import gradio as gr from gradio_client import Client from huggingface_hub import InferenceClient import random from datetime import datetime #from models import models ss_client = Client("https://omnibus-html-image-current-tab.hf.space/") models=[ "bigcode/starcoder2-15b", "bigcode/starcoder2-7b", "bigcode/starcoder2-3b", ] def test_models(): log_box=[] for model in models: start_time = datetime.now() try: generate_kwargs = dict( temperature=0.9, max_new_tokens=128, top_p=0.9, repetition_penalty=1.0, do_sample=True, seed=111111111, ) print(f'trying: {model}\n') client= InferenceClient(model) outp="" stream=client.text_generation("What is a cat", **generate_kwargs, stream=True, details=True, return_full_text=True) for response in stream: outp += response.token.text print (outp) time_delta = datetime.now() - start_time count=time_delta.total_seconds() #if time_delta.total_seconds() >= 180: log = {"Model":model,"Status":"Success","Output":outp, "Time":count} print(f'{log}\n') log_box.append(log) except Exception as e: time_delta = datetime.now() - start_time count=time_delta.total_seconds() log = {"Model":model,"Status":"Error","Output":e,"Time":count} print(f'{log}\n') log_box.append(log) yield log_box def format_prompt_default(message, history,cust_p): prompt = "" if history: #userHow does the brain work?model for user_prompt, bot_response in history: prompt += f"<|startoftext|>{user_prompt}<|endoftext|>" print(prompt) prompt += f"<|startoftext|>{bot_response}<|endoftext|>" print(prompt) prompt += "<|startoftext|>" prompt+=cust_p.replace("USER_INPUT",message) prompt += "<|endoftext|>" prompt += "<|startoftext|>" return prompt def load_models(inp): print(type(inp)) print(inp) print(models[inp]) model_state= InferenceClient(models[inp]) out_box=gr.update(label=models[inp]) prompt_out="<|startoftext|>USER_INPUT<|endoftext|>" return out_box,prompt_out, model_state VERBOSE=False def chat_inf(prompt,model_state,model_name,seed,temp,tokens,top_p,rep_p,chat_mem,cust_p): #token max=8192 #model_n=models[model_name] #print(model_state) hist_len=0 client=model_state #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, ) stream = client.text_generation(prompt, **generate_kwargs, stream=True, details=True, return_full_text=True) output = prompt for response in stream: output += response.token.text yield output #history.append((prompt,output)) #memory.append((prompt,output)) yield output def get_screenshot(chat: list,height=5000,width=600,chatblock=[],theme="light",wait=3000,header=True): print(chatblock) 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]}' print(out) 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 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: model_state=gr.State() memory=gr.State() gr.HTML("""

Huggingface Hub InferenceClient


Chatbot's

""") #chat_b = gr.Chatbot(height=500) chat_b = gr.Textbox(lines=10) 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.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=3,value="userUSER_INPUTmodel") 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") test_btn=gr.Button("Test") client_choice=gr.Dropdown(label="Models",type='index',choices=[c for c in models],value=models[0],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=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=0.99) top_p=gr.Slider(label="Top-P",step=0.01, minimum=0.01, maximum=1.0, value=0.99) rep_p=gr.Slider(label="Repetition Penalty",step=0.01, minimum=0.1, maximum=2.0, value=1.2) chat_mem=gr.Number(label="Chat Memory", info="Number of previous chats to retain",value=4) 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) test_json=gr.JSON(label="Test Output") #test_btn.click(test_models,None,test_json) c_comp=chat_b.input(chat_inf,[chat_b,model_state,client_choice,seed,temp,tokens,top_p,rep_p,chat_mem,custom_prompt],[chat_b]) client_choice.change(load_models,client_choice,[chat_b,custom_prompt,model_state]) app.load(load_models,client_choice,[chat_b,custom_prompt,model_state]) 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,memory,model_state,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,model_state,client_choice,seed,temp,tokens,top_p,rep_p,chat_mem,custom_prompt],[chat_b,memory]) stop_btn.click(None,None,None,cancels=[im_go,c_comp]) clear_btn.click(clear_fn,None,[inp,sys_inp,chat_b,memory]) app.queue(default_concurrency_limit=10).launch()