app.py

from huggingface_hub import InferenceClient
import gradio as gr
from pathlib import Path
import datetime

# Initialize the InferenceClient
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")

def format_prompt(message, history, system_prompt):
    prompt = ""
    for user_prompt, bot_response in history:
        prompt += f"[INST] {user_prompt} [/INST]"
        prompt += f" {bot_response} "
    prompt += f"[INST] {message} [/INST]"
    # Add the system prompt only once at the beginning
    prompt = system_prompt + prompt
    return prompt

def generate(prompt, history, user_system_prompt, temperature=0.9, max_new_tokens=9048, top_p=0.95, repetition_penalty=1.0):
    system_prompt = ''' '''    
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2

    top_p = float(top_p)
    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )
    # Get current time
    now = datetime.datetime.now()
    formatted_time = now.strftime("%H.%M.%S, %B, %Y")

    # Load chat history from localStorage
    loaded_history = gr.Context.client.call('loadChat')
    history = loaded_history + history

    formatted_prompt = format_prompt(f"{prompt}", history, system_prompt)

    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

    # Save the updated chat history to localStorage
    new_history = history + [(prompt, output)]
    gr.Context.client.call('saveChat', [new_history])

    return output

additional_inputs = [
    gr.Textbox(label="System Prompt", max_lines=1, interactive=True),
    gr.Slider(label="Temperature", value=0.9, minimum=0.0, maximum=1.0, step=0.05, interactive=True, info="Higher values produce more diverse outputs"),
    gr.Slider(label="Max new tokens", value=9048, minimum=256, maximum=9048, step=64, interactive=True, info="The maximum numbers of new tokens"),
    gr.Slider(label="Top-p (nucleus sampling)", value=0.90, minimum=0.0, maximum=1, step=0.05, interactive=True, info="Higher values sample more low-probability tokens"),
    gr.Slider(label="Repetition penalty", value=1.2, minimum=1.0, maximum=2.0, step=0.05, interactive=True, info="Penalize repeated tokens")
]

avatar_images = ("https://i.postimg.cc/pXjKKVXG/user-circle.png", "https://i.postimg.cc/qq04Yz93/CL3.png")

with gr.Blocks() as demo:
    chatbot = gr.Chatbot(value=gr.Context.client.call('loadChat'))
    gr.ChatInterface(
        fn=generate,
        chatbot=gr.Chatbot(show_label=True, show_share_button=False, show_copy_button=True, likeable=True, layout="panel", height="auto", avatar_images=avatar_images),
        additional_inputs=additional_inputs,
        title="ConvoLite",
        submit_btn="➢",
        retry_btn="Retry",
        undo_btn="↩ Undo",
        clear_btn="Clear (New chat)",
        stop_btn="Stop ▢",
        concurrency_limit=20,
        theme=gr.themes.Soft(primary_hue=gr.themes.colors.cyan),
    ).launch(show_api=False)