Upload app.py

app.py

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+#Importing the libraries
+import gradio as gr
+import pickle
+import pandas as pd
+import numpy as np
+import joblib
+from PIL import Image
+
+#using joblib to load the model:
+encoder = joblib.load('encoder.joblib') # loading the encoder
+scaler = joblib.load('scaler.joblib') # loading the scaler
+model = joblib.load('model.joblib') # loading the model
+
+
+# Create a function that applies the ML pipeline and makes predictions
+def predict(age,gender,education,marital_status,race,employment_stat,wage_per_hour,working_week_per_year,industry_code,occupation_code,
+    total_employed,vet_benefit,tax_status,gains,losses,stocks_status,citizenship,mig_year,importance_of_record):
+
+
+
+    # Create a dataframe with the input data
+     input_df = pd.DataFrame({
+        'age': [age],
+        'gender': [gender],
+        'education': [education],
+        'marital_status': [marital_status],
+        'race': [race],
+        'employment_stat': [employment_stat],
+        'wage_per_hour': [wage_per_hour],
+        'working_week_per_year': [working_week_per_year],
+        'industry_code': [industry_code],
+        'occupation_code': [occupation_code],
+        'total_employed': [total_employed],
+        'vet_benefit': [vet_benefit],
+        'tax_status': [tax_status],
+        'gains': [gains],
+        'losses': [losses],
+        'stocks_status': [stocks_status],
+        'citizenship': [citizenship],
+        'mig_year': [mig_year],
+        'importance_of_record': [importance_of_record]
+
+ }) # type: ignore
+
+# Create a list with the categorical and numerical columns
+     cat_columns = [col for col in input_df.columns if input_df[col].dtype == 'object']
+     num_columns = [col for col in input_df.columns if input_df[col].dtype != 'object']
+
+    # # Impute the missing values
+    #  input_df_imputed_cat = cat_imputer.transform(input_df[cat_columns]) 
+    #  input_df_imputed_num = num_imputer.transform(input_df[num_columns]) 
+
+    # Encode the categorical columns
+     input_encoded_df = pd.DataFrame(encoder.transform(input_df[cat_columns]).toarray(),
+                                   columns=encoder.get_feature_names_out(cat_columns))
+
+    # Scale the numerical columns
+     input_df_scaled = scaler.transform(input_encoded_df)
+     input_scaled_df = pd.DataFrame(input_df_scaled , columns = num_columns)
+
+
+    #joining the cat encoded and num scaled
+     final_df = pd.concat([input_encoded_df, input_scaled_df], axis=1)
+
+
+    # Make predictions using the model
+     predict = model.predict(final_df)
+
+
+     prediction_label = "INCOME ABOVE LIMIT" if predict.item() == '1' else "INCOME BELOW LIMIT"
+
+
+     return prediction_label
+
+     #return predictions
+
+#define the input interface
+
+input_interface = []
+
+with gr.Blocks(css=".gradio-container {background-color:silver}") as app:
+    title = gr.Label('INCOME PREDICTION APP.')
+    img = gr.Image("income_image.png").style(height= 210 , width= 1250)
+
+ 
+    with gr.Row():
+        gr.Markdown("This application provides predictions on whether a person earns above or below the income level. Please enter the person's information below and click PREDICT to view the prediction outcome.")
+
+    with gr.Row():
+        with gr.Column(scale=4, min_width=500):
+            input_interface = [
+                gr.components.Number(label="How Old are you?"),
+                gr.components.Radio(['male', 'female'], label='What is your Gender?'),
+                gr.components.Dropdown(['High School', 'left', 'Undergrad', 'Grad', 'Associate Degree',
+                                         'Doctorate'], label='What is your level of education?'),
+                gr.components.Dropdown(['Widowed', 'Single', 'Married', 'Divorced', 'Separated'], label='Marital Status?'),
+                gr.components.Dropdown([' White', ' Black', ' Asian or Pacific Islander',
+                                        ' Amer Indian Aleut or Eskimo', ' Other'], label='Whats your race?'),
+                gr.components.Dropdown([0, 2, 1], label='Whats your emploment status? (0 = Unemployed, 1 = Self-Employed, 2 = Employed)'),
+                gr.components.Number(label='How much is your Wage per Hour? (0 - 10000)'),
+                gr.components.Number(label='How many weeks have you worked in a year? (1 - 52)'),
+                gr.components.Number(label='How many working weeks per year do you work?'),
+                gr.components.Number(label='What is your Industry Code? (1 - 51)'),
+                gr.components.Number(label='What is your occupation Code? (1 - 46)'),
+                gr.components.Number(label='Number of persons working for employer? (1 - 7)'),
+                gr.components.Number(label='Benefit? (1 - 3)'),
+                gr.components.Dropdown([' Head of household', ' Single', ' Nonfiler', ' Joint both 65+',
+                                        ' Joint one 65+ & one under 65', ' Joint one under 65 & one 65+'],label='Whats your tax status?'),
+                gr.components.Number(label='What is your Gain'), 
+                gr.components.Number(label='What is your Loss'),
+                gr.components.Number(label='What is your Stock Status'),
+                gr.components.Dropdown(['Native', ' Foreign born- Not a citizen of U S ',
+                                         ' Foreign born- U S citizen by naturalization',
+                                         ' Native- Born abroad of American Parent(s)',
+                                         ' Native- Born in U S',' Native- Born in Puerto Rico or U S Outlying'], label='Whats is your Citizenshiip?'),
+                gr.components.Radio([94,95], label='Whats your year of migration?'),
+                gr.components.Number(label='Whats your Weight Of Instance?')    
+                       
+            ]
+
+    with gr.Row():
+        predict_btn = gr.Button('Predict') 
+        
+ 
+
+# Define the output interfaces
+    output_interface = gr.Label(label="INCOME ABOVE LIMIT")
+
+    predict_btn.click(fn=predict, inputs=input_interface, outputs=output_interface)
+
+
+    app.launch(share=False)