Update app.py

app.py

@@ -1,25 +1,57 @@
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
 import torch
 
-# 你自己的模型 repo
-model_id = "Seunggg/lora-plant"
-
-# 加载模型和 tokenizer
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, device_map="auto")
-
-# 定义接口函数
-def plant_chat(user_input):
-    prompt = f"用户提问：{user_input}\n请用人性化语言回答，并推荐相关的植物资料或文献：\n回答："
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-    outputs = model.generate(**inputs, max_new_tokens=256)
-    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return answer
-
-# 启动 Gradio 接口
-gr.Interface(fn=plant_chat,
-             inputs="text",
-             outputs="text",
-             title="🌿 植物问答助手",
-             description="根据你的问题，提供植物养护建议和文献线索。").launch()
+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()
+