Create app.py

app.py

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+import streamlit as st
+import numpy as np
+import joblib
+from sklearn.preprocessing import StandardScaler
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Load the non-anomaly data
+non_anomaly_csv_filename = 'non_anomaly_data.csv'
+non_anomaly_df = pd.read_csv(non_anomaly_csv_filename)
+
+# Open the Mitos Spreadsheet file
+#st.write("Opening Mitos Spreadsheet file...")
+#st.csv_open("non_anomaly_data.csv")
+
+# Display the first sheet
+#st.write(st.get_active_sheet().name)
+
+# Display the first row of the first sheet
+#st.write(st.get_active_sheet().rows[0])
+
+# Load the Isolation Forest model
+model_filename = "IsolationForest.joblib"
+isolation_forest = joblib.load(model_filename)
+
+# Load the StandardScaler
+scaler_filename = "StandardScaler.joblib"
+scaler = joblib.load(scaler_filename)
+
+st.title("Anomaly Detection App with Isolation Forest")
+
+st.sidebar.title("Input Feature Values")
+transaction_dollar_amount = st.sidebar.slider("Transaction Dollar Amount", min_value=0.0, max_value=10000.0)
+longitude = st.sidebar.slider("Longitude (Long)", min_value=-180.0, max_value=180.0)
+latitude = st.sidebar.slider("Latitude (Lat)", min_value=-90.0, max_value=90.0)
+credit_card_limit = st.sidebar.slider("Credit Card Limit", min_value=0, max_value=50000)
+year = st.sidebar.slider("Year", min_value=2000, max_value=2030)
+month = st.sidebar.slider("Month", min_value=1, max_value=12)
+day = st.sidebar.slider("Day", min_value=1, max_value=31)
+
+submitted = st.sidebar.button("Submit")
+
+if submitted:
+    input_data = {
+        'transaction_dollar_amount': transaction_dollar_amount,
+        'Long': longitude,
+        'Lat': latitude,
+        'credit_card_limit': credit_card_limit,
+        'year': year,
+        'month': month,
+        'day': day
+    }
+
+    selected_columns = pd.DataFrame([input_data])
+
+    # Standardize the input data using the loaded StandardScaler
+    selected_columns_scaled = scaler.transform(selected_columns)
+
+    # Apply Isolation Forest for anomaly detection on the non-anomaly dataset
+    non_anomaly_scores = isolation_forest.decision_function(scaler.transform(non_anomaly_df))
+
+# Apply Isolation Forest for anomaly detection on your single input data
+    your_anomaly_score = isolation_forest.decision_function(selected_columns_scaled)[0]
+
+
+
+    # Calculate the minimum and maximum anomaly scores from non-anomaly data
+    min_non_anomaly_score = np.min(non_anomaly_scores)
+    max_non_anomaly_score = np.max(non_anomaly_scores)
+
+# Add a margin of error for the range
+    margin = 0.5
+    min_threshold = min_non_anomaly_score - margin
+    max_threshold = max_non_anomaly_score + margin
+
+    # Determine if the input data point is an anomaly based on the score
+    #is_anomaly = your_anomaly_score >= np.percentile(non_anomaly_scores, 95)
+
+    # Determine if the input data point is an anomaly based on the score
+    is_anomaly = your_anomaly_score < min_threshold or your_anomaly_score > max_threshold
+
+
+# Print the anomaly status
+    st.subheader("Anomaly Classification")
+    if is_anomaly:
+        st.write("Prediction Result: 🚨 Anomaly Detected!")
+    else:
+        st.write("Prediction Result: ✅ Not Anomaly")
+
+# Create a bar plot to visualize the anomaly score distribution and your data point's score
+    plt.figure(figsize=(8, 5))
+
+# Plot the distribution of anomaly scores from the non-anomaly dataset
+    sns.histplot(non_anomaly_scores, kde=True, color='gray', label='Non-Anomaly Score Distribution')
+
+# Plot your data point's anomaly score
+    plt.axvline(x=your_anomaly_score, color='blue', linestyle='dashed', label='Your Data Point')
+
+# Set labels and title
+    plt.xlabel('Anomaly Score')
+    plt.ylabel('Frequency')
+    plt.title('Anomaly Score Distribution and Your Data Point')
+    plt.legend()
+#plt.grid(True)
+
+# Display the histogram plot
+    st.pyplot(plt)
+
+
+# Explain the results
+    st.write("The input data point has been classified as an anomaly." if is_anomaly
+            else "The input data point is not classified as an anomaly.")
+    st.write("The anomaly score is:", your_anomaly_score)
+    st.write("The threshold for anomaly detection is:", min_threshold, "to", max_threshold)
+
+    # Create a scatter plot for longitude and latitude
+    fig, ax = plt.subplots(figsize=(10, 8))
+
+# Plot non-anomaly data
+    sns.scatterplot(data=non_anomaly_df, x='Long', y='Lat', color='lightgrey', label='Normal 🏙️', ax=ax)
+
+# Plot input data
+    if is_anomaly:
+        ax.scatter(selected_columns['Long'], selected_columns['Lat'], color='red', label='Suspicious 🚩', s=100, marker='x')
+        anomaly_marker = 'Suspicious 🚩'
+    else:
+        ax.scatter(selected_columns['Long'], selected_columns['Lat'], color='green', label='Valid ✅', s=100, marker='o')
+        anomaly_marker = 'Valid ✅'
+
+    ax.set_xlabel("Longitude")
+    ax.set_ylabel("Latitude")
+    ax.set_title("Location Plot: Anomaly Detection 🗺️")
+    ax.legend()
+    ax.grid(True)
+
+# Show the scatter plot in Streamlit
+    st.subheader("Location Plot: Anomaly Detection 🗺️")
+    st.pyplot(fig)
+
+# Explanation based on the anomaly classification
+    st.subheader("Anomaly Classification")
+    if your_anomaly_score < min_threshold or your_anomaly_score > max_threshold:
+        st.write("Prediction Result: 🚨 Anomaly Detected!")
+    else:
+        st.write("Prediction Result: ✅ Not Anomaly")
+
+# Explain the results
+    # Explain the results
+    st.write("The location plot visualizes the anomaly detection result based on longitude and latitude.")
+    if your_anomaly_score < min_threshold or your_anomaly_score > max_threshold:
+        st.write("The input data point is marked as Suspicious 🚩 due to its anomaly score.")
+        st.write("The red 'x' marker indicates a suspicious location.")
+    else:
+        st.write("The input data point is marked as Valid ✅ due to its anomaly score.")
+        st.write("The green 'o' marker indicates a valid location.")