Update app.py

app.py

@@ -8,10 +8,8 @@ import json
 model_id = "deepseek-ai/deepseek-coder-1.3b-base"
 lora_id = "Seunggg/lora-plant"
 
-# 加载 tokenizer
 tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
 
-# 加载基础模型
 base = AutoModelForCausalLM.from_pretrained(
     model_id,
     device_map="auto",
@@ -20,7 +18,6 @@ base = AutoModelForCausalLM.from_pretrained(
     trust_remote_code=True
 )
 
-# 加载 LoRA adapter
 model = PeftModel.from_pretrained(
     base,
     lora_id,
@@ -30,7 +27,6 @@ model = PeftModel.from_pretrained(
 
 model.eval()
 
-# 创建 pipeline
 from transformers import pipeline
 pipe = pipeline(
     "text-generation",
@@ -52,7 +48,6 @@ def respond(user_input):
     sensor_display = get_sensor_data()
     if not user_input.strip():
         return sensor_display, "请输入植物相关的问题 😊"
-
     prompt = f"用户提问：{user_input}\n"
     try:
         sensor_response = requests.get("https://arduino-realtime.onrender.com/api/data", timeout=5)
@@ -60,25 +55,26 @@ def respond(user_input):
         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："
-
         result = pipe(prompt)
         full_output = result[0]["generated_text"]
         answer = full_output.replace(prompt, "").strip()
     except Exception as e:
         answer = f"生成建议时出错：{str(e)}"
-
     return sensor_display, answer
 
-# Gradio 界面
-demo = gr.Interface(
-    fn=respond,
-    inputs=gr.Textbox(lines=4, label="植物问题"),
-    outputs=[
-        gr.Textbox(label="🧪 当前传感器数据", lines=6, interactive=False),
-        gr.Textbox(label="🤖 回答建议", lines=8, interactive=False)
-    ],
-    title="🌱 植物助手 - 实时联动版",
-    description="结合 Render 实时传感器数据 + 本地 LoRA 模型，生成更合理建议。"
-)
+def auto_update_sensor():
+    return gr.Textbox.update(value=get_sensor_data())
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 🌱 植物助手 - 实时联动版\n结合 Render 实时传感器数据 + 本地 LoRA 模型，生成更合理建议。")
+
+    sensor_box = gr.Textbox(label="🧪 当前传感器数据", lines=6, interactive=False)
+    user_input = gr.Textbox(label="植物问题", lines=4)
+    answer_box = gr.Textbox(label="🤖 回答建议", lines=8, interactive=False)
+
+    user_input.submit(fn=respond, inputs=user_input, outputs=[sensor_box, answer_box])
+
+    # 自动刷新传感器数据，每 5 秒
+    demo.load(fn=auto_update_sensor, inputs=None, outputs=sensor_box, every=5)
 
 demo.launch()