app.py

import gradio as gr
import torch
from src.model import BertClassifier, RobertaClassifier
from transformers import BertTokenizer
from datetime import datetime

device = torch.device('cpu')
model_name = 'bert-base-uncased'
model = BertClassifier(model_name, 0.5)
model.to(device)
model.load_state_dict(torch.load('models/bert-all-data.pth', map_location=device))
tokenizer = BertTokenizer.from_pretrained(model_name)

def ai_text_classifier(text: str) -> dict:    
    # Convert Text into tokens
    tokens = tokenizer(text, return_tensors='pt', max_length=512, padding='max_length', truncation=True).to(device)
    
    # Get probability of the text
    prob = model(tokens['input_ids'], tokens['attention_mask']).item()
    
    # Return the probability in dictionary
    return {
        "AI": prob, 
        'Others': 1 - prob
    }

demo = gr.Interface(fn=ai_text_classifier, inputs="text", outputs="label")
demo.launch()