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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# 預先定義 Hugging Face 模型
MODEL_NAMES = {
"DeepSeek-V3": "deepseek-ai/DeepSeek-V3",
"DeepSeek-R1": "deepseek-ai/DeepSeek-R1"
}
def load_model(model_name):
"""載入 Hugging Face 模型與 tokenizer"""
model_path = MODEL_NAMES.get(model_name, "deepseek-ai/DeepSeek-V3")
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16).cuda()
return model, tokenizer
# 預設載入 DeepSeek-V3
current_model, current_tokenizer = load_model("DeepSeek-V3")
def chat(message, history, model_name):
"""處理聊天訊息"""
global current_model, current_tokenizer
# 若模型不同則切換
if model_name != current_model:
current_model, current_tokenizer = load_model(model_name)
inputs = current_tokenizer(message, return_tensors="pt").to("cuda")
outputs = current_model.generate(**inputs, max_length=1024)
response = current_tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
with gr.Blocks() as app:
gr.Markdown("## Chatbot with DeepSeek Models")
with gr.Row():
chat_interface = gr.ChatInterface(chat, streaming=True, save_history=True)
model_selector = gr.Dropdown(
choices=list(MODEL_NAMES.keys()),
value="DeepSeek-V3",
label="Select Model"
)
chat_interface.append(model_selector)
app.launch()
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