diff --git a/optimal_knn.py b/optimal_knn.py
index 53200c3119acb21350831b431d4ccb8cb3b2adc7..bbca8e6e4318ae5d7a47170ad28d2ecdae406b61 100644
--- a/optimal_knn.py
+++ b/optimal_knn.py
@@ -1,15 +1,48 @@
-# TODO: 导入必要的库和模块
+import numpy as np
+from sklearn.datasets import load_digits
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.metrics import accuracy_score
+import pickle
+import matplotlib.pyplot as plt
+import gradio as gr
 
-# TODO: 加载数字数据集
+# 加载保存的KNN模型
+with open('best_knn_model.pkl', 'rb') as f:
+    knn = pickle.load(f)
 
-# TODO: 将数据集划分为训练集和测试集
+# 定义预测函数
+def predict(image):
+    # 将输入的图像转换为与训练数据相同的格式
+    image = np.array(image).reshape(1, -1)
+    # 使用预训练的KNN模型进行预测
+    prediction = knn.predict(image)
+    return prediction[0]
 
-# TODO: 初始化变量以存储最佳准确率，相应的k值和最佳knn模型
+# 创建Gradio接口
+demo = gr.Interface(
+    predict,
+    gr.Image(label="输入图像"),
+    gr.Label(label="预测结果"),
+    title="手写数字识别",
+    description="输入一个手写数字图像，模型将预测出相应的数字。"
+)
 
-# TODO: 初始化一个列表以存储每个k值的准确率
+# 启动Gradio接口
+if __name__ == "__main__":
+    demo.launch()
+# Print the best accuracy and corresponding k value
+print(f"\nBest accuracy: {best_accuracy:.4f}")
+print(f"Best k value: {best_k}")
 
-# TODO: 尝试从1到40的k值，对于每个k值，训练knn模型，保存最佳准确率，k值和knn模型
+# Plot the relationship between k values and accuracy
+plt.figure(figsize=(10, 6))
+plt.plot(range(1, 41), accuracies, marker='o')
+plt.title('Relationship between K value and Accuracy')
+plt.xlabel('K value')
+plt.ylabel('Accuracy')
+plt.grid(True)
+plt.savefig('accuracy_plot.pdf')
+print("Accuracy plot saved as 'accuracy_plot.pdf'")
 
-# TODO: 将最佳KNN模型保存到二进制文件
-
-# TODO: 打印最佳准确率和相应的k值
\ No newline at end of file
+print("\nProcess completed successfully!")
\ No newline at end of file