Update app.py

app.py

@@ -1,3 +1,21 @@
+
+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
+
+
+# Load pre-trained model and scaler
+with open('scalers.pkl', 'rb') as scaler_file:
+    scaler = pickle.load(scaler_file)
+
+with open('rf_Random_model.pkl', 'rb') as model_file:
+    model = pickle.load(model_file)
+
+
 def predict_fraud(user_input):
     user_input_amount = user_input['Amount']  
     user_input_time = user_input['Time']  
@@ -23,6 +41,27 @@ 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