# Fork of the SantaCoder demo (https://huggingface.co/spaces/bigcode/santacoder-demo)
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
from transformers import pipeline
import os
import torch
from typing import Union, Tuple, List
description = """# <p style="text-align: center; color: #292b47;"> ποΈ <span style='color: #3264ff;'>DeciCoder:</span> A Fast Code Generation Modelπ¨ </p>
<span style='color: #292b47;'>Welcome to <a href="https://huggingface.co/deci/decicoder" style="color: #3264ff;">DeciCoder</a>!
DeciCoder is a 1B parameter code generation model trained on The Stack dataset and released under an Apache 2.0 license. It's capable of writing code in Python,
JavaScript, and Java. It's a code-completion model, not an instruction-tuned model; you should prompt the model with a function signature and docstring
and let it complete the rest. The model can also do infilling, specify where you would like the model to complete code with the <span style='color: #3264ff;'><FILL_HERE></span>
token.</span>"""
token = os.environ["HUGGINGFACEHUB_API_TOKEN"]
device="cuda" if torch.cuda.is_available() else "cpu"
FIM_PREFIX = "<fim_prefix>"
FIM_MIDDLE = "<fim_middle>"
FIM_SUFFIX = "<fim_suffix>"
FIM_PAD = "<fim_pad>"
EOD = "<|endoftext|>"
GENERATION_TITLE= "<p style='font-size: 24px; color: #292b47;'>π» Your generated code:</p>"
tokenizer_fim = AutoTokenizer.from_pretrained("bigcode/starcoder", use_auth_token=token, padding_side="left")
tokenizer_fim.add_special_tokens({
"additional_special_tokens": [EOD, FIM_PREFIX, FIM_MIDDLE, FIM_SUFFIX, FIM_PAD],
"pad_token": EOD,
})
tokenizer = AutoTokenizer.from_pretrained("bigcode/starcoder", use_auth_token=token)
model = AutoModelForCausalLM.from_pretrained("Deci/DeciCoder-1b", trust_remote_code=True, use_auth_token=token).to(device)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device=device)
def post_processing(prompt: str, completion: str) -> str:
"""
Post-processes the generated code completion with HTML styling.
Args:
prompt (str): The input code prompt.
completion (str): The generated code completion.
Returns:
str: The HTML-styled code with prompt and completion.
"""
completion = "<span style='color: #ff5b86;'>" + completion + "</span>"
prompt = "<span style='color: #7484b7;'>" + prompt + "</span>"
code_html = f"<br><hr><br><pre style='font-size: 12px'><code>{prompt}{completion}</code></pre><br><hr>"
return GENERATION_TITLE + code_html
def post_processing_fim(prefix: str, middle: str, suffix: str) -> str:
"""
Post-processes the FIM (fill in the middle) generated code with HTML styling.
Args:
prefix (str): The prefix part of the code.
middle (str): The generated middle part of the code.
suffix (str): The suffix part of the code.
Returns:
str: The HTML-styled code with prefix, middle, and suffix.
"""
prefix = "<span style='color: #7484b7;'>" + prefix + "</span>"
middle = "<span style='color: #ff5b86;'>" + middle + "</span>"
suffix = "<span style='color: #7484b7;'>" + suffix + "</span>"
code_html = f"<br><hr><br><pre style='font-size: 12px'><code>{prefix}{middle}{suffix}</code></pre><br><hr>"
return GENERATION_TITLE + code_html
def fim_generation(prompt: str, max_new_tokens: int, temperature: float) -> str:
"""
Generates code for FIM (fill in the middle) task.
Args:
prompt (str): The input code prompt with <FILL_HERE> token.
max_new_tokens (int): Maximum number of tokens to generate.
temperature (float): Sampling temperature for generation.
Returns:
str: The HTML-styled code with filled missing part.
"""
prefix = prompt.split("<FILL_HERE>")[0]
suffix = prompt.split("<FILL_HERE>")[1]
[middle] = infill((prefix, suffix), max_new_tokens, temperature)
return post_processing_fim(prefix, middle, suffix)
def extract_fim_part(s: str) -> str:
"""
Extracts the FIM (fill in the middle) part from the generated string.
Args:
s (str): The generated string with FIM tokens.
Returns:
str: The extracted FIM part.
"""
# Find the index of
start = s.find(FIM_MIDDLE) + len(FIM_MIDDLE)
stop = s.find(EOD, start) or len(s)
return s[start:stop]
def infill(prefix_suffix_tuples: Union[Tuple[str, str], List[Tuple[str, str]]], max_new_tokens: int, temperature: float) -> List[str]:
"""
Generates the infill for the given prefix and suffix tuples.
Args:
prefix_suffix_tuples (Union[Tuple[str, str], List[Tuple[str, str]]]): Prefix and suffix tuples.
max_new_tokens (int): Maximum number of tokens to generate.
temperature (float): Sampling temperature for generation.
Returns:
List[str]: The list of generated infill strings.
"""
if type(prefix_suffix_tuples) == tuple:
prefix_suffix_tuples = [prefix_suffix_tuples]
prompts = [f"{FIM_PREFIX}{prefix}{FIM_SUFFIX}{suffix}{FIM_MIDDLE}" for prefix, suffix in prefix_suffix_tuples]
# `return_token_type_ids=False` is essential, or we get nonsense output.
inputs = tokenizer_fim(prompts, return_tensors="pt", padding=True, return_token_type_ids=False).to(device)
with torch.no_grad():
outputs = model.generate(
**inputs,
do_sample=True,
temperature=temperature,
max_new_tokens=max_new_tokens,
pad_token_id=tokenizer.pad_token_id
)
# WARNING: cannot use skip_special_tokens, because it blows away the FIM special tokens.
return [
extract_fim_part(tokenizer_fim.decode(tensor, skip_special_tokens=False)) for tensor in outputs
]
def code_generation(prompt: str, max_new_tokens: int, temperature: float = 0.2, seed: int = 42) -> str:
"""
Generates code based on the given prompt. Handles both regular and FIM (Fill-In-Missing) generation.
Args:
prompt (str): The input code prompt.
max_new_tokens (int): Maximum number of tokens to generate.
temperature (float, optional): Sampling temperature for generation. Defaults to 0.2.
seed (int, optional): Random seed for reproducibility. Defaults to 42.
Returns:
str: The HTML-styled generated code.
"""
if "<FILL_HERE>" in prompt:
return fim_generation(prompt, max_new_tokens, temperature=temperature)
else:
completion = pipe(prompt, do_sample=True, top_p=0.95, temperature=temperature, max_new_tokens=max_new_tokens)[0]['generated_text']
completion = completion[len(prompt):]
return post_processing(prompt, completion)
demo = gr.Blocks(
css=".gradio-container {background-color: white; color: #292b47}"
)
with demo:
with gr.Row():
_, colum_2, _ = gr.Column(scale=1), gr.Column(scale=6), gr.Column(scale=1)
with colum_2:
gr.Markdown(value=description)
code = gr.Code(lines=5, language="python", label="Input code", value="def nth_element_in_fibonnaci(element):\n \"\"\"Returns the nth element of the Fibonnaci sequence.\"\"\"")
with gr.Accordion("Additional settings", open=True):
max_new_tokens= gr.Slider(
minimum=8,
maximum=2048,
step=1,
value=75,
label="Number of tokens to generate",
)
temperature = gr.Slider(
minimum=0.1,
maximum=2.5,
step=0.01,
value=0.2,
label="Temperature",
)
seed = gr.inputs.Number(
default=42,
label="Enter a seed value (integer)"
)
run = gr.Button(value="π¨π½βπ» Generate code", size='lg')
output = gr.HTML(label="π» Your generated code")
event = run.click(code_generation, [code, max_new_tokens, temperature, seed], output, api_name="predict")
gr.HTML(label="Keep in touch", value="<img src='https://huggingface.co/spaces/Deci/DeciCoder-Demo/resolve/main/deci-coder-banner.png' alt='Keep in touch' style='display: block; color: #292b47; margin: auto; max-width: 800px;'>")
demo.launch()