Update app.py

app.py

@@ -1,32 +1,18 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-import pickle
-from sklearn.preprocessing import StandardScaler
-from sklearn.ensemble import RandomForestClassifier
-import matplotlib.pyplot as plt
-from PIL import Image
-
-# Load pre-trained model and scaler
-with open('standard_scaler.pkl', 'rb') as scaler_file:
-    scaler = pickle.load(scaler_file)
-
-with open('random_forest_model.pkl', 'rb') as model_file:
-    model = pickle.load(model_file)
-
 def predict_fraud(user_input):
     user_input_amount = user_input['Amount']  
-    user_input_features = user_input.drop(columns=['Amount'])  
+    user_input_time = user_input['Time']  
+    user_input_features = user_input.drop(columns=['Amount', 'Time'])  
 
-    # Scale the amount column
+    # 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 with other features
-    user_input_scaled = np.concatenate((user_input_features, user_input_amount_scaled), axis=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:
@@ -37,27 +23,6 @@ def predict_fraud(user_input):
     probability = model.predict_proba(user_input_scaled)[0][1]
     return prediction, probability
 
-# Function to generate charts
-def generate_charts(prediction, probability, amount):
-    fig, axes = plt.subplots(1, 2, figsize=(12, 6))
-
-    # Pie chart for prediction distribution
-    prediction_labels = ['Non-Fraudulent', 'Fraudulent']
-    prediction_values = [1 - prediction, prediction]
-    axes[0].pie(prediction_values, labels=prediction_labels, autopct='%1.1f%%', startangle=90, colors=['skyblue', 'lightcoral'])
-    axes[0].set_title('Prediction Distribution')
-
-    # Bar chart for probability distribution
-    axes[1].bar(['Probability'], [probability], color='lightgreen' if prediction == 0 else 'lightcoral')
-    axes[1].set_title('Probability of Fraud')
-
-    # Display amount
-    st.write(f"Transaction Amount: ${amount}")
-
-    # Display charts
-    st.pyplot(fig)
-
-
 # Streamlit app
 def main():
     # Set page layout
@@ -77,15 +42,20 @@ def main():
     max_amount = st.sidebar.number_input('Maximum Amount', value=10000.0, step=0.01)
     amount = st.sidebar.number_input('Amount', min_value=min_amount, max_value=max_amount, value=(min_amount + max_amount) / 2, step=0.01)
 
+    min_time = st.sidebar.number_input('Minimum Time', value=0.0, step=1)
+    max_time = st.sidebar.number_input('Maximum Time', value=172800.0, step=1)
+    time = st.sidebar.number_input('Time', min_value=min_time, max_value=max_time, value=(min_time + max_time) / 2, step=1)
+
     # Descriptions and explanations
     st.markdown("# Fraud Detection Prediction App")
     st.markdown("This app predicts the likelihood of a transaction being fraudulent based on input features.")
-    st.markdown("The input features include V1 to V28, representing anonymized numerical features, and the transaction amount.")
+    st.markdown("The input features include V1 to V28, representing anonymized numerical features, and the transaction amount and time.")
 
     # Make prediction on button click
     if st.sidebar.button('Predict'):
         user_input_df = pd.DataFrame([user_input])  # Convert list of dictionaries to DataFrame
         user_input_df['Amount'] = amount
+        user_input_df['Time'] = time
         prediction, probability = predict_fraud(user_input_df)
 
         # Display prediction result
@@ -95,6 +65,3 @@ def main():
 
         # Generate, display, and save charts
         generate_charts(prediction, probability, amount)
-
-if __name__ == "__main__":
-    main()