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"
    "meta-llama/Llama-2-7b-chat-hf",
    "codellama/CodeLlama-70b-Instruct-hf",
    "openchat/openchat-3.5-0106",
    "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
    "mistralai/Mixtral-8x7B-Instruct-v0.1",
    "mistralai/Mixtral-8x7B-Instruct-v0.2"
]
'''clients=[
InferenceClient(models[0]),
InferenceClient(models[1]),
InferenceClient(models[2]),
InferenceClient(models[3]),
]'''

client_z=[]


def load_models(inp):
    
    out_box=[]
    print(type(inp))
    print(inp)
    print(models[inp[0]])
    client_z.clear()
    for z,ea in inp:
        client_z.append(InferenceClient(models[inp[z]]))
        out_box.append(gr.update(label=models[inp[z]]))
    return out_box[0]


def format_prompt(message, history):
    prompt = ""
    if history:
        #<start_of_turn>userHow does the brain work?<end_of_turn><start_of_turn>model
        for user_prompt, bot_response in history:
            prompt += f"{user_prompt}\n"
            print(prompt)
            prompt += f"{bot_response}\n"
            print(prompt)
    prompt += f"<start_of_turn>user{message}<end_of_turn><start_of_turn>model"
    print(prompt)
    return prompt

def chat_inf(system_prompt,prompt,history,client_choice,seed,temp,tokens,top_p,rep_p):
    #token max=8192
    client=clients[int(client_choice)-1]
    if not history:
        history = []
        hist_len=0
    if history:
        hist_len=len(history)
        print(hist_len)
    in_len=len(system_prompt+prompt)+hist_len
    print("\n#########"+in_len)
    if (in_len+tokens) > 8000:
        yield [(prompt,"Wait. I need to compress our Chat history...")]
        history=compress_history(history,client_choice,seed,temp,tokens,top_p,rep_p)
        yield [(prompt,"History has been compressed, processing request...")]
            
    generate_kwargs = dict(
        temperature=temp,
        max_new_tokens=tokens,
        top_p=top_p,
        repetition_penalty=rep_p,
        do_sample=True,
        seed=seed,
    )
    #formatted_prompt=prompt   
    formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)



    
    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 [(prompt,output)]
    history.append((prompt,output))
    yield history

def clear_fn():
    return None,None,None
rand_val=random.randint(1,1111111111111111)
def check_rand(inp,val):
    if inp==True:
        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:
    gr.HTML("""<center><h1 style='font-size:xx-large;'>Google Gemma Models</h1><br><h3>running on Huggingface Inference Client</h3><br><h7>EXPERIMENTAL""")
    with gr.Row():
        chat_a = gr.Chatbot(height=500)
        chat_b = gr.Chatbot(height=500)
    with gr.Row():
        chat_c = gr.Chatbot(height=500)
        chat_d = 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.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],multiselect=True,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=3840,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=0.9)
                    top_p=gr.Slider(label="Top-P",step=0.01, minimum=0.01, maximum=1.0, value=0.9)
                    rep_p=gr.Slider(label="Repetition Penalty",step=0.1, minimum=0.1, maximum=2.0, value=1.0)
        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_a)

    #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,client_choice,seed,temp,tokens,top_p,rep_p],chat_b)
    #go=btn.click(check_rand,[rand,seed],seed).then(chat_inf,[sys_inp,inp,chat_b,client_choice,seed,temp,tokens,top_p,rep_p],chat_b)
    #stop_btn.click(None,None,None,cancels=[go,im_go,chat_sub])
    #clear_btn.click(clear_fn,None,[inp,sys_inp,chat_b])
app.queue(default_concurrency_limit=10).launch()