app.py

import gradio as gr
from transformers import MarianMTModel, MarianTokenizer

# Specify the model name from the Hugging Face Hub, for example, an English to French model by the University of Helsinki
model_name = "Helsinki-NLP/opus-mt-en-fr"

# Load the tokenizer and model
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)

# Function to handle translation
def translate_text(text, target_language):
    # Adjust the model_name based on the target language
    # Note: You'd need to find the exact model names for each language pair you want to support
    model_name_map = {
        "French": "Helsinki-NLP/opus-mt-en-fr",
        "German": "Helsinki-NLP/opus-mt-en-de",
        "Spanish": "Helsinki-NLP/opus-mt-en-es",
    }
    
    selected_model_name = model_name_map.get(target_language, "Helsinki-NLP/opus-mt-en-fr")
    
    # Load the selected model and tokenizer
    tokenizer = MarianTokenizer.from_pretrained(selected_model_name)
    model = MarianMTModel.from_pretrained(selected_model_name)
    
    # Prepare the text for translation
    encoded_text = tokenizer.prepare_seq2seq_batch([text], return_tensors="pt")
    
    # Perform the translation
    translated = model.generate(**encoded_text)
    
    # Decode the translated text
    translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
    
    return translated_text

# Define the interface
iface = gr.Interface(
    fn=translate_text,
    inputs=[gr.inputs.Textbox(lines=2, placeholder="Enter text to translate..."), gr.inputs.Dropdown(["French", "German", "Spanish"], label="Select Language")],
    outputs=[gr.outputs.Textbox()],
    title="Text Translator with Helsinki NLP Models",
    description="Select a language to translate English text into using University of Helsinki models."
)

# Launch the app
iface.launch()