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import gradio as gr |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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from peft import PeftModel |
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import torch |
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import requests |
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import json |
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import time |
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def auto_refresh_sensor(): |
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while True: |
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sensor_box.value = sensor_display_text() |
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time.sleep(5) |
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demo.load(auto_refresh_sensor, None, None) |
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model_id = "deepseek-ai/deepseek-coder-1.3b-base" |
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lora_id = "Seunggg/lora-plant" |
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) |
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base = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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torch_dtype=torch.float32, |
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trust_remote_code=True |
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) |
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model = PeftModel.from_pretrained( |
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base, |
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lora_id, |
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torch_dtype=torch.float32 |
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) |
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model.eval() |
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from transformers import pipeline |
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pipe = pipeline( |
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"text-generation", |
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model=model, |
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tokenizer=tokenizer, |
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max_new_tokens=256 |
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) |
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def get_sensor_data(): |
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try: |
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res = requests.get("https://arduino-realtime.onrender.com/api/data", timeout=5) |
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sensor_data = res.json().get("sensorData", None) |
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return sensor_data if sensor_data else {} |
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except Exception as e: |
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return {"错误": str(e)} |
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def sensor_display_text(): |
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sensor_data = get_sensor_data() |
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return json.dumps(sensor_data, ensure_ascii=False, indent=2) if sensor_data else "暂无传感器数据" |
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def generate_answer(user_input): |
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if not user_input.strip(): |
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return "请输入植物相关的问题 😊" |
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prompt = f"用户提问：{user_input}\n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：" |
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try: |
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result = pipe(prompt) |
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output = result[0]["generated_text"] |
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return output.replace(prompt, "").strip() |
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except Exception as e: |
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return f"生成建议时出错：{str(e)}" |
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def update_chart(): |
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sensor_data = get_sensor_data() |
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if not sensor_data or "温度" not in sensor_data: |
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return gr.LinePlot.update(value=None) |
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return { |
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"data": [ |
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{"x": [0], "y": [sensor_data.get("温度", 0)], "name": "温度"}, |
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{"x": [0], "y": [sensor_data.get("湿度", 0)], "name": "湿度"} |
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], |
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"layout": {"title": "实时传感器数据"} |
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} |
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with gr.Blocks() as demo: |
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gr.Markdown("# 🌱 植物助手 - 实时传感器联动") |
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with gr.Row(): |
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sensor_box = gr.Textbox(label="🧪 当前传感器数据", lines=6, interactive=False) |
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chart = gr.LinePlot(label="📈 实时数据图表", x="x", y="y", overlay=True) |
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question = gr.Textbox(label="🌿 植物问题", lines=4, placeholder="请输入植物相关的问题 😊") |
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answer_box = gr.Textbox(label="🤖 回答建议", lines=8, interactive=False) |
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send_btn = gr.Button("发送") |
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send_btn.click(fn=generate_answer, inputs=question, outputs=answer_box) |
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demo.launch() |
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