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import streamlit as st
import pandas as pd
import pickle
from sklearn.ensemble import IsolationForest # Example model, replace if using a different one
st.markdown("<h1 style='text-align: center; font-size: 48px; color: red;'>Credit Card Fraud Anomaly Detection ML App</h1>", unsafe_allow_html=True)
# Load the credit card dataset directly
@st.cache_data
def load_dataset():
return pd.read_csv('creditcard.csv') # Path to the creditcard.csv file
# Cache function to convert DataFrame to CSV
@st.cache_data
def convert_df(df):
return df.to_csv(index=False).encode("utf-8")
# Load pre-trained anomaly detection model
@st.cache_resource
def load_model():
with open("model.pkl", "rb") as f:
model = pickle.load(f)
return model
# Load the dataset
df = load_dataset()
# Display the dataset preview
st.write("Dataset Preview:")
st.dataframe(df.head())
# Ensure the dataset has the 'Class' column
if 'Class' not in df.columns:
st.error("The dataset must contain a 'Class' column.")
else:
# Add functionality for row selection
st.markdown("### Select a Row for Model Input:")
# Option to select a row
selected_row = st.selectbox("Select a Row", options=range(len(df)), index=0)
# Add a button below the row selection for anomaly detection
detect_button = st.button("Detect Anomaly")
# If the "Detect" button is clicked
if detect_button:
# Row to use for the model
row_to_use = df.iloc[selected_row]
# Drop 'Class' column (exclude it from features used for prediction)
row_to_use_for_model = row_to_use.drop('Class', errors='ignore')
# Check if the number of features matches the model's expectations
model = load_model()
if len(row_to_use_for_model) != model.n_features_in_:
st.error(f"The model expects {model.n_features_in_} features, but {len(row_to_use_for_model)} were provided.")
else:
# Apply the model for anomaly detection
prediction = model.predict([row_to_use_for_model])
# Display the row and the anomaly detection result
st.write(f"Row selected for anomaly detection:")
st.write(row_to_use)
# Show the anomaly result for the selected row
result = "Anomaly" if prediction[0] == -1 else "Not Anomaly"
st.write(f"Anomaly Detection Result: {result}")
# Provide option to download the result
result_df = row_to_use.to_frame().T # Convert Series to DataFrame
result_df['Anomaly'] = result
result_csv = convert_df(result_df)
st.download_button(
label="Download Selected Row Result",
data=result_csv,
file_name="Selected_Row_Anomaly_Result.csv",
mime="text/csv",
)
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