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

ss_client = Client("https://xilixmeaty40-html-image-current-tabx.hf.space/")

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

combined_model = "\n\n".join(models)

try:
    client = InferenceClient(combined_model)
except Exception as e:
    raise Exception(f"Failed to load models: {e}")

def load_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>"
    prompt += cust_p.replace("USER_INPUT", message)
    return prompt

def chat_inf(system_prompt, prompt, history, memory, seed, temp, tokens, top_p, rep_p, chat_mem, cust_p):
    hist_len = 0
    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)
        try:
            stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True, timeout=10)
            output = ""
            for response in stream:
                output += response.token.text
                yield [(prompt, output)], memory
            history.append((prompt, output))
            memory.append((prompt, output))
            yield history, memory
        except Exception as e:
            print(f"Error during model inference: {e}")
            yield [("Error", "The model failed to respond, possibly due to a timeout. Please try again.")], memory

def get_screenshot(chat, 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-html-image-current-tabx.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()
    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)")
                custom_prompt = gr.Textbox(label="Modify Prompt Format", 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):
                        stop_btn = gr.Button("Stop")
                        clear_btn = gr.Button("Clear")
                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)
                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", value=4)
        with gr.Accordion(label="Screenshot", open=False):
            im_btn = gr.Button("Screenshot")
            img = gr.Image(type='filepath')
            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", choices=list(range(0, 21)), value=0, type="index")
            header = gr.Checkbox(label="Include header?", value=True)
    check_rand(rand_val, rand_val)
    btn.click(chat_inf, inputs=[sys_inp, inp, chat_b, memory, seed, temp, tokens, top_p, rep_p, chat_mem, custom_prompt], outputs=[chat_b, memory])
    stop_btn.click(lambda: None, [])
    clear_btn.click(clear_fn, [])

app.launch(share=True)