app.py

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

# Check if GPU is available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load the IMDb dataset
dataset = load_dataset('imdb', split='test[:1%]')  # Load a small portion for testing

# Initialize the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2)
model.to(device)

# Function to classify sentiment
def classify_text(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
    outputs = model(**inputs)
    prediction = torch.argmax(outputs.logits, dim=-1).item()
    return "Positive" if prediction == 1 else "Negative"

# Set up the Gradio interface
iface = gr.Interface(fn=classify_text, inputs="text", outputs="text")
iface.launch()