app.py

import streamlit as st 
import numpy as np 
import pandas as pd 
import pickle 
from PIL import Image 

image = Image.open('pic2.jpg')
st.image(image,caption = 'Network Data Anomaly',width =1000)

st.title("Network Data Anomaly")
st.write("""An anomaly (also known as an outlier) is when something happens that is outside of the norm, 
when it stands out or deviates from what is expected. There are different kinds of anomalies in an e-commerce setting, 
they can be product anomaly, conversion anomaly or marketing anomaly.
The model used is Isolation Forest, which is built based on decision trees and is an unsupervised model.
Isolation forests can be used to detect anomaly in high dimensional and large datasets, with no labels.
""")

with open("./median.pickle", 'rb') as f:
    MED = pickle.load(f)
with open("./mad.pickle", 'rb') as g:
    MA = pickle.load(g)

def ZRscore_outlier(packet,med,ma):
    z = (0.6745*(packet-med))/ (np.median(ma))
    if np.abs(z) > 3: 
        return "Outlier"
    else:
        return "Not an Outlier"

packet = st.number_input("Packet Number",step=1)
st.header(ZRscore_outlier(packet,MED,MA))

st.write("""
For a detailed description please look through our Documentation 
""")

url = 'https://huggingface.co/spaces/ThirdEyeData/Retail-Anomaly/blob/main/README.md'

st.markdown(f'''
<a href={url}><button style="background-color: #668F45;">Documentation</button></a>
''',
unsafe_allow_html=True)