                     theme="huggingface")  # Using a built-in theme

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