| import gradio as gr |
| from transformers import pipeline |
| pipeline = pipeline("text-generation", model="not-lain/PyGPT") |
|
|
| def format_input(instruction,inp): |
| prefix = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n" |
| txt = prefix + f"### Instruction:\n{instruction}"+ f"\n\n### Input:{inp}"+"\n\n### Output:\n" |
| return txt |
|
|
| def process_markdown(out): |
| mark = f"""```py |
| {out} |
| ```""" |
| return mark |
|
|
| def generate_text(length,instruction,inp): |
| if instruction == None : |
| instruction = "" |
| if inp == None : |
| inp = "" |
| txt = format_input(instruction,inp) |
| out = pipeline(txt, max_length=len(txt)+length)[0]["generated_text"] |
| out = out.split("Output:\n")[1] |
| mark = process_markdown(out) |
| return mark |
|
|
|
|
| MARKDOWN_TEXT = """ |
| # PyGPT Text Generation Demo |
| this is a demo using the [PyGPT model](https://huggingface.co/not-lain/PyGPT) to generate text based on an input instruction and input text. |
| the model is based on the [GPT-2 model](https://huggingface.co/gpt2) and finetuned on the [python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca) dataset. |
| """ |
|
|
|
|
|
|
| with gr.Blocks() as iface: |
| gr.Markdown(MARKDOWN_TEXT) |
| length = gr.Slider(1, 100, 50, label="Max Length") |
| instruction = gr.Text(label= "instruction") |
| inp = gr.Text(label="input") |
| out = gr.Markdown(label="output") |
| submit = gr.Button("submit") |
| submit.click(generate_text,inputs=[length,instruction,inp],outputs=out) |
| gr.Examples([ |
| [50,"Create a function to calculate the sum of a sequence of integers.","[1, 2, 3, 4, 5]"], |
| [50,"Generate a Python code for crawling a website for a specific type of data.","website: www.example.com data to crawl: phone numbers"]], |
| inputs = [length,instruction,inp], |
| outputs= [out], |
| fn=generate_text, |
| cache_examples=True) |
|
|
| |
|
|
| iface.launch(debug=True) |
