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 = '''

CodeGemma

This Space demonstrates model CodeGemma-7b-it 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!

🔎 For more details about the CodeGemma release and how to use the models with transformers, take a look at our blog post.

''' PLACEHOLDER = """

CodeGemma-7B-IT Chatbot
""" # 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()