Update app.py

app.py

@@ -1,30 +1,43 @@
 import gradio as gr
 from transformers import AutoTokenizer
 from optimum.intel import OVModelForCausalLM
-from fastapi import FastAPI
-import uvicorn
-from pydantic import BaseModel
 import warnings
-warnings.filterwarnings("ignore", category=DeprecationWarning, message="__array__ implementation doesn't accept a copy keyword")
 
+warnings.filterwarnings("ignore", category=DeprecationWarning, message="__array__ implementation doesn't accept a copy keyword")
 
-# 模型與標記器載入（你的原始代碼）
+# 模型與標記器載入
 model_id = "hsuwill000/DeepSeek-R1-Distill-Qwen-1.5B-openvino"
 print("Loading model...")
 model = OVModelForCausalLM.from_pretrained(model_id, device_map="auto")
 print("Loading tokenizer...")
 tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
 
-def respond(prompt, history):
-    messages = [
-        {"role": "system", "content": "Answer the question in English only."},
-        {"role": "user", "content": prompt}
-    ]
+# 對話歷史記錄
+history = []
+
+# 回應函數
+def respond(prompt):
+    global history  # 使用全域變數存 history
+    
+    # 轉換 history 為 messages 格式
+    messages = [{"role": "system", "content": "Answer the question in English only."}]
+    
+    # 加入歷史對話
+    for user_text, assistant_text in history:
+        messages.append({"role": "user", "content": user_text})
+        messages.append({"role": "assistant", "content": assistant_text})
+
+    # 加入當前輸入
+    messages.append({"role": "user", "content": prompt})
+    
+    # 轉換為 tokenizer 需要的格式
     text = tokenizer.apply_chat_template(
         messages,
         tokenize=False,
         add_generation_prompt=True
     )
+
+    # 進行模型推理
     model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
     generated_ids = model.generate(
         **model_inputs,
@@ -38,10 +51,22 @@ def respond(prompt, history):
     ]
     response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
     response = response.replace("<think>", "**THINK**").replace("</think>", "**THINK**").strip()
+
+    # 更新 history
+    history.append((prompt, response))
+
     return response
 
+# 清除歷史記錄
+def clear_history():
+    global history
+    history = []
+    return "History cleared!"
+
+# Gradio 介面
 with gr.Blocks() as demo:
     gr.Markdown("# DeepSeek-R1-Distill-Qwen-1.5B-openvino")
+
     with gr.Tabs():
         with gr.TabItem("聊天"):
             chat_if = gr.Interface(
@@ -49,24 +74,16 @@ with gr.Blocks() as demo:
                 inputs=gr.Textbox(label="Prompt", placeholder="請輸入訊息..."),
                 outputs=gr.Textbox(label="Response", interactive=False),
                 api_name="hchat",
-                title="DeepSeek-R1-Distill-Qwen-1.5B-openvino",
+                title="DeepSeek-R1-Distill-Qwen-1.5B-openvino(with history)",
                 description="回傳輸入內容的測試 API",
             )
-            
 
-app = FastAPI()
-class Prompt(BaseModel):
-    prompt: str
+    with gr.Row():
+        clear_button = gr.Button("🧹 Clear History")
     
-def maxtest(prompt: str) -> str:
-    # 在此处实现您的逻辑
-    return f"您输入的内容是: {prompt}"
-
-@app.post("/maxtest")
-async def call_maxtest(prompt: Prompt):
-    response = maxtest(prompt.prompt)
-    return {"response": response}
+    # 點擊按鈕清除 history
+    clear_button.click(fn=clear_history, inputs=[], outputs=[])
 
 if __name__ == "__main__":
     print("Launching Gradio app...")
-    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)