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import gradio as gr
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# Load pre-trained model and tokenizer
model_name = "t5-small"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Function to translate text
def translate_text(text, source_lang, target_lang):
input_text = f"translate {source_lang} to {target_lang}: {text}"
inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
outputs = model.generate(**inputs)
translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
return translation
# List of Indian languages
indian_languages = [
"as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te", "ur"
]
# Supported languages
languages = ["en", "fr", "de", "es", "it"] + indian_languages
# Create Gradio interface
def translate_interface(text, source_lang, target_lang):
return translate_text(text, source_lang, target_lang)
iface = gr.Interface(
fn=translate_interface,
inputs=[
gr.Textbox(lines=2, placeholder="Enter text to translate"),
gr.Dropdown(choices=languages, label="Source Language"),
gr.Dropdown(choices=languages, label="Target Language")
],
outputs="text",
title="Hugging Face Translation App",
description="Translate text from one language to another using a T5 model."
)
if __name__ == "__main__":
iface.launch()
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