Update app.py

app.py

@@ -3,29 +3,28 @@ from huggingface_hub import InferenceClient
 from optimum.intel import OVModelForCausalLM
 from transformers import AutoTokenizer, pipeline
 
-# 載入模型和標記器
+# Load the model and tokenizer
 model_id = "hsuwill000/Qwen2.5-1.5B-Instruct-openvino-8bit"
 model = OVModelForCausalLM.from_pretrained(model_id, device_map="auto")
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 
-# 建立生成管道
+# Create generation pipeline
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
 def respond(message, 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)"
-    # 獲取模型的回應
+    # Combine current message with previous history
+    input_text = message if not history else history[-1]["value"] + " " + message
+    # Get model's response
     response = pipe(input_text, max_length=512, truncation=True, num_return_sequences=1)
     reply = response[0]['generated_text']
     
-    # 返回新的消息格式
+    # Return new message format
     print(f"Message: {message}")
     print(f"Reply: {reply}")
-    return reply
+    return [{"role": "bot", "value": reply}]
     
-# 設定 Gradio 的聊天界面
-demo = gr.ChatInterface(fn=respond, title="Qwen2.5-3B-Instruct-openvino", description="Qwen2.5-3B-Instruct-openvino", type='messages')
+# Set up Gradio chat interface
+demo = gr.ChatInterface(fn=respond, title="Qwen2.5-3B-Instruct-openvino", description="Qwen2.5-3B-Instruct-openvino", type='chatbot')
 
 if __name__ == "__main__":
     demo.launch()