app.py

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

# Select the appropriate device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load the model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("./", local_files_only=True).to(device)
tokenizer = AutoTokenizer.from_pretrained("gpt2")

def classify_text(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512).to(device)
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits
    predicted_class_id = logits.argmax().item()
    return "Proper Naming Notfcn" if predicted_class_id == 1 else "Wrong Naming Notificn"

iface = gr.Interface(fn=classify_text, inputs="text", outputs="text", title="Classification Naming", description="Classify naming notifications as proper or wrong.")
iface.launch()