Update app.py

app.py

@@ -1,181 +1,110 @@
-# import streamlit as st
-# import pandas as pd
-# import numpy as np
-# import joblib
-# from sklearn.preprocessing import LabelEncoder
-
-# class FraudDetectionApp:
-#     def __init__(self):
-#         self.model = joblib.load('model/only_model.joblib')
-#         self.categorical_columns = ['incident_severity', 'insured_hobbies', 'insured_education_level', 'incident_city']
-#         self.encoders = {col: LabelEncoder() for col in self.categorical_columns}
-#         self.fit_encoders()
-
-#     def fit_encoders(self):
-#         # Example unique values for fitting the encoders
-#         example_data = {
-#             'incident_severity': ['Minor Damage', 'Major Damage', 'Total Loss', 'Trivial Damage'],
-#             'insured_hobbies': ['sleeping', 'reading', 'board-games', 'bungie-jumping', 'base-jumping', 'golf', 'camping', 'dancing', 'skydiving', 'movies', 'hiking', 'yachting', 'paintball', 'chess', 'kayaking', 'polo', 'basketball', 'video-games', 'cross-fit', 'exercise'],
-#             'insured_education_level': ['MD', 'PhD', 'Associate', 'Masters', 'High School', 'College', 'JD'],
-#             'incident_city': ['Columbus', 'Riverwood', 'Arlington', 'Springfield', 'Hillsdale', 'Northbend', 'Northbrook']
-#         }
-#         for col in self.categorical_columns:
-#             self.encoders[col].fit(example_data[col])
-
-#     def preprocess_single_data(self, data):
-#         if not isinstance(data, pd.DataFrame):
-#             data = pd.DataFrame(data, index=[0])
-#         for col in self.categorical_columns:
-#             if col in data.columns:
-#                 data[col] = self.encoders[col].transform(data[col])
-#         return data
-
-#     def predict_single_fraud(self, data):
-#         data_processed = self.preprocess_single_data(data)
-#         prediction = self.model.predict(data_processed)[0]
-#         return prediction
-
-#     def run(self):
-#         st.title('Fraud Detection Prediction')
-
-#         # Input fields
-#         incident_severity = st.selectbox('Incident Severity', ['Minor Damage', 'Major Damage', 'Total Loss', 'Trivial Damage'])
-#         insured_hobbies = st.selectbox('Insured Hobbies', ['sleeping', 'reading', 'board-games', 'bungie-jumping',
-#                'base-jumping', 'golf', 'camping', 'dancing', 'skydiving',
-#                'movies', 'hiking', 'yachting', 'paintball', 'chess', 'kayaking',
-#                'polo', 'basketball', 'video-games', 'cross-fit', 'exercise'])
-#         total_claim_amount = st.number_input('Total Claim Amount')
-#         months_as_customer = st.number_input('Months as Customer')
-#         policy_annual_premium = st.number_input('Policy Annual Premium')
-#         incident_date = st.number_input('Incident Date', min_value=1, max_value=31, step=1)
-#         capital_loss = st.number_input('Capital Loss')
-#         capital_gains = st.number_input('Capital Gains')
-#         insured_education_level = st.selectbox('Insured Education Level', ['MD', 'PhD', 'Associate', 'Masters', 'High School', 'College', 'JD'])
-#         incident_city = st.selectbox('Incident City', ['Columbus', 'Riverwood', 'Arlington', 'Springfield', 'Hillsdale', 'Northbend', 'Northbrook'])
-
-#         # Collecting user input
-#         new_data_point = {
-#             'incident_severity': incident_severity,
-#             'insured_hobbies': insured_hobbies,
-#             'total_claim_amount': total_claim_amount,
-#             'months_as_customer': months_as_customer,
-#             'policy_annual_premium': policy_annual_premium,
-#             'incident_date': incident_date,
-#             'capital-loss': capital_loss,
-#             'capital-gains': capital_gains,
-#             'insured_education_level': insured_education_level,
-#             'incident_city': incident_city,
-#         }
-
-#         # Prediction button
-#         if st.button('Predict'):
-#             prediction = self.predict_single_fraud(new_data_point)
-#             if prediction == 0:
-#                 st.write('The applied application is not fraud.')
-#             else:
-#                 st.write('The applied application is fraud.')
-
-# if __name__ == '__main__':
-#     app = FraudDetectionApp()
-#     app.run()
-import streamlit as st
-import pandas as pd
-import numpy as np
-import joblib
-from sklearn.preprocessing import LabelEncoder
-
-class FraudDetectionApp:
-    def __init__(self):
-        self.model = joblib.load('model/only_model.joblib')
-        self.categorical_columns = ['incident_severity', 'insured_hobbies', 'insured_education_level', 'incident_city']
-        self.encoders = {col: LabelEncoder() for col in self.categorical_columns}
-        self.fit_encoders()
-
-    def fit_encoders(self):
-        # Example unique values for fitting the encoders
-        example_data = {
-            'incident_severity': ['Minor Damage', 'Major Damage', 'Total Loss', 'Trivial Damage'],
-            'insured_hobbies': ['sleeping', 'reading', 'board-games', 'bungie-jumping', 'base-jumping', 'golf', 'camping', 'dancing', 'skydiving', 'movies', 'hiking', 'yachting', 'paintball', 'chess', 'kayaking', 'polo', 'basketball', 'video-games', 'cross-fit', 'exercise'],
-            'insured_education_level': ['MD', 'PhD', 'Associate', 'Masters', 'High School', 'College', 'JD'],
-            'incident_city': ['Columbus', 'Riverwood', 'Arlington', 'Springfield', 'Hillsdale', 'Northbend', 'Northbrook']
-        }
-        for col in self.categorical_columns:
-            self.encoders[col].fit(example_data[col])
-
-    def preprocess_single_data(self, data):
-        if not isinstance(data, pd.DataFrame):
-            data = pd.DataFrame(data, index=[0])
-        for col in self.categorical_columns:
-            if col in data.columns:
-                data[col] = self.encoders[col].transform(data[col])
-        return data
-
-    def predict_single_fraud(self, data):
-        data_processed = self.preprocess_single_data(data)
-        prediction = self.model.predict(data_processed)[0]
-        return prediction
-
-    def run(self):
-        st.title('Insurance Fraud Prediction')
-
-        # Input fields
-        incident_severity = st.selectbox('Incident Severity', ['Minor Damage', 'Major Damage', 'Total Loss', 'Trivial Damage'])
-        insured_hobbies = st.selectbox('Insured Hobbies', ['sleeping', 'reading', 'board-games', 'bungie-jumping', 'base-jumping', 'golf', 'camping', 'dancing', 'skydiving', 'movies', 'hiking', 'yachting', 'paintball', 'chess', 'kayaking', 'polo', 'basketball', 'video-games', 'cross-fit', 'exercise'])
-        total_claim_amount = st.number_input('Total Claim Amount')
-        months_as_customer = st.number_input('Months as Customer')
-        policy_annual_premium = st.number_input('Policy Annual Premium')
-        incident_date = st.number_input('Incident Date', min_value=1, max_value=31, step=1)
-        capital_loss = st.number_input('Capital Loss')
-        capital_gains = st.number_input('Capital Gains')
-        insured_education_level = st.selectbox('Insured Education Level', ['MD', 'PhD', 'Associate', 'Masters', 'High School', 'College', 'JD'])
-        incident_city = st.selectbox('Incident City', ['Columbus', 'Riverwood', 'Arlington', 'Springfield', 'Hillsdale', 'Northbend', 'Northbrook'])
-
-        # Collecting user input
-        new_data_point = {
-            'incident_severity': incident_severity,
-            'insured_hobbies': insured_hobbies,
-            'total_claim_amount': total_claim_amount,
-            'months_as_customer': months_as_customer,
-            'policy_annual_premium': policy_annual_premium,
-            'incident_date': incident_date,
-            'capital-loss': capital_loss,
-            'capital-gains': capital_gains,
-            'insured_education_level': insured_education_level,
-            'incident_city': incident_city,
-        }
-
-        # Prediction button
-        if st.button('Predict'):
-            prediction = self.predict_single_fraud(new_data_point)
-            if prediction == 0:
-                st.write('The applied application is not fraud.')
-            else:
-                st.write('The applied application is fraud.')
-
-        # Generate sample data
-        if st.button('Generate Sample Data'):
-            sample_non_fraud = self.generate_sample_data(fraud=False)
-            sample_fraud = self.generate_sample_data(fraud=True)
-            st.write("Non-Fraud Sample Data:")
-            st.write(sample_non_fraud)
-            st.write("Fraud Sample Data:")
-            st.write(sample_fraud)
-
-    def generate_sample_data(self, fraud=False):
-        sample_data = {
-            'incident_severity': ['Major Damage' if fraud else 'Minor Damage'],
-            'insured_hobbies': ['skydiving' if fraud else 'reading'],
-            'total_claim_amount': [50000 if fraud else 1000],
-            'months_as_customer': [1 if fraud else 60],
-            'policy_annual_premium': [10000 if fraud else 200],
-            'incident_date': [15],
-            'capital-loss': [1000 if fraud else 0],
-            'capital-gains': [5000 if fraud else 0],
-            'insured_education_level': ['PhD' if fraud else 'College'],
-            'incident_city': ['Riverwood' if fraud else 'Northbrook']
-        }
-        return pd.DataFrame(sample_data)
-
-if __name__ == '__main__':
-    app = FraudDetectionApp()
-    app.run()
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+from sklearn.preprocessing import LabelEncoder
+
+class FraudDetectionApp:
+    def __init__(self):
+        self.model = joblib.load('model/only_model.joblib')
+        
+        # Assuming the model has an attribute 'feature_names_in_' which stores the feature names used during training
+        self.feature_names = self.model.feature_names_in_ if hasattr(self.model, 'feature_names_in_') else [
+            'incident_severity', 'insured_hobbies', 'total_claim_amount', 'months_as_customer', 'policy_annual_premium', 
+            'incident_date', 'capital-loss', 'capital-gains', 'insured_education_level', 'incident_city'
+        ]
+        
+        self.categorical_columns = ['incident_severity', 'insured_hobbies', 'insured_education_level', 'incident_city']
+        self.encoders = {col: LabelEncoder() for col in self.categorical_columns}
+        self.fit_encoders()
+
+    def fit_encoders(self):
+        # Example unique values for fitting the encoders
+        example_data = {
+            'incident_severity': ['Minor Damage', 'Major Damage', 'Total Loss', 'Trivial Damage'],
+            'insured_hobbies': ['sleeping', 'reading', 'board-games', 'bungie-jumping', 'base-jumping', 'golf', 'camping', 'dancing', 'skydiving', 'movies', 'hiking', 'yachting', 'paintball', 'chess', 'kayaking', 'polo', 'basketball', 'video-games', 'cross-fit', 'exercise'],
+            'insured_education_level': ['MD', 'PhD', 'Associate', 'Masters', 'High School', 'College', 'JD'],
+            'incident_city': ['Columbus', 'Riverwood', 'Arlington', 'Springfield', 'Hillsdale', 'Northbend', 'Northbrook']
+        }
+        for col in self.categorical_columns:
+            self.encoders[col].fit(example_data[col])
+
+    def preprocess_single_data(self, data):
+        if not isinstance(data, pd.DataFrame):
+            data = pd.DataFrame(data, index=[0])
+        for col in self.categorical_columns:
+            if col in data.columns:
+                data[col] = self.encoders[col].transform(data[col])
+        # Ensure the column order matches the training data
+        data = data[self.feature_names]
+        return data
+
+    def predict_single_fraud(self, data):
+        data_processed = self.preprocess_single_data(data)
+        prediction = self.model.predict(data_processed)[0]
+        return prediction
+
+    def run(self):
+        st.title('Insurance Fraud Prediction')
+
+        # Input fields
+        incident_severity = st.selectbox('Incident Severity', ['Minor Damage', 'Major Damage', 'Total Loss', 'Trivial Damage'])
+        insured_hobbies = st.selectbox('Insured Hobbies', ['sleeping', 'reading', 'board-games', 'bungie-jumping', 'base-jumping', 'golf', 'camping', 'dancing', 'skydiving', 'movies', 'hiking', 'yachting', 'paintball', 'chess', 'kayaking', 'polo', 'basketball', 'video-games', 'cross-fit', 'exercise'])
+        total_claim_amount = st.number_input('Total Claim Amount')
+        months_as_customer = st.number_input('Months as Customer')
+        policy_annual_premium = st.number_input('Policy Annual Premium')
+        incident_date = st.number_input('Incident Date', min_value=1, max_value=31, step=1)
+        capital_loss = st.number_input('Capital Loss')
+        capital_gains = st.number_input('Capital Gains')
+        insured_education_level = st.selectbox('Insured Education Level', ['MD', 'PhD', 'Associate', 'Masters', 'High School', 'College', 'JD'])
+        incident_city = st.selectbox('Incident City', ['Columbus', 'Riverwood', 'Arlington', 'Springfield', 'Hillsdale', 'Northbend', 'Northbrook'])
+
+        # Collecting user input
+        new_data_point = {
+            'incident_severity': incident_severity,
+            'insured_hobbies': insured_hobbies,
+            'total_claim_amount': total_claim_amount,
+            'months_as_customer': months_as_customer,
+            'policy_annual_premium': policy_annual_premium,
+            'incident_date': incident_date,
+            'capital-loss': capital_loss,
+            'capital-gains': capital_gains,
+            'insured_education_level': insured_education_level,
+            'incident_city': incident_city,
+        }
+
+        # Prediction button
+        if st.button('Predict'):
+            prediction = self.predict_single_fraud(new_data_point)
+            if prediction == 0:
+                st.write('The applied application is not fraud.')
+            else:
+                st.write('The applied application is fraud.')
+
+        # Generate sample data
+        if st.button('Generate Sample Data'):
+            sample_non_fraud = self.generate_sample_data(fraud=False)
+            sample_fraud = self.generate_sample_data(fraud=True)
+            st.write("Non-Fraud Sample Data:")
+            st.write(sample_non_fraud)
+            st.write("Fraud Sample Data:")
+            st.write(sample_fraud)
+
+    def generate_sample_data(self, fraud=False):
+        sample_data = {
+            'incident_severity': ['Major Damage' if fraud else 'Minor Damage'],
+            'insured_hobbies': ['skydiving' if fraud else 'reading'],
+            'total_claim_amount': [50000 if fraud else 1000],
+            'months_as_customer': [1 if fraud else 60],
+            'policy_annual_premium': [10000 if fraud else 200],
+            'incident_date': [15],
+            'capital-loss': [1000 if fraud else 0],
+            'capital-gains': [5000 if fraud else 0],
+            'insured_education_level': ['PhD' if fraud else 'College'],
+            'incident_city': ['Riverwood' if fraud else 'Northbrook']
+        }
+        return pd.DataFrame(sample_data)
+
+if __name__ == '__main__':
+    app = FraudDetectionApp()
+    app.run()