Create app.py

app.py

@@ -0,0 +1,173 @@
+## IMPORTING LIBS
+
+import pandas as pd
+import plotly.express as px
+
+from sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix
+
+import pickle
+import gradio as gr
+
+## CREATING FUNCTION
+
+def predict_credit_worthiness(name, x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22):
+    path =  'german_credit_risk/model/model.pickle'
+    greet = 'Hey, ' + name + '!'
+    with open(path, 'rb') as file:
+        model = pickle.load(file)
+        inputs = {'Account Balance_1': int(x1),
+                  'Account Balance_2': int(x2),
+                  'Account Balance_3': int(x3),
+                  'Payment Status of Previous Credit_1': int(x4),
+                  'Payment Status of Previous Credit_3': int(x5),
+                  'Purpose_1': int(x6),
+                  'Purpose_4': int(x7),
+                  'Value Savings/Stocks_1': int(x8),
+                  'Value Savings/Stocks_3': int(x9),
+                  'Value Savings/Stocks_5': int(x10),
+                  'Length of current employment_1': int(x11),
+                  'Length of current employment_4': int(x12),
+                  'Instalment per cent_4': int(x13),
+                  'Guarantors_1': int(x14),
+                  'Duration in Current address_1': int(x15),
+                  'Duration in Current address_2': int(x16),
+                  'Most valuable available asset_1': int(x17),
+                  'Most valuable available asset_4': int(x18),
+                  'Concurrent Credits_3': int(x19),
+                  'Type of apartment_1': int(x20),
+                  'No of Credits at this Bank_1': int(x21),
+                  'Occupation_1': int(x22)
+                  }
+        prediction = model.predict([list(inputs.values())])
+        
+    y_test = pd.read_parquet('german_credit_risk/data/processed/y_test.parquet')
+    y_test = y_test.squeeze()
+
+    yhat = pd.read_parquet('german_credit_risk/data/processed/yhat.parquet')
+    yhat = yhat.squeeze()
+    
+    precision = precision_score(y_test, yhat).round(2)
+    recall = recall_score(y_test, yhat).round(2)
+    f1 = f1_score(y_test, yhat).round(2)
+
+    features_names =  ['No account', 'No balance', 'Some balance', 'No credit problems', 
+                       'Some credit problems', 'New car', 'Other purpose', 'No savings', 
+                       'DM betwenn [100, 1000]', 'DM >= 1000', 'Employment: <1 year (or unemployed)', 'Employment: 4<x<7 years', 
+                       'Installment smaller than 20%', 'No guarantors', 'Less than a year in same address', '1<x<4 years in address', 
+                       'Not available / no assets', 'Ownership of house or land',  'No further running credits', 'Free ap', 
+                       'One credit at thins bank','Unemployed or unskilled']
+    importance = model.feature_importances_
+    data = pd.DataFrame()
+    data['Feature Importance'] = importance
+    data['Feature'] = features_names
+    p = px.bar(data, y='Feature Importance', x='Feature', width=1200, height=500)
+    
+    cfm = confusion_matrix(y_test, yhat)
+    cfm_plot = px.imshow(cfm,
+                x=['Predicted 0', 'Predicted 1'],
+                y=['Actual 0', 'Actual 1'],
+                color_continuous_scale='Blues',
+                labels=dict(x="Predicted", y="Actual", color="Count"),
+                text_auto=True)    
+    
+    if prediction == 1:
+        return (greet + ' According to our model, your client is eligible for the loan.', 
+                'Precision: '+ str(precision), 
+                'Recall: '+ str(recall), 
+                'F1 Score: '+ str(f1), 
+                p, 
+                cfm_plot)
+    else:
+        return (greet + ' Unfortunately, according to our model, your client is not eligible for the loan for now :(.', 
+                'Precision: '+ str(precision), 
+                'Recall: '+ str(recall), 
+                'F1 Score: '+ str(f1), 
+                p, 
+                cfm_plot)
+    
+## creating the interface
+
+with gr.Blocks() as demo:
+    gr.Markdown('# Credit Worthiness Prediction')
+    gr.Markdown("""
+                To predict our clients' creditworthiness, please use this application as follows:
+                
+                1. Enter your name and navigate through the client's information tabs. Select the boxes that best match your client's characteristics. Leave blank if none apply.
+
+                2. Once completed, click 'Predict' to determine if the client is creditworthy.
+                """)
+    with gr.Accordion('Name'):
+        name = gr.Textbox(lines=1, label='Your name')
+    with gr.Accordion("Enter your client's information"):
+        with gr.Tab('Account Balance'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x1 = gr.Checkbox(1, label='No account')
+            x2 = gr.Checkbox(0, label='No balance')
+            x3 = gr.Checkbox(0, label='Some balance')
+        with gr.Tab('Payment status of previous credit'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x4 = gr.Checkbox(1, label='Some problems')
+            x5 = gr.Checkbox(0, label='No problems in this bank')
+        with gr.Tab('Purpose'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x6 = gr.Checkbox(1, label='New car')
+            x7 = gr.Checkbox(0, label='Other')
+        with gr.Tab('Value savings/stocks'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x8 = gr.Checkbox(1, label='No savings')
+            x9 = gr.Checkbox(0, label='DM betwenn [100, 1000]')
+            x10 = gr.Checkbox(0, label='DM >= 1000')
+        with gr.Tab('Length of current employment'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x11 = gr.Checkbox(1, label='Below 1 year (or unemployed)')
+            x12 = gr.Checkbox(0, label='Between 4 and 7 years')
+        with gr.Tab('Instalment per cent'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x13 = gr.Checkbox(0, label='Smaller than 20%')
+        with gr.Tab('Guarantors'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x14 = gr.Checkbox(0, label='No guarantors')
+        with gr.Tab('Duration in current address'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x15 = gr.Checkbox(1, label='Less than a year')
+            x16 = gr.Checkbox(0, label='Between 1 and 4 years')
+        with gr.Tab('Most valuable available asset'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x17 = gr.Checkbox(1, label='Not available / no assets')
+            x18 = gr.Checkbox(0, label='Ownership of house or land')
+        with gr.Tab('Concurrent credits'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x19 = gr.Checkbox(0, label='No further running credits')
+        with gr.Tab('Type of apartment'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x20 = gr.Checkbox(0, label='Free apartment')
+        with gr.Tab('Number of credits at this Bank'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x21 = gr.Checkbox(0, label='One credit')        
+        with gr.Tab('Occupation'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x22 = gr.Checkbox(0, label='Unemployed or unskilled with no permanent') 
+    predict_button = gr.Button('Predict')
+    prediction_output = gr.Label(num_top_classes=2)
+    with gr.Accordion('Metrics and plots'):
+        with gr.Tab('Metrics'):
+            with gr.Row():
+                precision_output = gr.Label()
+            with gr.Row():
+                recall_output = gr.Label()
+            with gr.Row():
+                f1_output = gr.Label()
+        with gr.Tab('Feature Importances'):
+            fimp_output = gr.Plot()
+        with gr.Tab('Confusion Matrix'):
+            cfm_output = gr.Plot()
+    predict_button.click(fn=predict_credit_worthiness,
+                         inputs=[name, x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22],
+                         outputs=[prediction_output,precision_output, recall_output, f1_output, fimp_output, cfm_output])
+    gr.Markdown('''
+                Want to work in a project together or have interest in my services? Reach me:
+                [Linkedin](https://www.linkedin.com/in/marcilioduarte98/)
+                [Github](https://github.com/marcilioduarte)
+                @marcilioduarte | Economics and Data Science
+                ''')
+demo.launch()