app.py

import gradio as gr
import os
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread


# Set an environment variable
HF_TOKEN = os.environ.get("HF_TOKEN", None)

DESCRIPTION = '''
<div>
<h1 style="text-align: center;">CodeGemma</h1>

<p>This Space demonstrates model <a href="https://huggingface.co/google/codegemma-7b-it">CodeGemma-7b-it</a> by Google. CodeGemma is a collection of lightweight open code models built on top of Gemma. Feel free to play with it, or duplicate to run privately!</p>

<p>🔎 For more details about the CodeGemma release and how to use the models with <code>transformers</code>, take a look <a href="https://huggingface.co/blog/codegemma">at our blog post</a>.</p>
</div>
'''

PLACEHOLDER = """
<div style="opacity: 0.65;">
    <img src="https://ysharma-dummy-chat-app.hf.space/file=/tmp/gradio/7dd7659cff2eab51f0f5336f378edfca01dd16fa/gemma_lockup_vertical_full-color_rgb.png" style="width:30%;">
    <br><b>CodeGemma-7B-IT Chatbot</b>
</div>
"""

    
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("google/codegemma-7b-it")
model = AutoModelForCausalLM.from_pretrained("google/codegemma-7b-it", device_map="auto")


@spaces.GPU(duration=120)
def codegemma(message: str, 
              history: list, 
              temperature: float, 
              max_new_tokens: int
             ) -> str:
    """
    Generate a streaming response using the CodeGemma model.
    Args:
        message (str): The input message.
        history (list): The conversation history used by ChatInterface.
        temperature (float): The temperature for generating the response.
        max_new_tokens (int): The maximum number of new tokens to generate.
    Returns:
        str: The generated response.
    """
    conversation = []
    for user, assistant in history:
        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
    conversation.append({"role": "user", "content": message})

    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device)
    
    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)

    generate_kwargs = dict(
        input_ids= input_ids,
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        temperature=temperature,
    )
    # This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash.             
    if temperature == 0:
        generate_kwargs['do_sample'] = False
        
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    outputs = []
    for text in streamer:
        outputs.append(text)
        yield "".join(outputs)
        

# Gradio block
chatbot=gr.Chatbot(placeholder=PLACEHOLDER,height=500)

with gr.Blocks(fill_height=True) as demo:
    
    gr.HTML(DESCRIPTION)
    
    gr.ChatInterface(
        fn=codegemma,
        chatbot=chatbot,
        fill_height=True,
        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
        additional_inputs=[
            gr.Slider(minimum=0,
                      maximum=1, 
                      step=0.1,
                      value=0.95, 
                      label="Temperature", 
                      render=False),
            gr.Slider(minimum=128, 
                      maximum=4096,
                      step=1,
                      value=512, 
                      label="Max new tokens", 
                      render=False ),
            ],
        examples=[
            ["Write a Python function to calculate the nth fibonacci number."]
            ],
        cache_examples=False,
                     )
    
if __name__ == "__main__":
    demo.launch()