app

app.py

@@ -1,22 +1,42 @@
 import gradio as gr
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+from sklearn.feature_selection import mutual_info_classif
+from sklearn.feature_selection import chi2
+from sklearn.linear_model import LinearRegression
+import numpy as np
 
-title = "Employee Experience"
 
-data_desc = gr.Interface.load(
-    "spaces/jaleesahmed/model-development",
-    title=None,
-    examples=[],
-    description="Data Description!",
-)
+def update(name):
+    df = pd.read_csv('emp_experience_data.csv')
+    pd.options.display.max_columns = 25
+    data_encoded = df.copy(deep=True)
+    categorical_column = ['Attrition', 'Gender', 'BusinessTravel', 'Education', 'EmployeeExperience', 'EmployeeFeedbackSentiments', 'Designation',
+                            'SalarySatisfaction', 'HealthBenefitsSatisfaction', 'UHGDiscountProgramUsage', 'HealthConscious', 'CareerPathSatisfaction', 'Region']
+    label_encoding = LabelEncoder()
+    for col in categorical_column:
+        data_encoded[col] = label_encoding.fit_transform(data_encoded[col])
 
-data_corr = gr.Interface.load(
-    "spaces/jaleesahmed/correlation-and-visualization",
-    title=None,
-    examples=[],
-    description="Data correlation and pattern visualization!",
-)
+    data_selected = data_encoded[['EmployeeExperience', 'HealthBenefitsSatisfaction', 'SalarySatisfaction', 'Designation', 'HealthConscious',
+                            'EmployeeFeedbackSentiments', 'Education', 'Gender', 'HoursOfTrainingAttendedLastYear', 'InternalJobMovement', 'Attrition']]
 
-demo = gr.TabbedInterface([data_desc, data_corr], ["Data Description", "Data-Correlation"])
+    input_data = data_selected.drop(['Attrition'], axis=1)
+    target_data = data_selected[['Attrition']]
+    input_data = data_selected[0:100]
+    validation_data = data_selected[100:198]
+    validation_input_data = validation_data.drop(['Attrition'], axis=1)
+    validation_target_data = validation_data[['Attrition']]
+    reg = LinearRegression().fit(validation_input_data, validation_target_data)
+    prediction_value = reg.predict(np.array([[2,2,1,3,1,2,0,1,40,1]]))
+    print(prediction_value)
+    return f"Prediction : , {prediction_value}!"
 
-if __name__ == "__main__":
-    demo.launch()
+with gr.Blocks() as demo:
+    gr.Markdown("Start typing below and then click **Run** to see the output.")
+    with gr.Row():
+        inp = gr.Textbox(placeholder="Enter Employee Experience Data")
+        out = gr.Textbox()
+    btn = gr.Button("Run")
+    btn.click(fn=update, inputs=inp, outputs=out)
+
+demo.launch()