Update app.py

app.py

@@ -12,46 +12,46 @@ import gradio as gr
 def Tree_Detection(sample):
     
     sample=list(sample)
-	with open('lenses.txt', 'r') as fr:										# 加载文件
+    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_target = []														# 提取每组数据的类别，保存在列表里
+    lenses_target = []														# 提取每组数据的类别，保存在列表里
 	# print(lenses)
-	for each in lenses:
+    for each in lenses:
 		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
+    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 = []
 	# print(lenses_dict)														# 打印字典信息
-	lenses_pd = pd.DataFrame(lenses_dict)									# 生成pandas.DataFrame
+    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
+    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])
 	# print(lenses_pd)														# 打印编码信息
 
-	clf = tree.DecisionTreeClassifier(max_depth=None)						# 创建DecisionTreeClassifier()类
-	clf = clf.fit(lenses_pd.values.tolist(), lenses_target)					# 使用数据，构建决策树
+    clf = tree.DecisionTreeClassifier(max_depth=None)						# 创建DecisionTreeClassifier()类
+    clf = clf.fit(lenses_pd.values.tolist(), lenses_target)					# 使用数据，构建决策树
 
-	dot_data = StringIO()
-	tree.export_graphviz(clf, out_file=dot_data,							# 绘制决策树
+    dot_data = StringIO()
+    tree.export_graphviz(clf, out_file=dot_data,							# 绘制决策树
 						feature_names=lenses_pd.keys(),
 						class_names=clf.classes_,
 						filled=True, rounded=True,
 						special_characters=True)
 
-	graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
+    graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
 	# img = graph.write_jpg("tree.jpg")												# 保存绘制好的决策树，以JPG的形式存储。
 
-	result = clf.predict([sample])										# 预测
+    result = clf.predict([sample])										# 预测
 
-	return result
+    return result
 # print(Tree_Detection([2, 1, 1, 0]))
 
 def test(image):