from huggingface_hub import InferenceClient, create_branch
import gradio as gr
import time

client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
#client = InferenceClient("meta-llama/Llama-2-70b-chat-hf")

def split_list(lst, chunk_size):
    return [lst[i:i + chunk_size] for i in range(0, len(lst), chunk_size)]

def format_prompt(message, history, system_prompt):
  prompt = f"<s>[INST] <<SYS>>{system_prompt}<</SYS>> [/INST] </s>" if system_prompt else "<s>"
  for user_prompt, bot_response in history:
    prompt += f"[INST] {user_prompt} [/INST]"
    prompt += f" {bot_response}</s> "
  prompt += f"[INST] {message} [/INST]"
  return prompt

def format_sys_prompt(username, botname, botpersonality, botexamplechat, chatsituation):
    return f"You are a fictional character named \"{botname}\"\nHowever, don\'t say we\'re roleplaying, as to not break the immersion.\n\nPersonality of {botname}:\n{botpersonality}\n\nCurrent situation/place/time of this conversation:\n{chatsituation}\n\nIn this case, you will be talking to \"{username}\". You should not help to talk or act as \"{username}\". Leave everything by the user.\n\nChat formatting:\nChat: <\"Hello, this is a chat text. Chat text is enclosed with < as the starting point and > as the ending point.\">\nExample:\n{botexamplechat}"

def generate(
    prompt, history, username, botname, botpersonality, botexamplechat, chatsituation, shouldoverridehistory, historyoverride, max_new_tokens=1024, temperature=1.2, top_p=0.95, repetition_penalty=1.0,
):
    print(history)
    print(historyoverride)
    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=round(time.time()),
    )

    if shouldoverridehistory:
        history = split_list(historyoverride[0], 2)

    print(history)
    
    formatted_prompt = format_prompt(prompt, history, format_sys_prompt(username, botname, botpersonality, botexamplechat, chatsituation))

    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
    return output

    
mychatbot = gr.Chatbot(
    avatar_images=["./user.png", "./user.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,)

demo = gr.ChatInterface(fn=generate, 
                        chatbot=mychatbot,
                        title="Joystick's Mixtral Chat-optimized interface",
                        retry_btn="🔁 Regenerate",
                        undo_btn="↩️ Undo",
                        additional_inputs=[
                            gr.Textbox(label="Name of user", lines=1, value="Jake"),
                            gr.Textbox(label="Name of bot", lines=1, value="Janet"),
                            gr.Textbox(label="Personality of bot", lines=3, value="Janet's a lovely person. A woman, blue eyed, glasses, smart and looks stunning."),
                            gr.Textbox(label="Example of bot chat", lines=3, value='<"Oh hey Jake!"> She said to Jake as he hurries to him. <"How are you?">'),
                            gr.Textbox(label="Current conversation situation", lines=2, value="It was a Friday afternoon, after-school hours, it was outside of school. Jake and Janet met each other at the entrance of the school."),
                            gr.Checkbox(label="Override history: History should be in the following format: user-bot-user-bot-user-...\nOverride history should be checked in order for it to be effective. Override primarily only used for APIs.", value=False),
                            gr.List(label="History", value=None, row_count=(1, "fixed"), headers=None),
                            gr.Slider(label="Max new tokens", maximum=2048, value=512)
                        ]
                       )

demo.queue().launch(show_api=True)