Update app.py

app.py

@@ -7,11 +7,12 @@ from sklearn.impute import KNNImputer
 from sklearn.decomposition import PCA
 import pickle
 from tensorflow.keras.models import load_model
+import pickle
 
 
 
 # # Define the prediction function
-def predict(age, workclass, education, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country):
+def predict_ann(age, workclass, education, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country):
     # columns = {
     # "age": [age], "workclass":[workclass], "educational-num":[education], "marital-status":[marital_status], "occupation":[occupation], 
     # "relationship":[relationship], "race":[race], "gender":[gender], "capital-gain":[capital_gain], "capital-loss":[capital_loss], 
@@ -32,6 +33,27 @@ def predict(age, workclass, education, marital_status, occupation, relationship,
     # prediction = 1
     return "Income >50K" if prediction == 1 else "Income <=50K"
 
+def rf_interface(age, workclass, education, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country):
+    # columns = {
+    # "age": [age], "workclass":[workclass], "educational-num":[education], "marital-status":[marital_status], "occupation":[occupation], 
+    # "relationship":[relationship], "race":[race], "gender":[gender], "capital-gain":[capital_gain], "capital-loss":[capital_loss], 
+    # "hours-per-week":[hours_per_week], "native-country":[native_country]}
+    columns = { "0":[0],
+    "age": [age], "workclass":[workclass], "educational-num":[education], "occupation":[occupation],
+    "race":[race], "gender":[gender], "capital-gain":[capital_gain], "capital-loss":[capital_loss], 
+    "hours-per-week":[hours_per_week], "native-country":[native_country]}
+    df = pd.DataFrame(data=columns)
+    fixed_features = cleaning_features(df,race)
+    print(fixed_features)
+    # with open('ann_model.pkl', 'rb') as ann_model_file:
+    #     ann_model = pickle.load(ann_model_file)
+    scaler = StandardScaler()
+    X = scaler.fit_transform(fixed_features)
+    rf_model = pickle.load(open('rf_model.pkl', 'rb'))
+    prediction = rf_model.predict(fixed_features)
+    # prediction = 1
+    return "Income >50K" if prediction == 1 else "Income <=50K"
+
 def cleaning_features(data,race):
     # with open('race_onehot_encoder.pkl', 'rb') as enc_file:
     #     encoder = pickle.load(enc_file)
@@ -135,58 +157,46 @@ def hbdscan_tranform(df_transformed):
     df_transformed[numerical_features] = scaler.fit_transform(df_transformed[numerical_features])
     return df_transformed
 
+# Launch the app
+interface.launch()
 
-# Create the Gradio interface
-interface = gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.Slider(18, 90, step=1, label="Age"),
-        gr.Dropdown(
-            ["Private", "Self-emp-not-inc", "Self-emp-inc", "Federal-gov", 
-             "Local-gov", "State-gov", "Without-pay", "Never-worked"], 
-            label="Workclass"
-        ),
-        gr.Dropdown(
-            ["Bachelors", "Some-college", "11th", "HS-grad", "Prof-school", 
-             "Assoc-acdm", "Assoc-voc", "9th", "7th-8th", "12th", "Masters", 
-             "1st-4th", "10th", "Doctorate", "5th-6th", "Preschool"], 
-            label="Education"
-        ),
-        gr.Dropdown(
-            ["Married-civ-spouse", "Divorced", "Never-married", "Separated", 
-             "Widowed", "Married-spouse-absent", "Married-AF-spouse"], 
-            label="Marital Status"
-        ),
-        gr.Dropdown(
-            ["Tech-support", "Craft-repair", "Other-service", "Sales", 
-             "Exec-managerial", "Prof-specialty", "Handlers-cleaners", 
-             "Machine-op-inspct", "Adm-clerical", "Farming-fishing", 
-             "Transport-moving", "Priv-house-serv", "Protective-serv", 
-             "Armed-Forces"], 
-            label="Occupation"
-        ),
-        gr.Dropdown(
-            ["Wife", "Husband", "Own-child", "Unmarried", "Other-relative", "Not-in-family"], 
-            label="Relationship"
-        ),
-        gr.Dropdown(
-            ["White", "Black", "Asian-Pac-Islander", "Amer-Indian-Eskimo", "Other"], 
-            label="Race"
-        ),
-        gr.Dropdown(
-            ["Male", "Female"], 
-            label="Gender"
-        ),
-        gr.Slider(1, 60, step=1, label="Hours Per Week"),
-        gr.Slider(0, 100000, step=100, label="Capital Gain"),
-        gr.Slider(0, 5000, step=50, label="Capital Loss"),
-        gr.Dropdown(
-            ["United-States", "Other"], 
-            label="Native Country"
-        )
-    ],
+# Shared inputs
+inputs = [
+    gr.Slider(18, 90, step=1, label="Age"),
+    gr.Dropdown(["Male", "Female"], label="Gender"),
+    gr.Dropdown(["Private", "Self-emp-not-inc", "Self-emp-inc", "Federal-gov", "Local-gov", "State-gov", "Without-pay", "Never-worked"], label="Workclass"),
+    gr.Dropdown(["Preschool", "1st-4th", "5th-6th", "7th-8th", "9th", "10th", "11th", "12th", "HS-grad", "Some-college", "Assoc-voc", "Assoc-acdm", "Bachelors", "Masters", "Doctorate", "Prof-school"], label="Education"),
+    gr.Dropdown(["Married-civ-spouse", "Divorced", "Never-married", "Separated", "Widowed", "Married-spouse-absent", "Married-AF-spouse"], label="Marital Status"),
+    gr.Dropdown(["Tech-support", "Craft-repair", "Other-service", "Sales", "Exec-managerial", "Prof-specialty", "Handlers-cleaners", "Machine-op-inspct", "Adm-clerical", "Farming-fishing", "Transport-moving", "Priv-house-serv", "Protective-serv", "Armed-Forces"], label="Occupation"),
+    gr.Dropdown(["Wife", "Husband", "Own-child", "Not-in-family", "Other-relative", "Unmarried"], label="Relationship"),
+    gr.Dropdown(["White", "Black", "Asian-Pac-Islander", "Amer-Indian-Eskimo", "Other"], label="Race"),
+    gr.Slider(0, 100000, step=100, label="Capital Gain"),
+    gr.Slider(0, 5000, step=50, label="Capital Loss"),
+    gr.Slider(1, 60, step=1, label="Hours Per Week"),
+    gr.Dropdown(["United-States", "Canada", "Mexico", "Other"], label="Native Country")
+]
+
+# Interfaces for each model
+ann_interface = gr.Interface(
+    fn=predict_ann,
+    inputs=inputs,
+    outputs="text",
+    title="Artificial Neural Network",
+    description="Predict income using an Artificial Neural Network."
+)
+
+rf_interface = gr.Interface(
+    fn=predict_rf,
+    inputs=inputs,
     outputs="text",
-    title="Adult Income Predictor"
+    title="Random Forest",
+    description="Predict income using a Random Forest model."
+)
+
+# Combine the interfaces into tabs
+interface = gr.TabbedInterface(
+    [ann_interface, rf_interface],
+    ["ANN Model", "Random Forest Model"]
 )
 
 # Launch the app