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from transformers import AutoTokenizer, AutoModelWithLMHead
from urllib.request import urlretrieve
import gradio as gr
# Loads latest model state from Github
urlretrieve("https://github.com/equ1/generative_python_transformer/tree/main/GPT-python")
# inference function
def inference(inp):
tokenizer = AutoTokenizer.from_pretrained("GPT-python").to("cuda")
model = AutoModelWithLMHead.from_pretrained("GPT-python").to("cuda")
input_ids = tokenizer.encode(inp, return_tensors="pt").to("cuda")
beam_output = model.generate(input_ids,
max_length=512,
num_beams=10,
temperature=0.7,
no_repeat_ngram_size=5,
num_return_sequences=1,
).to("cuda")
output = []
for beam in beam_output:
out = tokenizer.decode(beam)
fout = out.replace("<N>", "\n")
output.append(fout)
return '\n'.join(output)
desc = """
Enter some Python code and click submit to see the model's autocompletion.\n
Best results have been observed with the prompt of \"import\".\n
Please note that outputs are reflective of a model trained on a measly 40 MBs of text data for
a single epoch of ~16 GPU hours. Given more data and training time, the autocompletion should be much stronger.\n
Computation will take some time.
"""
# Creates and launches gradio interface
gr.Interface(fn=inference,
inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
outputs=gr.outputs.Textbox(),
title="Generative Python Transformer",
description=desc,
).launch()
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