app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig

description = """# <p style="text-align: center; color: #292b47;"> 🏎️ <span style='color: #3264ff;'>DeciCoder-6B:</span> A Fast Code Generation Model💨 </p>
<span style='color: #292b47;'>Welcome to <a href="https://huggingface.co/Deci/DeciCoder-6B" style="color: #3264ff;">DeciCoder</a>! 
DeciCoder-6B was trained on the Python, Java, Javascript, Rust, C++, C, and C# subset of the Starcoder Training Dataset, and it's released under the Apache 2.0 license. This model is capable of code-completion and instruction following. It surpasses CodeGen 2.5 7B, CodeLlama 7B, abd StarCoder 7B in its supported languages on HumanEval, and leads by 3 points in Python over StarCoderBase 15.5B."""

GENERATION_TITLE= "<p style='font-size: 24px; color: #292b47;'>💻 Your generated code:</p>"

def instantiate_huggingface_model(
    model_name,
    quantization_config=None,
    device_map="auto",
    use_cache=True,
    trust_remote_code=None,
    pad_token=None,
    padding_side="left"
):
    """
    Instantiate a HuggingFace model with optional quantization using the BitsAndBytes library.

    Parameters:
    - model_name (str): The name of the model to load from HuggingFace's model hub.
    - quantization_config (BitsAndBytesConfig, optional): Configuration for model quantization.
      If None, defaults to a pre-defined quantization configuration for 4-bit quantization.
    - device_map (str, optional): Device placement strategy for model layers ('auto' by default).
    - use_cache (bool, optional): Whether to cache model outputs (False by default).
    - trust_remote_code (bool, optional): Whether to trust remote code for custom layers (True by default).
    - pad_token (str, optional): The pad token to be used by the tokenizer. If None, uses the EOS token.
    - padding_side (str, optional): The side on which to pad the sequences ('left' by default).

    Returns:
    - model (PreTrainedModel): The instantiated model ready for inference or fine-tuning.
    - tokenizer (PreTrainedTokenizer): The tokenizer associated with the model.

    The function will throw an exception if model loading fails.
    """

    # If quantization_config is not provided, use the default configuration
    if quantization_config is None:
        quantization_config = BitsAndBytesConfig(
            load_in_8bit=True,
            low_cpu_mem_usage=True,
        )

    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        quantization_config=quantization_config,
        device_map=device_map,
        use_cache=use_cache,
        trust_remote_code=trust_remote_code
    )

    tokenizer = AutoTokenizer.from_pretrained(model_name, 
                                              trust_remote_code=trust_remote_code)

    if pad_token is not None:
        tokenizer.pad_token = pad_token
    else:
        tokenizer.pad_token = tokenizer.eos_token
    
    tokenizer.padding_side = padding_side

    return model, tokenizer

model, tokenizer = instantiate_huggingface_model("Deci-early-access/DeciCoder-6B", trust_remote_code=True)

pipe = pipeline("text-generation",
                model=model,
                tokenizer=tokenizer,
                device_map="auto",
                max_length=2048,
                temperature=1e-3,
)

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 code_generation(prompt: str) -> str:
    """
    Generates code based on the given prompt. Handles both regular and FIM (Fill-In-Missing) generation.

    Args:
        prompt (str): The input code prompt.

    Returns:
        str: The HTML-styled generated code.
    """    
    completion = pipe(prompt)[0]['generated_text']
    completion = completion[len(prompt):]
    return post_processing(prompt, completion)

demo = gr.Blocks(
    css=".gradio-container {background-color: #FAFBFF; 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.\"\"\"")
            run = gr.Button(value="👨🏽‍💻 Generate code", size='lg')
            output = gr.HTML(label="💻 Your generated code")
            

    event = run.click(code_generation, [code], output)
    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()