first

app.py

@@ -0,0 +1,108 @@
+import datetime
+import joblib
+from math import radians
+from sml import cc_features
+
+import pandas as pd
+import numpy as np
+import plotly.express as px
+from matplotlib import pyplot
+import warnings
+
+import hopsworks
+from sml import synthetic_data
+
+import streamlit as st
+
+import folium
+from streamlit_folium import st_folium
+import json
+
+start_date = (datetime.datetime.now() - datetime.timedelta(hours=200)) 
+end_date = (datetime.datetime.now()) 
+
+synthetic_data.set_random_seed(12345)
+credit_cards = [cc["cc_num"] for cc in synthetic_data.generate_list_credit_card_numbers()]
+lat = 0
+long = 0
+
+warnings.filterwarnings("ignore")
+
+project = hopsworks.login()
+fs = project.get_feature_store()
+
+@st.cache(allow_output_mutation=True, suppress_st_warning=True)
+def retrieve_dataset(fv, start_date, end_date):
+    st.write(36 * "-")
+    print_fancy_header('\n💾 Dataset Retrieving...')
+    batch_data = fv.get_batch_data(start_time = start_date, end_time = end_date)
+    batch_data.drop(["tid", "cc_num", "datetime"], axis = 1, inplace=True)
+    return batch_data
+
+
+@st.cache(suppress_st_warning=True, allow_output_mutation=True)
+def get_feature_view():
+    fv = fs.get_feature_view("cc_trans_fraud", 1)
+    return fv
+
+
+@st.cache(allow_output_mutation=True,suppress_st_warning=True)
+def get_model(project = project):
+    mr = project.get_model_registry()
+    model = mr.get_model("cc_fraud", version = 1)
+    model_dir = model.download()
+    return joblib.load(model_dir + "/cc_fraud_model.pkl")
+
+def explore_data(batch_data):
+    st.write(36 * "-")
+    print_fancy_header('\n👁 Data Exploration...')
+    labels = ["Suspected of Fraud", "Not Suspected of Fraud"]
+    unique, counts = np.unique(batch_data.fraud.values, return_counts=True)
+    values = counts.tolist()
+
+    def plot_pie(values, labels):
+        fig = px.pie(values=values, names=labels, title='Distribution of predicted fraud transactions')
+        return fig
+
+    fig1 = plot_pie(values, labels)
+    st.plotly_chart(fig1)
+
+
+def print_fancy_header(text, font_size=24):
+    res = f'<span style="color:#ff5f27; font-size: {font_size}px;">{text}</span>'
+    st.markdown(res, unsafe_allow_html=True)
+
+def transform_preds(predictions):
+    return ['Fraud' if pred == 1 else 'Not Fraud' for pred in predictions]    
+
+progress_bar = st.sidebar.header('⚙️ Working Progress')
+progress_bar = st.sidebar.progress(0)
+st.title('🆘 Fraud transactions detection 🆘')
+
+st.write(36 * "-")
+print_fancy_header('\n📡 Connecting to Hopsworks Feature Store...')
+
+st.write(36 * "-")
+print_fancy_header('\n🤖 Connecting to Model Registry on Hopsworks...')
+model = get_model(project)
+st.write(model)
+st.write("✅ Connected!")
+
+progress_bar.progress(40)
+
+st.write(36 * "-")
+print_fancy_header('\n✨ Fetch batch data and predict')
+fv = get_feature_view()
+
+
+if st.button('📊 Make a prediction'):
+    batch_data = retrieve_dataset(fv, start_date, end_date)
+    st.write("✅ Retrieved!")
+    progress_bar.progress(55)
+    predictions = model.predict(batch_data)
+    predictions = transform_preds(predictions)
+    batch_data_to_explore = batch_data.copy()
+    batch_data_to_explore['fraud'] = predictions
+    explore_data(batch_data_to_explore)
+
+st.button("Re-run")

requirements.txt

@@ -0,0 +1,4 @@
+hopsworks
+joblib
+scikit-learn
+streamlit