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0e9a35a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | import streamlit as st
import pandas as pd
import joblib
# heading
html_temp = """
<div style="background-color:black;padding:10px">
<h2 style="color:white;text-align:center;">Fraud Detection APP </h2>
</div>
"""
st.markdown(html_temp, unsafe_allow_html=True)
# image
url="https://tse2.mm.bing.net/th?id=OIP.ROc4vnkJBbKTf8uWRQpldAHaDt&pid=Api&P=0&h=180"
st.image(url, use_container_width=True)
@st.cache_data
def convert_df(df):
return df.to_csv(index=False).encode("utf-8")
# loading model
model=joblib.load('iso_fraude_dection.joblib')
# Dataset preiction
def Dataset_prediction():
# Required column in dataframe
req_col= pd.DataFrame(columns=['step', 'type', 'amount'])
# Download the template
csv = convert_df(req_col)
st.download_button(
label="Download Template",
data=csv,
file_name="Template.csv",
mime="text/csv")
# uploading model
file=st.file_uploader('Please Upload the CSV File', type=["csv"])
col1, col2 = st.columns(2)
if file is not None:
with col1:
df = pd.read_csv(file,encoding='ISO-8859-1')
st.write("Uploaded File Preview:")
st.dataframe(df.head())
if st.button("Predict Outliers"):
try:
# Ensure required columns exist
required_columns = req_col
if not all(col in df.columns for col in required_columns):
st.error("Uploaded file does not match the required template structure.")
else:
predictions = model.predict(df)
with col2:
df['Anomaly'] = ['Anomaly' if pred == -1 else 'Not Anomaly' for pred in predictions]
st.write("Anomaly Detection Results:")
st.dataframe(df.head())
result_csv = convert_df(df)
st.download_button(
label="Download Results",
data=result_csv,
file_name="Anomaly_Detection_Results.csv",
mime="text/csv")
except Exception as e:
st.error(f"An error occurred while processing the file: {e}")
# value prediction
def values_prediction():
step=st.slider("Slide the Step Value:",min_value=1,max_value=743,value=1)
amount=st.slider('Slide the amount Value:',min_value=1,max_value=92445516,value=1)
option_type=['CASH_IN','CASH_OUT','DEBIT','PAYMENT','TRANSFER']
type=st.selectbox("Select the type of Transaction:",options=option_type)
type_value=option_type.index(type)
if st.button('Submit'):
output=model.predict([[step,type_value,amount]])
if output == -1:
st.write('Its not a Anomaly')
else:
st.write("Its a Anomaly")
# main
st.sidebar.title("Select your Choice ")
file_type = st.sidebar.radio("Choose your BOT", ("Dataset Prediction", "Values Prediction"))
# if st.sidebar.button("submit"):
if file_type =="Dataset Prediction":
Dataset_prediction()
elif file_type== "Values Prediction":
values_prediction()
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