| import gradio | |
| from transformers import GPT2LMHeadModel, GPT2Tokenizer, AdamW | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| model_name = "zaanind/gpt2_finetune_alpaca" | |
| tokenizer = GPT2Tokenizer.from_pretrained(model_name) | |
| tokenizer.pad_token = tokenizer.eos_token | |
| model = GPT2LMHeadModel.from_pretrained(model_name) | |
| def translate(text): | |
| prompt = f"<s>[INST] translate this sentence to sinhala - {text} [/INST] sure,here the translation of the provided text - " | |
| input_ids = tokenizer.encode(prompt, return_tensors='pt') | |
| output = model.generate(input_ids, max_length=250, num_return_sequences=1) | |
| translation = tokenizer.decode(output[0], skip_special_tokens=True) | |
| return translation | |
| def nmtapifunc(text): | |
| text = translate(text) | |
| return text | |
| gradio_interface = gradio.Interface( | |
| fn=nmtapifunc, | |
| inputs="text", | |
| outputs="text", | |
| title="ZoomAI Inference Server", | |
| description="", | |
| article="© Zaanind 2023-2024" | |
| ) | |
| gradio_interface.launch() |