app.py

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch.nn.functional as F

def load_model(model_directory):
    # Assuming 'config.json' and 'pytorch_model.bin' are in 'model_directory'
    model = AutoModelForSequenceClassification.from_pretrained(model_directory)
    tokenizer = AutoTokenizer.from_pretrained(model_directory)
    return model, tokenizer

def predict(model, tokenizer, input_text):
    # Preprocess the input
    inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
    
    # Move tensors to the same device as the model
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = {k: v.to(device) for k, v in inputs.items()}

    # Model in evaluation mode
    model.eval()
    
    # Make the model generate a prediction
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits

    # Convert logits to probabilities
    probabilities = F.softmax(logits, dim=1)

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

    return predicted_class, probabilities

def main():
    # Replace 'your-model-directory' with the actual path to your model directory
    model_directory = "Kurkur99/modeling"  # e.g., "Kurkur99/Kurkur99/transactionmerchant/model_directory"
    model, tokenizer = load_model(model_directory)

    # Example input text
    input_text = "Example input text for prediction"
    
    # Get predictions
    predicted_class, probabilities = predict(model, tokenizer, input_text)
    
    # Output the results
    print(f"Predicted Class: {predicted_class[0]}")
    print(f"Probabilities: {probabilities[0]}")

if __name__ == "__main__":
    main()