Update app.py

app.py

@@ -17,12 +17,7 @@ with open("pca_model.pkl", "rb") as file:
     pca = pickle.load(file)
 
 # Dummy data for generating choices
-df = pd.DataFrame({
-    'card_type': ['Visa', 'MasterCard', 'Discover'],
-    'location': ['Location1', 'Location2'],
-    'purchase_category': ['Groceries', 'Travel', 'Retail'],
-    'country': ['USA', 'Canada']
-})
+df = pd.read_csv('/content/synthetic_financial_data.csv')
 
 # Choices for dropdown menus
 card_type_choices = [(val, val) for val in df['card_type'].unique()]
@@ -32,37 +27,40 @@ country_choices = [(val, val) for val in df['country'].unique()]
 
 # Function to predict
 def predict(transaction_id, customer_id, merchant_id, amount, transaction_time, card_type, location, purchase_category, customer_age, transaction_description, country):
-    data = pd.DataFrame({
-        'amount': [amount],
-        'country': [country],
-        'customer_age': [customer_age],
-        'card_type': [card_type],
-        'purchase_category': [purchase_category]
-    })
-    data = pd.get_dummies(data, columns=['card_type', 'purchase_category'])
-    data['country'] = data['country'].map(fraud_mean_by_country_dict)
-    data['Age_group'] = data['customer_age'].apply(lambda x: 'Age_group_60+' if x > 60 else ('Age_group_33-60' if x >= 33 else 'Age_group_<33'))
-    data = pd.get_dummies(data, columns=['Age_group'], drop_first=True, dtype=float)
+    try:
+        data = pd.DataFrame({
+            'amount': [amount],
+            'country': [country],
+            'customer_age': [customer_age],
+            'card_type': [card_type],
+            'purchase_category': [purchase_category]
+        })
+        data = pd.get_dummies(data, columns=['card_type', 'purchase_category'])
+        data['country'] = data['country'].map(fraud_mean_by_country_dict)
+        data['Age_group'] = data['customer_age'].apply(lambda x: group(x))
+        data = pd.get_dummies(data, columns=['Age_group'], drop_first=True, dtype=float)
 
-    columns_to_add = ['customer_age', 'card_type_Discover', 'card_type_Visa', 'purchase_category_Groceries', 'purchase_category_Restaurant','purchase_category_Retail', 'purchase_category_Travel', 'Age_group_33-60', 'Age_group_60+']
+        columns_to_add = ['customer_age','card_type_MasterCard','purchase_category_Online Shopping' ,'card_type_Discover', 'card_type_Visa', 'purchase_category_Groceries', 'purchase_category_Restaurant','purchase_category_Retail', 'purchase_category_Travel', 'Age_group_33-60', 'Age_group_60+']
 
-    for column in columns_to_add:
-        if column not in data.columns:
-            data[column] = 0
+        # Add missing columns with default value of 0
+        for column in columns_to_add:
+            if column not in data.columns:
+                data[column] = 0
 
-    for i in data.columns:
-        data[i] = data[i].astype(int)
+        for i in data.columns:
+          data[i] = data[i].astype(int)
 
-    # Perform PCA
-    X_pca = pca.transform(data[['purchase_category_Groceries', 'purchase_category_Retail', 'purchase_category_Travel', 'Age_group_33-60', 'Age_group_60+']])
-    data['pca1'] = X_pca[:, 0]
-    data['pca2'] = X_pca[:, 1]
+        # Perform PCA
+        X_pca = pca.transform(data[['purchase_category_Groceries', 'purchase_category_Retail', 'purchase_category_Travel', 'Age_group_33-60', 'Age_group_60+']])
+        data['pca1'] = X_pca[:, 0]
+        data['pca2'] = X_pca[:, 1]
 
-    data.drop(columns=['customer_age', 'card_type_Discover', 'card_type_Visa', 'purchase_category_Groceries', 'purchase_category_Retail', 'purchase_category_Travel', 'Age_group_33-60', 'Age_group_60+'], inplace=True)
-
-    # Make predictions
-    prediction = clf.predict(data)
-    return "Fraudulent" if prediction[0] == 1 else "Not Fraudulent"
+        data.drop(columns=['customer_age', 'card_type_Discover', 'card_type_Visa', 'purchase_category_Groceries', 'purchase_category_Retail', 'purchase_category_Travel', 'Age_group_33-60', 'Age_group_60+'], inplace=True)
+        # Make predictions
+        prediction = clf.predict(data)
+        return "Fraudulent" if prediction[0] == 1 else "Not Fraudulent"
+    except Exception as e:
+        return str(e)
 
 # Define Gradio interface
 inputs = [
@@ -79,5 +77,5 @@ inputs = [
     gr.Dropdown(choices=country_choices, label="Country")
 ]
 
-# Define Gradio interfacefg
+# Define Gradio interface
 gr.Interface(fn=predict, inputs=inputs, outputs="text").launch()