Update app.py

app.py

@@ -8,60 +8,45 @@ model_id = "hsuwill000/DeepSeek-R1-Distill-Qwen-1.5B-openvino"
 model = OVModelForCausalLM.from_pretrained(model_id, device_map="auto")
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 
-def respond(prompt, history):
-    # 建立初始的系統訊息
+# 建立生成管道
+#pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+def respond(prompt , history):
+    # 將當前訊息與歷史訊息合併
+    #input_text = message if not history else history[-1]["content"] + " " + message
+    #input_text = message+",(450字內回覆)"
+    
     messages = [
-        {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}
+    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
+    {"role": "user", "content": prompt }
     ]
-
-    # 將歷史對話內容整合進 messages（注意這裡轉換成字典）
-    for msg in history:
-        # 假設 history 中每個元素原本是字典格式 (如果不是，請自行轉換)
-        messages.append({"role": "user", "content": msg["user"]})
-        messages.append({"role": "assistant", "content": msg["assistant"]})
-
-    # 加入當前用戶輸入
-    messages.append({"role": "user", "content": prompt})
-
-    # 構造模型輸入並生成回覆
     text = tokenizer.apply_chat_template(
         messages,
         tokenize=False,
         add_generation_prompt=True
     )
-    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)    
     generated_ids = model.generate(
         **model_inputs,
         max_new_tokens=512
-    )
+    )    
+    
+    # 獲取模型的回應
+    #response = pipe(input_text, max_length=512, truncation=True, num_return_sequences=1)
+    #reply = response[0]['generated_text']
     generated_ids = [
         output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
     ]
-    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-
+    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]    
+    
+    # 返回新的消息格式
     print(f"Messages: {messages}")
     print(f"Reply: {response}")
     return response
-    '''''
-    # 更新並返回完整的聊天歷史（改為字典列表）
-    new_history = history.copy()
-    new_history.append({"user": prompt, "assistant": response})
-    # 最後返回的是一個消息列表，每一條消息為字典格式（可進一步轉換為 ChatMessage 格式）
-    final_messages = []
-    # 如果需要顯示完整對話，可將歷史中每一條對話分拆成兩條消息
-    for item in new_history:
-        final_messages.append({"role": "user", "content": item["user"]})
-        final_messages.append({"role": "assistant", "content": item["assistant"]})
-    return final_messages
-    '''''
-   
+    
 # 設定 Gradio 的聊天界面
-demo = gr.ChatInterface(
-    fn=respond,
-    title="DeepSeek-R1-Distill-Qwen-1.5B-openvino Chat",
-    description="Chat with DeepSeek-R1-Distill-Qwen-1.5B-openvino model.",
-    type="messages"
-)
+demo = gr.ChatInterface(fn=respond, title="Qwen2.5-0.5B-Instruct-openvino-4bit", description="Qwen2.5-0.5B-Instruct-openvino-4bit", type='messages')
 
 if __name__ == "__main__":
     demo.launch()
+