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import gradio as gr | |
from huggingface_hub import InferenceClient | |
def format_chat_prompt(message, chat_history,instruction): | |
prompt = f"System:{instruction}" | |
for turn in chat_history: | |
user_message, bot_message = turn | |
prompt = f"{prompt}\nUser: {user_message}\nAssistant:{bot_message}" | |
prompt= f"{prompt}\nUser: {message}\nAssistant:" | |
return prompt | |
def respond(message, chat_history,instruction,model,temperature=0.7): | |
if model == "Llama2-Chat": | |
model = "meta-llama/Llama-2-7b-chat-hf" | |
else: | |
model="mistralai/Mistral-7B-Instruct-v0.1" | |
client = InferenceClient(model=f"{model}",timeout=30) | |
formatted_prompt = format_chat_prompt(message,chat_history,instruction) | |
bot_message = client.text_generation(formatted_prompt, max_new_tokens=1024, | |
stop_sequences=["\nUser:", "<|endoftext|>"], | |
temperature=temperature) | |
chat_history.append((message,bot_message)) | |
return "",chat_history | |
with gr.Blocks() as demo: | |
chatbot = gr.Chatbot(height=240) #visual param | |
msg = gr.Textbox(label='prompt') | |
with gr.Accordion(label="Advanced options",open=False): | |
model = gr.Radio(["MistralAI"]) | |
system = gr.Textbox(label="System message", lines=2, value="A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.") | |
temperature = gr.Slider(label="temperature", minimum=0.1, maximum=1, value=0.7, step=0.1) | |
btn = gr.Button("Submit") | |
clear = gr.ClearButton(components=[msg,chatbot], value="Clear console") | |
btn.click(respond, inputs=[msg,chatbot,system,model], outputs=[msg, chatbot]) | |
msg.submit(respond, inputs=[msg, chatbot,system,model], outputs=[msg, chatbot]) | |
demo.queue().launch() |