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app.py

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+import streamlit as st
+import numpy as np
+import tensorflow as tf
+import pandas as pd
+import requests
+from streamlit_lottie import st_lottie
+
+st.title("Automated Fraud Detection System Web app")
+st.write("""
+
+This app will helps us to track what type of transactions lead to fraud. I collected a dataset from [Kaggle repositry](https://www.kaggle.com/datasets/jainilcoder/online-payment-fraud-detection)
+,which contains historical information about fraudulent transactions which can be used to detect fraud in online payments.
+""")
+
+def load_lottieurl(url: str):
+    r = requests.get(url)
+    if r.status_code != 200:
+        return None
+    return r.json()
+
+
+lottie_url = "https://assets8.lottiefiles.com/packages/lf20_yhTqG2.json"
+
+lottie_hello = load_lottieurl(lottie_url)
+
+with st.sidebar:
+    st_lottie(lottie_hello,quality='high')
+
+st.sidebar.title('Users Features Explanation')
+st.sidebar.markdown("**step**: represents a unit of time where 1 step equals 1 hour")
+st.sidebar.markdown("**type**: type of online transaction")
+st.sidebar.markdown('**amount**: the amount of the transaction')
+st.sidebar.markdown('**oldbalanceOrg**: balance before the transaction')
+st.sidebar.markdown('**newbalanceOrig**: balance after the transaction')
+st.sidebar.markdown('**oldbalanceDest**: initial balance of recipient before the transaction')
+st.sidebar.markdown('**newbalanceDest**: the new balance of recipient after the transaction')
+
+
+
+st.header('User Input Features')
+
+def user_input_features():
+    step = st.number_input('Step', 0, 3)
+    type = st.selectbox('Online Transaction Type', ("CASH IN", "CASH OUT", "DEBIT", "PAYMENT", "TRANSFER"))
+    amount = st.number_input("Amount of the transaction")
+    oldbalanceOrg = st.number_input("Old balance Origin")
+    newbalanceOrig = st.number_input("New balance Origin")
+    oldbalanceDest = st.number_input("Old Balance Destination")
+    newbalanceDest = st.number_input("New Balance Destination")
+    data = {'step': step,
+            'type': type,
+            'amount': amount,
+            'oldbalanceOrg': oldbalanceOrg,
+            'newbalanceOrig': newbalanceOrig,
+            'oldbalanceDest': oldbalanceDest,
+            'newbalanceDest': newbalanceDest}
+    features = pd.DataFrame(data, index=[0])
+    return features
+input_df = user_input_features()
+
+# Combines user input features with sample dataset
+# This will be useful for the encoding phase
+fraud_raw = pd.read_csv('samp_online.csv')
+fraud = fraud_raw.drop(columns=['isFraud','nameOrig','nameDest','isFlaggedFraud'])
+df = pd.concat([input_df,fraud],axis=0)
+
+# Encoding of ordinal features
+
+
+encode = ['type']
+for col in encode:
+    dummy = pd.get_dummies(df[col], prefix=col)
+    df = pd.concat([df,dummy], axis=1)
+    del df[col]
+df = df[:1] # Selects only the first row (the user input data)
+
+# Reads in saved classification model
+if st.button("Predict"):
+    load_clf = tf.keras.models.load_model('fraud.h5', compile=False)
+    load_clf.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+
+
+
+    # Apply model to make predictions
+
+    y_probs = load_clf.predict(df)
+    pred = tf.round(y_probs)
+    pred = tf.cast(pred, tf.int32)
+
+    st.markdown(
+        """
+    <style>
+    [data-testid="stMetricValue"] {
+        font-size: 25px;
+    }
+    </style>
+    """,
+        unsafe_allow_html=True,
+    )
+
+    if pred == 0:
+
+        col1, col2 = st.columns(2)
+        col1.metric("Prediction", value="Transaction is not fraudulent ")
+        col2.metric("Confidence Level", value=f"{np.round(np.max(y_probs) * 100)}%")
+    else:
+        col1, col2 = st.columns(2)
+        col1.metric("prediction", value="Transaction is fraudulent")
+        col2.metric("Confidence Level", value=f"{np.round(np.max(y_probs) * 100)}%")
+
+
+
+
+
+
+
+

fraud.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:29da2797ef633f1d26b6f56ebf213e16c47fc8a42db5b4a29d0ea3d88192961a
+size 946904

requirements.txt

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+numpy==1.23.5
+streamlit==1.15.1
+tensorflow_cpu==2.8.0
+pandas==1.5.2
+requests==2.28.1
+streamlit-lottie==0.0.3
+
+
+

samp_online.csv

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+step,type,amount,nameOrig,oldbalanceOrg,newbalanceOrig,nameDest,oldbalanceDest,newbalanceDest,isFraud,isFlaggedFraud
+1,PAYMENT,9839.64,C1231006815,170136,160296.36,M1979787155,0,0,0,0
+1,TRANSFER,181,C1305486145,181,0,C553264065,0,0,1,0
+1,CASH_OUT,181,C840083671,181,0,C38997010,21182,0,1,0
+1,DEBIT,5337.77,C712410124,41720,36382.23,C195600860,41898,40348.79,0,0
+1,CASH_OUT,229133.94,C905080434,15325,0,C476402209,5083,51513.44,0,0