新增聊天機器人 app 與依賴

app.py

@@ -1,20 +1,33 @@
-from transformers import pipeline
 import gradio as gr
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 
-generator = pipeline(
-    "text-generation",
-    model="Qwen/Qwen1.5-0.5B-Chat",
-    trust_remote_code=True
-)
+model_id = "MediaTek-Research/Breeze-7B-Instruct-v1_0"  # Breeze7 模型名稱（請確認 Huggingface 上正確名稱）
 
-chat_history = []
+# 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)
+
+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()
 
-def chat(user_input):
-    chat_history.append(f"使用者: {user_input}")
-    prompt = "\n".join(chat_history[-6:]) + "\nAI:"
-    response = generator(prompt, max_new_tokens=100)[0]["generated_text"]
-    reply = response[len(prompt):].strip()
     chat_history.append(f"AI: {reply}")
-    return 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])
 
-gr.ChatInterface(chat).launch()
+demo.launch()

requirements.txt

@@ -1,2 +1,3 @@
 transformers
-torch
+torch
+gradio