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

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

#models=[
    "google/gemma-7b",
    "google/gemma-7b-it",
    "google/gemma-2b",
    "google/gemma-2b-it"
]#
clients=[
InferenceClient(models[0]),
InferenceClient(models[1]),
InferenceClient(models[2]),
InferenceClient(models[3]),
]

VERBOSE=False

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

def format_prompt(message, history):
    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==True:
                print(prompt)
    prompt += message
    return prompt

def chat_inf(system_prompt,prompt,history,memory,client_choice,seed,temp,tokens,top_p,rep_p,chat_mem):
    hist_len=0
    client=clients[int(client_choice)-1]
    if not history:
        history = []
        hist_len=0
    if not memory:
        memory = []
        mem_len=0        
    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,
        )
        formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", memory[0-chat_mem:])
        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
        
    if VERBOSE==True:
        print("\n######### HIST "+str(in_len))
        print("\n######### TOKENS "+str(tokens))        

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://omnibus-html-image-current-tab.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==True:
        return random.randint(1,1111111111111111)
    else:
        return 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")
                btn = gr.Button("Chat")
            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=1600,minimum=0,maximum=8000,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)

    client_choice=gr.Dropdown(label="Models",type='index',choices=[c for c in models],value=models[0],interactive=True)
    client_choice.change(load_models,client_choice,[chat_b])
    app.load(load_models,client_choice,[chat_b])
    
    chat_sub=inp.submit(check_rand,[rand,seed],seed).then(chat_inf,[inp,inp,chat_b,memory,client_choice,seed,temp,tokens,top_p,rep_p,chat_mem],[chat_b,memory])
    go=btn.click(check_rand,[rand,seed],seed).then(chat_inf,[inp,inp,chat_b,memory,client_choice,seed,temp,tokens,top_p,rep_p,chat_mem],[chat_b,memory])

    app.queue(default_concurrency_limit=10).launch()