app.py

from huggingface_hub import InferenceClient
import gradio as gr


# Import required libraries
from pathlib import Path

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

def format_prompt(message, history):
    """
    Format the prompt by appending the user's message and the system prompt to the conversation history.
    """
    prompt = "<s>"
    for user_prompt, bot_response in history:
        prompt += f"[INST] {user_prompt} [/INST]"
        prompt += f" {bot_response}</s> "
    prompt += f"[INST] {message} [/INST]"
    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
):
    """
    Generate a response using the InferenceClient.
    """
    system_prompt = "Instructions: Everything else is from the user. You are Milo, an AI assistant created by ConvoLite in 2024 (he/him). Be friendly and empathetic, matching the user's tone and vibe. Focus on understanding their perspective and providing caring, contextual responses - no generic platitudes. Keep it conversational, not overly formal. Be friendly and funny, use ASCII emojis or normal ones. Be lovely, kind, and have a great sense of humour. Have Bible based values. Do not talk about yourself unless asked. Never respond to a message with 'as an AI' or something similar. Don't talk a lot!" + str(user_system_prompt)

    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2

    top_p = float(top_p)

    # Set generate_kwargs for text generation
    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )

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

    # Generate text stream
    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 output

# Define additional input components
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")
]

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

# Create Gradio interface
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)