app.py

import gradio as gr
from gradio_client import Client
from huggingface_hub import InferenceClient
import random

ss_client = Client("https://xilixmeaty40-testing.hf.space/")

with open("models.txt", "r") as file:
    models = file.read().splitlines()

clients = [InferenceClient(model) for model in models]

VERBOSE = False

def load_models(inp):
    if VERBOSE:
        print(type(inp))
        print(inp)
        print(models[inp])
    return gr.update(label=models[inp])

def format_prompt(message, history, cust_p):
    prompt = ""
    if history:
        for user_prompt, bot_response in history:
            prompt += f"<start_of_turn>user{user_prompt}<end_of_turn>"
            prompt += f"<start_of_turn>model{bot_response}<end_of_turn>"
            if VERBOSE:
                print(prompt)
    prompt += cust_p.replace("USER_INPUT", message)
    return prompt

def chat_inf(system_prompt, prompt, history, memory, client_choice, seed, temp, tokens, top_p, rep_p, chat_mem, cust_p):
    hist_len = 0
    client = clients[int(client_choice) - 1]
    if not history:
        history = []
    if not memory:
        memory = []
    if memory:
        for ea in memory[0 - chat_mem:]:
            hist_len += len(str(ea))
    in_len = len(system_prompt + prompt) + hist_len

    if (in_len + tokens) > 8000:
        history.append((prompt, "Wait, that's too many tokens, please reduce the 'Chat Memory' value, or reduce the 'Max new tokens' value"))
        yield history, memory
    else:
        generate_kwargs = dict(
            temperature=temp,
            max_new_tokens=tokens,
            top_p=top_p,
            repetition_penalty=rep_p,
            do_sample=True,
            seed=seed,
        )
        if system_prompt:
            formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", memory[0 - chat_mem:], cust_p)
        else:
            formatted_prompt = format_prompt(prompt, memory[0 - chat_mem:], cust_p)
        stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
        output = ""
        for response in stream:
            output += response.token.text
            yield [(prompt, output)], memory
        history.append((prompt, output))
        memory.append((prompt, output))
        yield history, memory

def get_screenshot(chat: list, height=5000, width=600, chatblock=[], theme="light", wait=3000, header=True):
    tog = 0
    if chatblock:
        tog = 3
    result = ss_client.predict(str(chat), height, width, chatblock, header, theme, wait, api_name="/run_script")
    out = f'https://xilixmeaty40-testing.hf.space/file={result[tog]}'
    return out

def clear_fn():
    return None, None, 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:
    memory = gr.State()
    gr.HTML("""<center><h1 style='font-size:xx-large;'>Google Gemma Models</h1><br><h3>running on Huggingface Inference Client</h3><br><h7>EXPERIMENTAL""")
    chat_b = gr.Chatbot(height=500)
    with gr.Group():
        with gr.Row():
            with gr.Column(scale=3):
                inp = gr.Textbox(label="Prompt")
                sys_inp = gr.Textbox(label="System Prompt (optional)")
                with gr.Accordion("Prompt Format", open=False):
                    custom_prompt = gr.Textbox(label="Modify Prompt Format", info="For testing purposes. 'USER_INPUT' is where 'SYSTEM_PROMPT, PROMPT' will be placed", lines=3, value="<start_of_turn>userUSER_INPUT<end_of_turn><start_of_turn>model")
                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")
                client_choice = gr.Dropdown(label="Models", type='index', choices=[c for c in models], value=models[0], interactive=True)
            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=300000, minimum=0, maximum=800000, 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=0.49)
                    top_p = gr.Slider(label="Top-P", step=0.01, minimum=0.01, maximum=1.0, value=0.49)
                    rep_p = gr.Slider(label="Repetition Penalty", step=0.01, minimum=0.1, maximum=2.0, value=0.99)
                    chat_mem = gr.Number(label="Chat Memory", info="Number of previous chats to retain", value=4)
        with gr.Accordion(label="Screenshot", open=False):
            with gr.Row():
                with gr.Column(scale=3):
                    im_btn = gr.Button("Screenshot")
                    img = gr.Image(type='filepath')
                with gr.Column(scale=1):
                    with gr.Row():
                        im_height = gr.Number(label="Height", value=5000)
                        im_width = gr.Number(label="Width", value=500)
                    wait_time = gr.Number(label="Wait Time", value=3000)
                    theme = gr.Radio(label="Theme", choices=["light", "dark"], value="light")
                    chatblock = gr.Dropdown(label="Chatblocks", info="Choose specific blocks of chat", choices=[c for c in range(1, 40)], multiselect=True)

    client_choice.change(load_models, client_choice, [chat_b])
    app.load(load_models, client_choice, [chat_b])

    im_go = im_btn.click(get_screenshot, [chat_b, im_height, im_width, chatblock, theme, wait_time], img)

    chat_sub = inp.submit(check_rand, [rand, seed], seed).then(chat_inf, [sys_inp, inp, chat_b, memory, client_choice, seed, temp, tokens, top_p, rep_p, chat_mem, custom_prompt], [chat_b, memory])
    go = btn.click(check_rand, [rand, seed], seed).then(chat_inf, [sys_inp, inp, chat_b, memory, client_choice, seed, temp, tokens, top_p, rep_p, chat_mem, custom_prompt], [chat_b, memory])

    stop_btn.click(None, None, None, cancels=[go, im_go, chat_sub])
    clear_btn.click(clear_fn, None, [inp, sys_inp, chat_b, memory])
app.queue(default_concurrency_limit=10).launch()