import gradio as gr from huggingface_hub import InferenceClient import random import textwrap # Define the model to be used model = "mistralai/Mixtral-8x7B-Instruct-v0.1" client = InferenceClient(model) # Embedded system prompt system_prompt_text = "You are a smart and helpful co-worker of Law on Earth. You help with any kind of request and provide a detailed answer to the question. But if you are asked about something unethical or dangerous, you must refuse and instead provide a safe and respectful way to handle it." # Read the content of the info.md file with open("info.md", "r") as file: info_md_content = file.read() # Chunk the info.md content into smaller sections chunk_size = 2500 # Adjust this size as needed info_md_chunks = textwrap.wrap(info_md_content, chunk_size) def get_all_chunks(chunks): return "\n\n".join(chunks) def format_prompt_mixtral(message, history, info_md_chunks): prompt = "" all_chunks = get_all_chunks(info_md_chunks) prompt += f"{all_chunks}\n\n" # Add all chunks of info.md at the beginning prompt += f"{system_prompt_text}\n\n" # Add the system prompt if history: for user_prompt, bot_response in history: prompt += f"[INST] {user_prompt} [/INST]" prompt += f" {bot_response} " prompt += f"[INST] {message} [/INST]" return prompt def chat_inf(prompt, history, seed, temp, tokens, top_p, rep_p): generate_kwargs = dict( temperature=temp, max_new_tokens=tokens, top_p=top_p, repetition_penalty=rep_p, do_sample=True, seed=seed, ) formatted_prompt = format_prompt_mixtral(prompt, history, info_md_chunks) stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) output = "" for response in stream: output += response.token.text yield [(prompt, output)] history.append((prompt, output)) yield history def clear_fn(): return None, None rand_val = random.randint(1, 1111111111111111) def check_rand(inp, val): if inp: return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=random.randint(1, 1111111111111111)) else: return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=int(val)) with gr.Blocks() as app: # Add auth here gr.HTML("""

Law on Earth Chatbot


running on Huggingface Inference


EXPERIMENTAL
""") with gr.Row(): chat = gr.Chatbot(height=500) with gr.Group(): with gr.Row(): with gr.Column(scale=3): inp = gr.Textbox(label="Prompt", lines=5, interactive=True) # Increased lines and interactive with gr.Row(): with gr.Column(scale=2): btn = gr.Button("Chat") with gr.Column(scale=1): with gr.Group(): stop_btn = gr.Button("Stop") clear_btn = gr.Button("Clear") with gr.Column(scale=1): with gr.Group(): rand = gr.Checkbox(label="Random Seed", value=True) seed = gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, step=1, value=rand_val) tokens = gr.Slider(label="Max new tokens", value=3840, minimum=0, maximum=8000, step=64, interactive=True, visible=True, info="The maximum number of tokens") temp = gr.Slider(label="Temperature", step=0.01, minimum=0.01, maximum=1.0, value=0.9) top_p = gr.Slider(label="Top-P", step=0.01, minimum=0.01, maximum=1.0, value=0.9) rep_p = gr.Slider(label="Repetition Penalty", step=0.1, minimum=0.1, maximum=2.0, value=1.0) hid1 = gr.Number(value=1, visible=False) go = btn.click(check_rand, [rand, seed], seed).then(chat_inf, [inp, chat, seed, temp, tokens, top_p, rep_p], chat) stop_btn.click(None, None, None, cancels=[go]) clear_btn.click(clear_fn, None, [inp, chat]) app.queue(default_concurrency_limit=10).launch(share=True, auth=("admin", "0112358"))