add appfile

app.py

@@ -0,0 +1,47 @@
+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()