app.py

# app.py
import streamlit as st
from models.fraud_detection_model import load_fraud_detection_model, predict_fraud
from utils.preprocessing import preprocess_data_for_streamlit
from utils import feature_engineering
import pandas as pd
import tensorflow

def load_parquet_file(parquet_file_path):
    return pd.read_parquet(parquet_file_path)


model_path = 'models/fraud_detection_model.h5'
fraud_model = load_fraud_detection_model(model_path)

from datasets import load_dataset
dataset = load_dataset("iix/Parquet_FIles/Fraud_detection.parquet")
# Load data
#data_path = 'data/dataset.csv'
df, X_scaled = preprocess_data_for_streamlit(dataset)

# Streamlit App
st.title('Fraud Detection Web App')

# Sidebar with user input
selected_index = st.sidebar.selectbox('Select an index:', df.index)
selected_data = X_scaled[selected_index].reshape(1, -1)

# Display selected data
st.write('Selected Data:')
st.write(df.iloc[selected_index])

# Predict fraud
if st.button('Predict Fraud'):
    prediction = predict_fraud(fraud_model, selected_data)
    result = "Fraud" if prediction[0][0] == 1 else "Non-Fraud"
    st.write(f'Prediction: {result}')