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Zero
| 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") | |
| 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() |