Update app.py

app.py

@@ -6,7 +6,7 @@ import pickle
 from sklearn.preprocessing import StandardScaler
 from sklearn.ensemble import RandomForestClassifier
 import matplotlib.pyplot as plt
-import PIL as Image
+from PIL import Image as PILImage  # Renamed PIL to avoid conflict with Streamlit's Image
 
 # Load pre-trained model and scaler
 with open('scalers.pkl', 'rb') as scaler_file:
@@ -17,21 +17,21 @@ with open('rf_random_model.pkl', 'rb') as model_file:
 
 
 def predict_fraud(user_input):
-    user_input_amount = user_input['Amount']  
-    user_input_time = user_input['Time']  
-    user_input_features = user_input.drop(columns=['Amount', 'Time'])  
+    user_input_amount = user_input['Amount']
+    user_input_time = user_input['Time']
+    user_input_features = user_input.drop(columns=['Amount', 'Time'])
 
     # Scale the amount and time columns
     user_input_amount_scaled = scaler.transform(np.array(user_input_amount).reshape(-1, 1))
     user_input_time_scaled = scaler.transform(np.array(user_input_time).reshape(-1, 1))
-    
+
     # Reshape user_input_features if necessary
     if len(user_input_features.shape) == 1:
         user_input_features = user_input_features.values.reshape(1, -1)
 
     # Combine scaled amount and time with other features
     user_input_scaled = np.concatenate((user_input_features, user_input_amount_scaled, user_input_time_scaled), axis=1)
-    
+
     # Ensure user_input_scaled is a 2D array
     if len(user_input_scaled.shape) == 1:
         user_input_scaled = user_input_scaled.reshape(1, -1)
@@ -42,7 +42,6 @@ def predict_fraud(user_input):
     return prediction, probability
 
 
-# Function to generate charts
 def generate_charts(prediction, probability, amount):
     fig, axes = plt.subplots(1, 2, figsize=(12, 6))
 
@@ -62,7 +61,7 @@ def generate_charts(prediction, probability, amount):
     # Display charts
     st.pyplot(fig)
 
-# Streamlit app
+
 def main():
     # Set page layout
     st.set_page_config(
@@ -71,6 +70,12 @@ def main():
         initial_sidebar_state="collapsed"
     )
 
+    # Load the image
+    image = PILImage.open("fraud_detection_image.jpg")
+
+    # Display the image
+    st.image(image, use_column_width=True)
+
     # Sidebar - User input fields
     st.sidebar.title("Input Parameters")
     user_input = {}
@@ -104,3 +109,7 @@ def main():
 
         # Generate, display, and save charts
         generate_charts(prediction, probability, amount)
+
+
+if __name__ == "__main__":
+    main()