Switch to CodeT5+ for better transformers compatibility

app.py

@@ -2,10 +2,10 @@ import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 import torch
 
-# 加载 CodeT5 模型
-model_name = "Salesforce/codet5-base"
-tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+# 加载 CodeT5+ 模型（兼容性更好）
+model_name = "Salesforce/codet5p-220m"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name, trust_remote_code=True)
 
 def generate_code(prompt: str, max_length: int = 128) -> str:
     """代码生成/补全"""
@@ -36,8 +36,8 @@ demo = gr.Interface(
         gr.Slider(32, 512, value=128, step=32, label="Max Length")
     ],
     outputs=gr.Textbox(label="Generated Code", lines=10),
-    title="CodeT5 Code Generation",
-    description="基于 Salesforce CodeT5 的代码生成模型。支持代码补全、代码生成等任务。",
+    title="CodeT5+ Code Generation",
+    description="基于 Salesforce CodeT5+ (220M) 的代码生成模型。支持代码补全、代码生成等任务。",
     examples=[
         ["def fibonacci(n):", 128],
         ["# Python function to calculate factorial", 128],