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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=[gr.Chatbot(),gr.Chatbot(),gr.Chatbot(),gr.Chatbot()] | |
print(type(inp)) | |
print(inp) | |
print(models[inp[0]]) | |
client_z.clear() | |
for z,ea in enumerate(inp): | |
client_z.append(InferenceClient(models[inp[z]])) | |
out_box[z]=(gr.update(label=models[inp[z]])) | |
return out_box[0],out_box[1],out_box[2],out_box[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"{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,hid_val): | |
if len(client_choice)>=hid_val: | |
#token max=8192 | |
client=client_z[int(hid_val)-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#########"+str(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 | |
else: | |
yield None | |
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],max_choices=4,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) | |
hid1=gr.Number(value=1) | |
hid2=gr.Number(value=2) | |
hid3=gr.Number(value=3) | |
hid4=gr.Number(value=4) | |
client_choice.change(load_models,client_choice,[chat_a,chat_b,chat_c,chat_d]) | |
#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,hid1],chat_a) | |
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,hid2],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,hid3],chat_c) | |
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,hid4],chat_d) | |
#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() | |