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import gradio as gr | |
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
# Dictionary to hold models and tokenizers for each language | |
models = {} | |
tokenizers = {} | |
# List of language pairs | |
language_pairs = { | |
"English to French": "Helsinki-NLP/opus-mt-en-fr", | |
"English to Chinese": "Helsinki-NLP/opus-mt-en-zh", | |
"English to German": "Helsinki-NLP/opus-mt-en-de", | |
"English to Urdu": "Helsinki-NLP/opus-mt-en-ur" | |
} | |
# Load models and tokenizers for each language pair | |
for lang, model_name in language_pairs.items(): | |
tokenizers[lang] = AutoTokenizer.from_pretrained(model_name) | |
models[lang] = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
# Function to perform translation | |
def translate_text(text, language_choice): | |
# Select the appropriate tokenizer and model based on the chosen language | |
tokenizer = tokenizers[language_choice] | |
model = models[language_choice] | |
# Tokenize the input text | |
inputs = tokenizer(text, return_tensors="pt", truncation=True) | |
# Generate the translation | |
outputs = model.generate(**inputs) | |
# Decode the translated text | |
translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
return translated_text | |
# Define the Gradio interface | |
def gradio_interface(text, language_choice): | |
translated_text = translate_text(text, language_choice) | |
return translated_text | |
# Create a list of language choices for the dropdown | |
language_choices = list(language_pairs.keys()) | |
# Set up the Gradio app | |
interface = gr.Interface( | |
fn=gradio_interface, | |
inputs=[gr.Textbox(lines=2, placeholder="Enter text here..."), | |
gr.Dropdown(choices=language_choices, label="Select Target Language")], | |
outputs=gr.Textbox(label="Translated Text"), | |
title="Multi-Language Translation App" | |
) | |
# Launch the app | |
interface.launch() | |