app.py

import gradio as gr
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
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')

# 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)

# Tokenize the dataset
def tokenize_function(examples):
    return tokenizer(examples['text'], padding="max_length", truncation=True)

tokenized_datasets = dataset.map(tokenize_function, batched=True)

# Set up training arguments
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=1,  # Start with fewer epochs for quicker runs
    weight_decay=0.01,
)

# Initialize the Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets["train"].shuffle(seed=42).select(range(1000)),  # Use a subset for quicker runs
    eval_dataset=tokenized_datasets["test"].shuffle(seed=42).select(range(1000)),
)

# Train the model
trainer.train()

# 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()