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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch
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
)
def respond(user_input):
if not user_input.strip():
return "请输入植物相关的问题 :)"
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："
result = pipe(prompt)
return result[0]["generated_text"]
# Gradio 界面
gr.Interface(
fn=respond,
inputs=gr.Textbox(lines=4, placeholder="在这里输入你的植物问题..."),
outputs="text",
title="🌱 植物助手 LoRA 版",
description="基于 DeepSeek 微调模型，提供植物养护建议和文献推荐。",
allow_flagging="never"
).launch()
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