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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):
print(type(inp))
client_z.clear()
if inp[0]:
client_z[0]=InferenceClient(models[inp[0]])
if inp[1]:
client_z[1]=InferenceClient(models[inp[1]])
if inp[2]:
client_z[2]=InferenceClient(models[inp[2]])
if inp[3]:
client_z[3]=InferenceClient(models[inp[3]])
return gr.update(label=models[inp[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()
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