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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch
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，启用自动设备分配并脱载
base = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
offload_folder="offload/",
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
trust_remote_code=True
)
# 加载 LoRA adapter，同样启用脱载
model = PeftModel.from_pretrained(
base,
lora_id,
offload_folder="offload/",
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
)
model.eval()
# 生成 pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
device_map="auto",
max_new_tokens=256
)
from ask_api import ask_with_sensor # 引入调用函数
def respond(user_input):
if not user_input.strip():
return "请输入植物相关的问题 😊"
# 获取 Render 实时传感器数据
try:
sensor_response = requests.get("https://arduino-realtime.onrender.com/api/data", timeout=5)
sensor_data = sensor_response.json().get("sensorData", None)
except Exception as e:
sensor_data = None
# 生成用于 LoRA 本地推理的 prompt
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："
# 本地 LoRA 推理
try:
result = pipe(prompt)
return result[0]["generated_text"]
except Exception as e:
return f"生成建议时出错：{str(e)}"
# Gradio 界面
gr.Interface(
fn=respond,
inputs=[
gr.Textbox(lines=4, label="植物问题"),
gr.Textbox(lines=2, label="传感器数据 (JSON 格式)", placeholder='{"temperature": 25, "humidity": 60}')
],
outputs="text",
title="🌱 植物助手 - 本地 LoRA + Render 联动版",
description="结合本地建议和传感器分析结果。"
).launch()
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