app.py

import torch
import gradio as gr

from model import create_marian_enfr

# Setup model and tokenizer
model, tokenizer = create_marian_enfr()

# Load state dict from model
model.load_state_dict(
    torch.load(
        f="marian_finetuned_kde4_enfr.pth",
        map_location=torch.device("cpu")
    ))

# Predict function
def predict(text:str):

  # Tokenize inputs and get model outputs
  input = tokenizer(text, 
                    max_length=128,
                    truncation=True,
                    return_tensors="pt")
  output_tokens = model.generate(**input)
  output_text = tokenizer.batch_decode(output_tokens, 
                                       skip_special_tokens=True)
  
  return output_text

# Create examples list
examples_list = ['What a beautiful day',
                 'I love music']

# Create gradio app
title = "English to French translator"
description = "Marian model finetuned for english to french translation on the kde4 dataset."

demo = gr.Interface(fn=predict,
                    inputs=gr.inputs.Textbox(label="Input",
                                             placeholder="Enter sentence here..."),
                    outputs="text",
                    examples=examples_list,
                    title=title,
                    description=description)

# Launch gradio
demo.launch()