app.py

import gradio as gr
from transformers import MarianMTModel, MarianTokenizer

# Assuming the environment is set up for GPU use if available
# This is more about the environment setup than code modification

# Define a function that loads a model and tokenizer based on the chosen language
def load_model(lang_pair):
    model_name = {
        "English to French": 'Helsinki-NLP/opus-mt-en-fr',
        "Kinyarwanda to English": 'Helsinki-NLP/opus-mt-rw-en'
    }[lang_pair]
    
    tokenizer = MarianTokenizer.from_pretrained(model_name)
    model = MarianMTModel.from_pretrained(model_name)
    return model, tokenizer

# Example function that could be used for caching (conceptual implementation)
cache = {}
def get_translation_from_cache_or_model(model, tokenizer, text):
    if text in cache:
        return cache[text]
    model_inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    gen = model.generate(**model_inputs)
    translation = tokenizer.batch_decode(gen, skip_special_tokens=True)[0]
    cache[text] = translation
    return translation

# Function to translate text based on selected language
def translate(lang_pair, text):
    model, tokenizer = load_model(lang_pair)
    # Use the caching function
    translation = get_translation_from_cache_or_model(model, tokenizer, text)
    return translation

# Create a Gradio interface with a dropdown menu for language selection
iface = gr.Interface(
    fn=translate,
    inputs=[gr.Dropdown(choices=["English to French", "Kinyarwanda to English"], label="Select Language Pair"),
            gr.Textbox(lines=2, placeholder="Enter Text...")],
    outputs=gr.Textbox(label="Translation")
)

# Launch the interface
iface.launch(debug=True,inline=False)