| import gradio as gr | |
| from transformers import pipeline | |
| #from fairseq.models.transformer import TransformerModel | |
| # Load the English to Urdu translation model from the transformers library | |
| model_name_or_path = "Helsinki-NLP/opus-mt-en-ur" | |
| #model_name_or_path = TransformerModel.from_pretrained('samiulhaq/iwslt-bt-en-ur') | |
| translator = pipeline("translation", model=model_name_or_path, tokenizer=model_name_or_path) | |
| # Create a Gradio interface for the translation app | |
| def translate(text): | |
| # Use the translator pipeline to translate the input text | |
| result = translator(text, max_length=500) | |
| return result[0]['translation_text'] | |
| input_text = gr.inputs.Textbox(label="Input English Text") | |
| output_text = gr.outputs.Textbox(label="Output Urdu Text") | |
| app = gr.Interface(fn=translate, inputs=input_text, outputs=output_text) | |
| # Launch the app | |
| app.launch() | |