| #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() | |
| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| # Load the English to Urdu translation model from the transformers library | |
| model_name_or_path = "aryanc55/english-urdu" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path) | |
| # Create a Gradio interface for the translation app | |
| def translate(text): | |
| # Tokenize the input text | |
| inputs = tokenizer(text, return_tensors="pt") | |
| # Use the model to generate the translated text | |
| outputs = model.generate(inputs["input_ids"], max_length=500, early_stopping=True) | |
| translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return translated_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() | |