Update app.py

app.py

@@ -1,33 +1,10 @@
+from transformers import pipeline
 import gradio as gr
-from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 
-model_id = "MediaTek-Research/Breeze-7B-Instruct-v1_0"  # Breeze7 模型名稱（請確認 Huggingface 上正確名稱）
+generator = pipeline("text-generation", model="distilgpt2")
 
-# 載入 tokenizer 和模型
-tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
+def chat_fn(message, history):
+    output = generator(message, max_new_tokens=50)[0]["generated_text"]
+    return output, history
 
-generator = pipeline("text-generation", model=model, tokenizer=tokenizer, trust_remote_code=True)
-
-def chat_fn(user_input, chat_history):
-    chat_history = chat_history or []
-    chat_history.append(f"你: {user_input}")
-
-    # 取最近幾句聊天作為 prompt，避免太長
-    prompt = "\n".join(chat_history) + "\nAI:"
-
-    response = generator(prompt, max_new_tokens=100, temperature=0.7)
-    reply = response[0]['generated_text'][len(prompt):].strip()
-
-    chat_history.append(f"AI: {reply}")
-    return reply, chat_history
-
-with gr.Blocks() as demo:
-    chat_history = gr.State([])
-
-    chatbot = gr.Chatbot()
-    user_input = gr.Textbox(show_label=False, placeholder="說點什麼...")
-
-    user_input.submit(chat_fn, inputs=[user_input, chat_history], outputs=[chatbot, chat_history])
-
-demo.launch()
+gr.Interface(fn=chat_fn, inputs="text", outputs="text").launch()