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import gradio as gr
from huggingface_hub import InferenceClient
import os
MODELS = {
"Zephyr 7B Beta": "HuggingFaceH4/zephyr-7b-beta",
"DeepSeek Coder V2": "deepseek-ai/DeepSeek-Coder-V2-Instruct",
"Meta Llama 3.1 8B": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"Mixtral 8x7B": "mistralai/Mixtral-8x7B-Instruct-v0.1",
"Cohere Command R+": "CohereForAI/c4ai-command-r-plus",
}
def get_client(model_name):
model_id = MODELS[model_name]
hf_token = os.getenv("HF_TOKEN")
if not hf_token:
raise ValueError("HF_TOKEN environment variable is required")
return InferenceClient(model_id, token=hf_token)
def respond(
message,
chat_history,
model_name,
max_tokens,
temperature,
top_p,
system_message,
):
try:
client = get_client(model_name)
except ValueError as e:
chat_history.append((message, str(e)))
return chat_history
messages = [{"role": "system", "content": system_message}]
for human, assistant in chat_history:
messages.append({"role": "user", "content": human})
messages.append({"role": "assistant", "content": assistant})
messages.append({"role": "user", "content": message})
try:
stream = client.chat_completion(
messages,
max_tokens=max_tokens, # 'max_new_tokens'에서 'max_tokens'로 변경
temperature=temperature,
top_p=top_p,
stream=True,
)
partial_message = ""
for response in stream:
if response.choices[0].delta.content is not None:
partial_message += response.choices[0].delta.content
chat_history = chat_history[:-1] + [(message, partial_message)]
yield chat_history
def clear_conversation():
return []
with gr.Blocks() as demo:
gr.Markdown("# Advanced AI Chatbot")
gr.Markdown("Chat with different language models and customize your experience!")
with gr.Row():
with gr.Column(scale=1):
model_name = gr.Radio(
choices=list(MODELS.keys()),
label="Language Model",
value="Zephyr 7B Beta"
)
max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max New Tokens")
temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature")
top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
system_message = gr.Textbox(
value="You are a friendly and helpful AI assistant.",
label="System Message",
lines=3
)
with gr.Column(scale=2):
chatbot = gr.Chatbot()
msg = gr.Textbox(label="Your message")
with gr.Row():
submit_button = gr.Button("Submit")
clear_button = gr.Button("Clear")
msg.submit(respond, [msg, chatbot, model_name, max_tokens, temperature, top_p, system_message], chatbot)
submit_button.click(respond, [msg, chatbot, model_name, max_tokens, temperature, top_p, system_message], chatbot)
clear_button.click(clear_conversation, outputs=chatbot, queue=False)
if __name__ == "__main__":
demo.launch() |