app.py

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

models=[
    "google/gemma-7b"
]
clients=[
InferenceClient(models[0])
]

VERBOSE=False

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

def format_prompt(message, history, cust_p):
    prompt = "<s>"
    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></s>"
            if VERBOSE==True:
                print(prompt)
    #prompt += f"<start_of_turn>user\n{message}<end_of_turn>\n<start_of_turn>model\n"
    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):
    #token max=8192
    print(client_choice)
    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,
        )
        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)
        
        
        chat = [
    { "role": "user", "content": f"{formatted_prompt}" },
    ]

        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 clear_fn():
    return None,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:
    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.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.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=5,value="<start_of_turn>userUSER_INPUT<end_of_turn><start_of_turn>model")
            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=0.9)
                    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)
                    chat_mem=gr.Number(label="Chat Memory", info="Number of previous chats to retain",value=4)

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