| import gradio | |
| from gpt import get_model | |
| model_small, tokenizer_small = get_model("gpt2") | |
| def predict(inp, model_type): | |
| if model_type == "gpt2-small": | |
| model, tokenizer = model_small, tokenizer_small | |
| input_ids = tokenizer.encode("user:"+inp+"\nai:", return_tensors='tf') | |
| beam_output = model.generate(input_ids, max_length=180, 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) | |
| if output.count(".") >= 2: | |
| output = ".".join(output.split(".")[:-1]) + "." | |
| return output | |
| INPUTS = [gradio.inputs.Textbox(lines=2, label="Input Text"), | |
| gradio.inputs.Radio(choices=["gpt2-small"], | |
| label="Model Size")] | |
| OUTPUTS = gradio.outputs.Textbox() | |
| examples = [ | |
| ["The toughest thing about software engineering is", "gpt2-small"], | |
| ["Is this the real life? Is this just fantasy?", "gpt2-small"] | |
| ] | |
| INTERFACE = gradio.Interface(fn=predict, inputs=INPUTS, outputs=OUTPUTS, title="Chat GPT-2", | |
| description="GPT-2 is a large transformer-based language " | |
| "model with 1.5 billion parameters, trained on " | |
| "a dataset of 8 million web pages. GPT-2 is " | |
| "trained with a simple objective: predict the " | |
| "next word, given all of the previous words " | |
| "within some text. You can configure small vs " | |
| "large below: the large model takes longer to " | |
| "run (" | |
| "55s vs 30s) " | |
| "but generates better text.", | |
| thumbnail="https://github.com/gradio-app/gpt-2/raw/master/screenshots/interface.png?raw=true", | |
| examples=examples, | |
| capture_session=False) | |
| INTERFACE.launch(inbrowser=True) |