app.py

import gradio as gr
import torch

from ipynb.fs.defs.germanToEnglish import Seq2SeqTransformer, translate

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.load_state_dict(torch.load('./transformer_model.pth', map_location=device))
model.eval()

if __name__ == "__main__":
    # Create the Gradio interface
    iface = gr.Interface(
        fn=translate,  # Specify the translation function as the main function
        inputs=[
            gr.inputs.Textbox(label="Text")

    ],
    outputs=["text"],  # Define the output type as text
    cache_examples=False,  # Disable caching of examples
    title="germanToenglish",  # Set the title of the interface
    )

    # Launch the interface
    iface.launch(share=True)