| import gradio as gr | |
| import tensorflow | |
| from transformers import TFGPT2LMHeadModel, GPT2Tokenizer | |
| tokenizer = GPT2Tokenizer.from_pretrained("gpt2") | |
| model = TFGPT2LMHeadModel.from_pretrained("gpt2",pad_token_id=tokenizer.eos_token_id) | |
| def generate_text(inp): | |
| input_ids = tokenizer.encode(inp, return_tensors='tf') | |
| beam_output = model.generate(input_ids, max_length=109, num_beams=5, no_repeat_ngram_size=2, early_stopping=True) | |
| output = tokenizer.decode(beam_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=True) | |
| return ".".join(output.split(".")[:-1]) + "." | |
| output_text = gr.outputs.Textbox() | |
| gr.Interface(generate_text,"textbox",output_text, title="MAX-GPT",description = "Chat and ask about anything, leave unfinished sentences for autocomplete or just ask questions revolving any topic").launch(share=True) |