Update app.py

app.py

@@ -13,27 +13,27 @@ def Tree_Detection(sample):
     
     sample=list(sample)
     with open('lenses.txt', 'r') as fr:										# 加载文件
-		lenses = [inst.strip().split('\t') for inst in fr.readlines()]		# 处理文件
+        lenses = [inst.strip().split('\t') for inst in fr.readlines()]		# 处理文件
     lenses_target = []														# 提取每组数据的类别，保存在列表里
 	# print(lenses)
     for each in lenses:
-		lenses_target.append(each[-1])
+        lenses_target.append(each[-1])
 	# print(lenses_target)
 
     lensesLabels = ['noise', 'rotation', 'power-up', 'temp']			# 特征标签
     lenses_list = []														# 保存lenses数据的临时列表
     lenses_dict = {}														# 保存lenses数据的字典，用于生成pandas
     for each_label in lensesLabels:											# 提取信息，生成字典
-		for each in lenses:
-			lenses_list.append(each[lensesLabels.index(each_label)])
-		lenses_dict[each_label] = lenses_list
-		lenses_list = []
+        for each in lenses:
+            lenses_list.append(each[lensesLabels.index(each_label)])
+        lenses_dict[each_label] = lenses_list
+        lenses_list = []
 	# print(lenses_dict)														# 打印字典信息
     lenses_pd = pd.DataFrame(lenses_dict)									# 生成pandas.DataFrame
 	# print(lenses_pd)														# 打印pandas.DataFrame
     le = LabelEncoder()														# 创建LabelEncoder()对象，用于序列化
     for col in lenses_pd.columns:											# 序列化
-		lenses_pd[col] = le.fit_transform(lenses_pd[col])
+        lenses_pd[col] = le.fit_transform(lenses_pd[col])
 	# print(lenses_pd)														# 打印编码信息
 
     clf = tree.DecisionTreeClassifier(max_depth=None)						# 创建DecisionTreeClassifier()类