Update app.py

app.py

@@ -11,7 +11,7 @@ lora_id = "Seunggg/lora-plant"
 # 加载 tokenizer
 tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
 
-# 加载基础模型，启用自动设备分配并脱载
+# 加载基础模型
 base = AutoModelForCausalLM.from_pretrained(
     model_id,
     device_map="auto",
@@ -20,7 +20,7 @@ base = AutoModelForCausalLM.from_pretrained(
     trust_remote_code=True
 )
 
-# 加载 LoRA adapter，同样启用脱载
+# 加载 LoRA adapter
 model = PeftModel.from_pretrained(
     base,
     lora_id,
@@ -30,7 +30,7 @@ model = PeftModel.from_pretrained(
 
 model.eval()
 
-# 生成 pipeline
+# 创建 pipeline
 from transformers import pipeline
 pipe = pipeline(
     "text-generation",
@@ -40,19 +40,13 @@ pipe = pipeline(
     max_new_tokens=256
 )
 
-from ask_api import ask_with_sensor  # 引入调用函数
-
 def get_sensor_data():
     try:
         sensor_response = requests.get("https://arduino-realtime.onrender.com/api/data", timeout=5)
         sensor_data = sensor_response.json().get("sensorData", None)
-        sensor_display = json.dumps(sensor_data, ensure_ascii=False, indent=2) if sensor_data else "暂无传感器数据"
+        return json.dumps(sensor_data, ensure_ascii=False, indent=2) if sensor_data else "暂无传感器数据"
     except Exception as e:
-        sensor_display = "⚠️ 获取失败：" + str(e)
-    return sensor_display
-
-def show_sensor_data(_=None):
-        return get_sensor_data()
+        return "⚠️ 获取失败：" + str(e)
 
 def respond(user_input):
     sensor_display = get_sensor_data()
@@ -75,21 +69,6 @@ def respond(user_input):
 
     return sensor_display, answer
 
-    # 构建提示词
-    prompt = f"用户提问：{user_input}\n"
-    if sensor_data:
-        prompt += f"当前传感器数据：{json.dumps(sensor_data, ensure_ascii=False)}\n"
-    prompt += "请用更人性化的语言生成建议，并推荐相关植物文献或资料。\n回答："
-
- # 模型生成
-try:
-    result = pipe(prompt)
-    full_output = result[0]["generated_text"]
-    # 删除重复的提问部分，只保留回答段
-    answer = full_output.replace(prompt, "").strip()
-except Exception as e:
-    answer = f"生成建议时出错：{str(e)}"
-
 # Gradio 界面
 with gr.Blocks() as demo:
     with gr.Row():
@@ -97,12 +76,13 @@ with gr.Blocks() as demo:
         question_box = gr.Textbox(label="🌿 植物问题", lines=4)
     answer_box = gr.Textbox(label="🤖 回答建议", lines=8, interactive=False)
 
-    # 实时轮询，每 2 秒自动更新
-    sensor_box.change(fn=show_sensor_data, inputs=None, outputs=sensor_box).every(2)
-
-    # 用户提问后，更新传感器 + 回答
+    # 用户提交提问后，更新传感器+回答
     question_box.submit(fn=respond, inputs=question_box, outputs=[sensor_box, answer_box])
 
-demo.launch()
+    # 定时每 5 秒自动刷新传感器数据
+    def update_sensor():
+        return get_sensor_data()
 
+    gr.Timer(interval=5, fn=update_sensor, outputs=sensor_box)
 
+demo.launch()