app

app.py

@@ -4,6 +4,7 @@ from sklearn.preprocessing import LabelEncoder
 
 def data_description(desc_type):
     df = pd.read_csv('emp_experience_data.csv')
+    data_encoded = df.copy(deep=True)
     pd.options.display.max_columns = 25
     pd.options.display.max_rows = 10
     if desc_type == "Display Data":
@@ -13,22 +14,21 @@ def data_description(desc_type):
         data_desc = df_copy.describe()
         data_desc.insert(0, "Description", ["count", "mean", "std", "min", "25%", "50%", "75%", "max"], True)
         return data_desc
-    if desc_type == "Encode Data":
+    if desc_type == "Display Encoding":
         categorical_column = ['Attrition', 'Gender', 'BusinessTravel', 'Education', 'EmployeeExperience', 'EmployeeFeedbackSentiments', 'Designation', 'SalarySatisfaction', 
                                 'HealthBenefitsSatisfaction', 'UHGDiscountProgramUsage', 'HealthConscious', 'CareerPathSatisfaction', 'Region']
-        data_encoded = df.copy(deep=True)
         label_encoding = LabelEncoder()
         data = [["Feature", "Mapping"]]
-        # Create the pandas DataFrame
         for col in categorical_column:
             data_encoded[col] = label_encoding.fit_transform(data_encoded[col])
             le_name_mapping = dict(zip(label_encoding.classes_, label_encoding.transform(label_encoding.classes_)))
             data.append([col, str(le_name_mapping)])
-        #df_dict = pd.DataFrame(data, columns=['Column', 'Mapping'])
         return data
+    if desc_type == "Display Encoded Data":
+        return data_encoded.head()
 
 inputs = [
-        gr.Dropdown(["Display Data", "Describe Data", "Encode Data"], label="Data Actions")
+        gr.Dropdown(["Display Data", "Describe Data", "Display Encoding", "Display Encoded Data"], label="Perform Data Actions")
     ]
 
 outputs = [gr.DataFrame()]