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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) |