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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=[
"NousResearch/OLMo-Bitnet-1B",
"1bitLLM/bitnet_b1_58-3B",
"1bitLLM/bitnet_b1_58-large",
"1bitLLM/bitnet_b1_58-xl",
]
client_z=[]
def load_models(inp,new_models):
if not new_models:
new_models=models
out_box=[gr.Chatbot(),gr.Chatbot(),gr.Chatbot(),gr.Chatbot()]
print(type(inp))
print(inp)
#print(new_models[inp[0]])
client_z.clear()
for z,ea in enumerate(inp):
client_z.append(InferenceClient(new_models[inp[z]]))
out_box[z]=(gr.update(label=new_models[inp[z]]))
return out_box[0],out_box[1],out_box[2],out_box[3]
def format_prompt_default(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"{message}\n"
return prompt
def format_prompt_gemma(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"
return prompt
def format_prompt_mixtral(message, history):
prompt = "<s>"
if history:
for user_prompt, bot_response in history:
prompt += f"[INST] {user_prompt} [/INST]"
prompt += f" {bot_response}</s> "
prompt += f"[INST] {message} [/INST]"
return prompt
def format_prompt_choose(message, history, model_name, new_models=None):
if not new_models:
new_models=models
if "gemma" in new_models[model_name].lower() and "it" in new_models[model_name].lower():
return format_prompt_gemma(message,history)
if "mixtral" in new_models[model_name].lower():
return format_prompt_mixtral(message,history)
else:
return format_prompt_default(message,history)
mega_hist=[[],[],[],[]]
def chat_inf_tree(system_prompt,prompt,history,client_choice,seed,temp,tokens,top_p,rep_p,hid_val):
if len(client_choice)>=hid_val:
client=client_z[int(hid_val)-1]
if history:
mega_hist[hid_val-1]=history
#history = []
hist_len=0
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}", mega_hist[hid_val-1])
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)]
mega_hist[hid_val-1].append((prompt,output))
yield mega_hist[hid_val-1]
else:
yield None
def chat_inf_a(system_prompt,prompt,history,client_choice,seed,temp,tokens,top_p,rep_p,hid_val):
if len(client_choice)>=hid_val:
if system_prompt:
system_prompt=f'{system_prompt}, '
client1=client_z[int(hid_val)-1]
if not history:
history = []
hist_len=0
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_choose(f"{system_prompt}{prompt}", history, client_choice[0])
stream1 = client1.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream1:
output += response.token.text
yield [(prompt,output)]
history.append((prompt,output))
yield history
else:
yield None
def chat_inf_b(system_prompt,prompt,history,client_choice,seed,temp,tokens,top_p,rep_p,hid_val):
if len(client_choice)>=hid_val:
if system_prompt:
system_prompt=f'{system_prompt}, '
client2=client_z[int(hid_val)-1]
if not history:
history = []
hist_len=0
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_choose(f"{system_prompt}{prompt}", history, client_choice[1])
stream2 = client2.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream2:
output += response.token.text
yield [(prompt,output)]
history.append((prompt,output))
yield history
else:
yield None
def chat_inf_c(system_prompt,prompt,history,client_choice,seed,temp,tokens,top_p,rep_p,hid_val):
if len(client_choice)>=hid_val:
if system_prompt:
system_prompt=f'{system_prompt}, '
client3=client_z[int(hid_val)-1]
if not history:
history = []
hist_len=0
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_choose(f"{system_prompt}{prompt}", history, client_choice[2])
stream3 = client3.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream3:
output += response.token.text
yield [(prompt,output)]
history.append((prompt,output))
yield history
else:
yield None
def chat_inf_d(system_prompt,prompt,history,client_choice,seed,temp,tokens,top_p,rep_p,hid_val):
if len(client_choice)>=hid_val:
if system_prompt:
system_prompt=f'{system_prompt}, '
client4=client_z[int(hid_val)-1]
if not history:
history = []
hist_len=0
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_choose(f"{system_prompt}{prompt}", history, client_choice[3])
stream4 = client4.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream4:
output += response.token.text
yield [(prompt,output)]
history.append((prompt,output))
yield history
else:
yield None
def add_new_model(inp, cur):
cur.append(inp)
return cur,gr.update(choices=[z for z in cur])
def load_new(models=models):
return models
def clear_fn():
return None,None,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:
new_models=gr.State([])
gr.HTML("""<center><h1 style='font-size:xx-large;'>Chatbot Model Compare</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)
add_model=gr.Textbox(label="New Model")
add_btn=gr.Button("Add 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=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,visible=False)
hid2=gr.Number(value=2,visible=False)
hid3=gr.Number(value=3,visible=False)
hid4=gr.Number(value=4,visible=False)
app.load(load_new,None,new_models)
add_btn.click(add_new_model,[add_model,new_models],[new_models,client_choice])
client_choice.change(load_models,[client_choice,new_models],[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)
go1=btn.click(check_rand,[rand,seed],seed).then(chat_inf_a,[sys_inp,inp,chat_b,client_choice,seed,temp,tokens,top_p,rep_p,hid1],chat_a)
go2=btn.click(check_rand,[rand,seed],seed).then(chat_inf_b,[sys_inp,inp,chat_b,client_choice,seed,temp,tokens,top_p,rep_p,hid2],chat_b)
go3=btn.click(check_rand,[rand,seed],seed).then(chat_inf_c,[sys_inp,inp,chat_b,client_choice,seed,temp,tokens,top_p,rep_p,hid3],chat_c)
go4=btn.click(check_rand,[rand,seed],seed).then(chat_inf_d,[sys_inp,inp,chat_b,client_choice,seed,temp,tokens,top_p,rep_p,hid4],chat_d)
stop_btn.click(None,None,None,cancels=[go1,go2,go3,go4])
clear_btn.click(clear_fn,None,[inp,sys_inp,chat_a,chat_b,chat_c,chat_d])
app.queue(default_concurrency_limit=10).launch() |