Create app.py

app.py

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+import numpy as np
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+import gradio as gr
+
+df = pd.read_csv("credit_risk_dataset.csv")
+
+df = df.dropna()
+
+df.columns
+
+X =df.drop(["loan_status", "loan_percent_income"], axis = 1)
+y = df['loan_status']
+
+categorical_features = ["person_home_ownership", "loan_intent", "loan_grade", "cb_person_default_on_file"]
+X = pd.get_dummies(X, categorical_features)
+X.columns
+
+
+
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
+
+
+from sklearn.preprocessing import StandardScaler
+scaler_normal = StandardScaler()
+def scaler(data, runtime = False):
+    normal_col = ['person_income','person_age','person_emp_length', 'loan_amnt','loan_int_rate','cb_person_cred_hist_length']
+    if(runtime == False):
+        data.loc[:,normal_col] = scaler_normal.fit_transform(data.loc[:,normal_col])
+    else:
+        data.loc[:,normal_col] = scaler_normal.transform(data.loc[:,normal_col])
+    return data
+
+X_train = scaler(X_train)
+X_test = scaler(X_test, True)
+
+rf_model = RandomForestClassifier(max_depth = 5)
+rf_model.fit(X_train, y_train)
+
+y_predict = rf_model.predict(X_test)
+y_predict
+
+
+features = {
+    "person_home_ownership": ['MORTGAGE', 'OTHER','OWN', 'RENT',],
+    "loan_intent": ['DEBTCONSOLIDATION', 'EDUCATION', 'HOMEIMPROVEMENT', 'MEDICAL', 'PERSONAL', 'VENTURE'],
+    "loan_grade": ['A','B', 'C', 'D', 'E','F', 'G'],
+    "cb_person_default_on_file": ['N', 'Y']
+}
+def preprocess(model_input):
+    for feature in features:
+        for option in features[feature]:
+            selection = model_input[feature]
+            if option is selection:
+                model_input[f'{feature}_{option}'] = 1
+            else:
+                model_input[f'{feature}_{option}'] = 0
+
+    model_input.drop([_ for _ in features], inplace = True, axis = 1)
+    return model_input
+
+def credit_run(person_age, person_emp_length,person_home_ownership,cb_person_default_on_file,loan_intent,loan_grade,person_income, loan_amnt,
+       loan_int_rate, cb_person_cred_hist_length):
+    model_input = preprocess(
+    pd.DataFrame( { 'person_age': person_age,
+                   'person_income': person_income,
+                   'person_home_ownership': person_home_ownership,
+                   'person_emp_length': person_emp_length,
+                   'loan_intent': loan_intent,
+                   'loan_grade': loan_grade,
+                   'loan_amnt': loan_amnt,
+                   'loan_int_rate': loan_int_rate,
+                   'cb_person_default_on_file': cb_person_default_on_file,
+                   'cb_person_cred_hist_length': cb_person_cred_hist_length
+                  }, index = [0]
+    ))
+    out = rf_model.predict(model_input)
+    return "High risk of defaulting" if out[0] == 1 else "Low risk of defaulting"
+import gradio as gr
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column(scale=1,min_width=600):
+            gr.Image("non_payment_logo.jpg").style(height='7')
+            person_age=gr.Slider(label="Person Age(In Years)", minimum=18, maximum=90, step=1)
+            Pererson_Emp_Length=gr.Slider(label="Pererson Emp Length(In Years)", minimum=0, maximum=60, step=1)
+
+        with gr.Column(scale=2,min_width=600):
+            with gr.Row():
+                with gr.Column(scale=1,min_width=400):
+                    Home_Ownership_Status=gr.Radio(['MORTGAGE', 'OTHER','OWN', 'RENT'],label="Home Ownership Status")
+                with gr.Column(scale=2,min_width=100):
+                    Person_Defaulted_in_History=gr.Radio(['0', '1'],label="Person Defaulted in History")
+
+            with gr.Row():
+                with gr.Column(scale=3,min_width=300):
+                    Credit_Intent=gr.Dropdown(['DEBTCONSOLIDATION', 'EDUCATION', 'HOMEIMPROVEMENT', 'MEDICAL', 'PERSONAL', 'VENTURE'],label="Credit Intent")
+                with gr.Column(scale=4,min_width=300):
+                    Type_Of_Credit=gr.Dropdown(['A','B', 'C', 'D', 'E','F', 'G'],label="Type Of Credit")
+            with gr.Row():
+                with gr.Column(scale=3,min_width=300):
+                    Person_Income=gr.Number(label="Person Income(per month)")
+                with gr.Column(scale=4,min_width=300):
+                    Loan_Amount=gr.Number(label="Loan Amount")
+            with gr.Row():
+                with gr.Column(scale=3,min_width=300):
+                    Loan_Interest_Rate=gr.Number(label="Loan Interest Rate")
+                with gr.Column(scale=4,min_width=300):
+                    Person_Credit_History_Length=gr.Number(label="Person's Credit History Length")
+            with gr.Row():
+                with gr.Column():
+                    default= gr.Radio(['Low risk of defaulting', 'High risk of defaulting'])
+
+            btn = gr.Button("PREDICT")
+            btn.click(fn=credit_run, inputs=[person_age,Person_Income,Home_Ownership_Status,Pererson_Emp_Length,Credit_Intent,Type_Of_Credit,Loan_Amount,Loan_Interest_Rate,Person_Defaulted_in_History,Person_Credit_History_Length], outputs=[default])
+            #gr.Examples(inputs=[person_age,Pererson_Emp_Length,Home_Ownership_Status,Person_Defaulted_in_History,Credit_Intent,Type_Of_Credit,Person_Income,Loan_Amount,Loan_Interest_Rate,Person_Credit_History_Length])
+demo.launch(debug=True)