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import gradio as gr
from gradio_client import Client
from huggingface_hub import InferenceClient
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"
]
clients=[
InferenceClient(models[0]),
InferenceClient(models[1]),
InferenceClient(models[2]),
InferenceClient(models[3]),
]
VERBOSE=False
def load_models(inp):
if VERBOSE==True:
print(type(inp))
print(inp)
print(models[inp])
return gr.update(label=models[inp])
def format_prompt(message, history):
prompt = ""
if history:
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}<end_of_turn>"
if VERBOSE==True:
print(prompt)
prompt += message
return prompt
def chat_inf(prompt,history,memory,client_choice,temp,tokens,top_p,rep_p,chat_mem):
hist_len=0
client=clients[int(client_choice)-1]
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(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,
)
formatted_prompt = format_prompt(prompt, memory[0-chat_mem:])
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
output = ""
for response in stream:
output += response.token.text
yield [(prompt,output)],memory
history.append((prompt,output))
memory.append((prompt,output))
yield history,memory
if VERBOSE==True:
print("\n######### HIST "+str(in_len))
print("\n######### TOKENS "+str(tokens))
def get_screenshot(chat: list,height=5000,width=600,chatblock=[],theme="light",wait=3000,header=True):
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]}'
return out
def clear_fn():
return None,None,None,None
with gr.Blocks() as app:
memory=gr.State()
chat_b = gr.Chatbot(height=500)
with gr.Group():
with gr.Row():
with gr.Column(scale=3):
inp = gr.Textbox(label="Prompt")
btn = gr.Button("Chat")
with gr.Column(scale=1):
with gr.Group():
temp=gr.Slider(label="Temperature",step=0.01, minimum=0.01, maximum=1.0, value=0.49)
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")
top_p=gr.Slider(label="Top-P",step=0.01, minimum=0.01, maximum=1.0, value=0.49)
rep_p=gr.Slider(label="Repetition Penalty",step=0.01, minimum=0.1, maximum=2.0, value=0.99)
chat_mem=gr.Number(label="Chat Memory", info="Number of previous chats to retain",value=4)
client_choice=gr.Dropdown(label="Models",type='index',choices=[c for c in models],value=models[0],interactive=True)
client_choice.change(load_models,client_choice,[chat_b])
app.load(load_models,client_choice,[chat_b])
chat_sub=inp.submit().then(chat_inf,[inp,chat_b,memory,client_choice,temp,tokens,top_p,rep_p,chat_mem],[chat_b,memory])
go=btn.click().then(chat_inf,[inp,chat_b,memory,client_choice,temp,tokens,top_p,rep_p,chat_mem],[chat_b,memory])
app.queue(default_concurrency_limit=10).launch()
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