app.py

import gradio as gr
from huggingface_hub import InferenceClient
import random
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]),
]
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"<start_of_turn>user{user_prompt}<end_of_turn>"
            prompt += f"<start_of_turn>model{bot_response}"
    prompt += f"<start_of_turn>user{message}<end_of_turn><start_of_turn>model"
    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)
        
    #seed = random.randint(1,1111111111111111)
    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.Group():
        chat_b = gr.Chatbot()
        with gr.Row():
            with gr.Column(scale=3):
                inp = gr.Textbox(label="Prompt")
                sys_inp = gr.Textbox(label="System Prompt (optional)")
                btn = gr.Button("Chat")
                
            with gr.Column(scale=1):
                with gr.Group():
                    rand = gr.Checkbox(label="Random", value=True)
                    seed=gr.Slider(label="Seed", minimum=1, maximum=1111111111111111,step=1, value=rand_val)
                    tokens = gr.Slider(label="Max new tokens",value=6400,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.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)
    
    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)
    clear_btn.click(clear_fn,None,[inp,sys_inp,chat_b])
app.queue(default_concurrency_limit=10).launch()