added files

app.py

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+import gradio as gr
+import shap
+import pickle
+import pandas as pd
+import matplotlib.pyplot as plt
+from pathlib import Path
+
+# load our models 
+boost_path = Path(__file__).parents[0] / "boost.sav"
+boost = pickle.load(open(boost_path,"rb"))
+
+# functions
+
+# preprocessing data function
+def preprocess(data):
+    columns = ['distance_from_home', 'distance_from_last_transaction',
+                'ratio_to_median_purchase_price', 'repeat_retailer', 'used_chip',
+                'used_pin_number', 'online_order']
+    
+    df = pd.DataFrame([data], columns = columns)
+    
+    # convert data type
+    df[['repeat_retailer','used_chip','used_pin_number','online_order']] = df[['repeat_retailer','used_chip','used_pin_number','online_order']].astype('int')
+    
+    return df
+
+
+# Prediction function with probabilities
+def predict(*data):
+        df = preprocess(data)
+        prob_pred = boost.predict_proba(df)
+        return {"Normal": float(prob_pred[0][0]), "Fraud": float(prob_pred[0][1])}
+
+# plot function
+def interpret(*data):
+        plt.style.use("fivethirtyeight")
+        
+        df = preprocess(data)
+        
+        explainer = shap.TreeExplainer(boost)
+        shap_values = explainer.shap_values(df)
+        scores_desc = list(zip(shap_values[0], df.columns))
+        scores_desc = sorted(scores_desc)
+        fig_m = plt.figure(tight_layout=True)
+        plt.barh([s[1] for s in scores_desc], [s[0] for s in scores_desc])
+        plt.title("Feature Shap Values")
+        plt.ylabel("Shap Value")
+        plt.xlabel("Feature Importance")
+        plt.tight_layout()
+        
+        return fig_m
+    
+    
+    
+with gr.Blocks() as demo:
+    gr.HTML("""
+    <h1 align="center">Credit Card Fraud Prediction System</h1>
+    """)
+    with gr.Row():
+        with gr.Column():
+            repeated_retailer = gr.Radio(["No","Yes"], type = "index", label = "Repeat Retailer", info ="Was the transaction at at a repeated store?")
+            
+            online_order = gr.Radio(["No","Yes"], type = "index", label = "Online Order", info ="Was the transaction an online order?")
+            
+            used_chip = gr.Radio(["No","Yes"], type = "index", label = "Used Chip", info ="did the purchase use the security chip of the card?")
+
+            used_pin = gr.Radio(["No","Yes"], type = "index", label = "Used Pin Number", info ="Did the transaction use the pin code of the card?")
+            
+            distance_home = gr.Number(value = 25, label = "Distance From Home (miles)", info = "How far was the transaction from the card owner's house? (in Miles)")
+            
+            distance_last = gr.Number(value = 5, label = "Distance From Last Transaction (miles)", info = "How far away was the it from the last transaction that happened? (in Miles)")
+            
+            gr.HTML("""
+                <h4 align="center">Ratio Median Purchase Price Equation</h4>
+                    """)
+            ratio_median = gr.Number(value = 1.8, label = "Ratio Median Purchase Price", info = "Divide the purchase price by card owners median purchase price?")
+        
+            
+        with gr.Column():
+            label = gr.Label()
+            plot = gr.Plot()
+            with gr.Row():
+                predict_btn = gr.Button(value="Predict")
+                interpret_btn = gr.Button(value="Explain")
+            predict_btn.click(
+                predict,
+                inputs= [
+                    distance_home,
+                    distance_last,
+                    ratio_median,
+                    repeated_retailer,
+                    used_chip,
+                    used_pin,
+                    online_order   
+                ],
+                outputs=[label],
+            )
+            interpret_btn.click(
+                interpret,
+                inputs=[
+                    distance_home,
+                    distance_last,
+                    ratio_median,
+                    repeated_retailer,
+                    used_chip,
+                    used_pin,
+                    online_order   
+                ],
+                outputs=[plot],
+            )
+
+demo.launch()