app.py

import gradio as gr
import torch
import spaces
from transformers import RobertaForSequenceClassification, RobertaTokenizer

# Load model and tokenizer
model_dir = "roberta_imdb_finetuned"
tokenizer = RobertaTokenizer.from_pretrained(model_dir)
model = RobertaForSequenceClassification.from_pretrained(model_dir).to("cuda")

# Set model to evaluation mode
model.eval()

# Define inference function
@spaces.GPU
def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512).to("cuda")
    
    with torch.no_grad():
        outputs = model(**inputs)
    
    logits = outputs.logits
    prediction = torch.argmax(logits, dim=1).item()
    
    label_map = {0: "Negative", 1: "Positive"}
    return f"Predicted Sentiment: {label_map[prediction]}"

# Gradio Interface
gr.Interface(
    fn=predict_sentiment,
    inputs="text",
    outputs="text",
    title="Roberta Movie Review Classifier"
).launch()