app.py

from transformers import AutoTokenizer
import transformers
import os
import sys
import fire
import torch
import gradio as gr


def main(
 base_model="ise-uiuc/Magicoder-S-DS-6.7B"
):
 pipeline = transformers.pipeline(
 "text-generation", model=base_model, torch_dtype=torch.bfloat16, device_map="auto"
 )

 def evaluate_magicoder(
 instruction,
 temperature=1,
 max_length=2048,
 ):
 MAGICODER_PROMPT = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.

@@ Instruction
{instruction}

@@ Response
"""
 prompt = MAGICODER_PROMPT.format(instruction=instruction)

 if temperature > 0:
 sequences = pipeline(
 prompt,
 do_sample=True,
 temperature=temperature,
 max_length=max_length,
 )
 else:
 sequences = pipeline(
 prompt,
 max_length=max_length,
 )
 for seq in sequences:
 generated_text = seq["generated_text"].replace(prompt, "")
 return generated_text

 gr.Interface(
 fn=evaluate_magicoder,
 inputs=[
 gr.components.Textbox(
 lines=3,
 label="Instruction",
 placeholder="Anything you want to ask Magicoder ?",
 ),
 gr.components.Slider(minimum=0, maximum=1, value=0, label="Temperature"),
 gr.components.Slider(
 minimum=1, maximum=2048, step=1, value=1024, label="Max tokens"
 ),
 ],
 outputs=[
 gr.components.Textbox(
 lines=30,
 label="Output",
 )
 ],
 title="Magicoder",
 description="This is a playground for Magicoder-S-DS-6.7B! Follow us on Github: https://github.com/ise-uiuc/magicoder and Huggingface: https://huggingface.co/ise-uiuc.",
 ).queue().launch()


if __name__ == "__main__":
 fire.Fire(main)