app.py

import gradio as gr
import torch
from transformers import BertForSequenceClassification, BertTokenizer

# Load the tokenizer from Hugging Face
token_model = "indolem/indobertweet-base-uncased"
tokenizer = BertTokenizer.from_pretrained(token_model)

# Define the model directory where your config.json and pytorch_model.bin are located
model_directory = "pretrained_arief.model"  # Make sure this directory has config.json and pytorch_model.bin

# Load the model
# If your weights are named differently, ensure the file is named pytorch_model.bin or modify the loading method
model = BertForSequenceClassification.from_pretrained(model_directory)
model.eval()  # Set the model to evaluation mode

# Check if CUDA is available and set the device accordingly
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)

def classify_transaction(notes):
    # Tokenize the input text
    inputs = tokenizer.encode_plus(
        notes,
        None,
        add_special_tokens=True,
        max_length=256,
        padding='max_length',
        return_token_type_ids=False,
        return_attention_mask=True,
        truncation=True,
        return_tensors='pt'
    )

    # Move tensors to the same device as the model
    input_ids = inputs['input_ids'].to(device)
    attention_mask = inputs['attention_mask'].to(device)

    # Model in evaluation mode
    model.eval()

    # Make prediction
    with torch.no_grad():
        outputs = model(input_ids, attention_mask=attention_mask)

    # Extract logits and convert to probabilities
    logits = outputs[0]
    probabilities = torch.softmax(logits, dim=1)

    # Get the predicted class
    predicted_class = torch.argmax(probabilities, dim=1).cpu().numpy()

    # Return the predicted class
    return f"Predicted Category: {predicted_class}"

# Creating the Gradio interface
iface = gr.Interface(
    fn=classify_transaction,
    inputs=gr.Textbox(lines=3, placeholder="Enter Transaction Notes Here", label="Transaction Notes"),
    outputs=gr.Text(label="Classification Result"),
    title="Transaction Category Classifier",
    description="Enter transaction notes to get the predicted category.",
    live=True  # Update the output as soon as the input changes
)

if __name__ == "__main__":
    iface.launch()