dashboard modified

app/__init__.py

@@ -5,7 +5,12 @@
 from app import pages, transactions, data, predict
 from app.models import db
 from app.database import init_db_command
-from app.data import create_dash_app
+from app.dashboard import (
+    create_summary_dash_app,
+    create_fraud_trends_dash_app,
+    create_geo_analysis_dash_app,
+    create_device_browser_dash_app
+)
 
 load_dotenv()
 
@@ -31,10 +36,13 @@ def create_app():
     app.register_blueprint(transactions.bp)
     app.register_blueprint(data.bp)
     app.register_blueprint(predict.bp)
-    #app.register_blueprint(train.bp)
 
-    # Initialize the Dash app, passing the Flask app as the server
-    create_dash_app(app)
+    # Initialize Dash apps and pass the `db` object for querying the database
+    create_summary_dash_app(app, db)
+    create_fraud_trends_dash_app(app, db)
+    create_geo_analysis_dash_app(app, db)
+    create_device_browser_dash_app(app, db)
+
     # Add CLI command
     app.cli.add_command(init_db_command)
 

app/dashboard.py

@@ -0,0 +1,107 @@
+import pandas as pd
+import plotly.express as px
+from dash import Dash, dcc, html, Input, Output
+from sqlalchemy import create_engine, func
+from app.models import Features 
+
+def create_summary_dash_app(flask_app, db):
+    dash_app = Dash(__name__, server=flask_app, url_base_pathname='/summary/')
+
+    with flask_app.app_context():
+        # Query for summary statistics from the 'features' table
+        total_records = db.session.query(func.count(Features.user_id)).scalar()
+        avg_purchase_value = db.session.query(func.avg(Features.purchase_value)).scalar()
+
+    dash_app.layout = html.Div([
+        html.H3('Summary Statistics'),
+        html.P(f"Total Records: {total_records}"),
+        html.P(f"Average Purchase Value: {avg_purchase_value:.2f}")
+    ])
+
+    return dash_app
+
+
+def create_fraud_trends_dash_app(flask_app, db):
+    dash_app = Dash(__name__, server=flask_app, url_base_pathname='/fraud_trends/')
+
+    dash_app.layout = html.Div([
+        html.H3('Fraud Trends Over Time'),
+        dcc.Graph(id='fraud-trends-graph')
+    ])
+
+    @dash_app.callback(
+        Output('fraud-trends-graph', 'figure'),
+        [Input('fraud-trends-graph', 'id')]
+    )
+    def update_fraud_trends(_):
+        with flask_app.app_context():
+            # Query for fraud trends over time
+            fraud_trends = (
+                db.session.query(Features.purchase_time, func.count(Features.user_id).label('fraud_cases'))
+                .filter(Features.class_ == 1)  # Assuming 'class' indicates fraud status
+                .group_by(Features.purchase_time)
+                .all()
+            )
+            fraud_trends_df = pd.DataFrame(fraud_trends, columns=['purchase_time', 'fraud_cases'])
+
+        fig = px.line(fraud_trends_df, x='purchase_time', y='fraud_cases', title='Fraud Cases Over Time')
+        return fig
+
+    return dash_app
+
+
+def create_geo_analysis_dash_app(flask_app, db):
+    dash_app = Dash(__name__, server=flask_app, url_base_pathname='/geo_analysis/')
+
+    dash_app.layout = html.Div([
+        html.H3('Geographical Analysis of Fraud'),
+        dcc.Graph(id='geo-analysis-graph')
+    ])
+
+    @dash_app.callback(
+        Output('geo-analysis-graph', 'figure'),
+        [Input('geo-analysis-graph', 'id')]
+    )
+    def update_geo_analysis(_):
+        with flask_app.app_context():
+            # Query for geographical fraud data
+            geo_data = (
+                db.session.query(Features.country, func.count(Features.ip_address).label('ip_address_count'))
+                .group_by(Features.country)
+                .all()
+            )
+            geo_data_df = pd.DataFrame(geo_data, columns=['country', 'ip_address_count'])
+
+        fig = px.bar(geo_data_df, x='ip_address_count', y='country', title='Fraud Cases by Country')
+        return fig
+
+    return dash_app
+
+
+def create_device_browser_dash_app(flask_app, db):
+    dash_app = Dash(__name__, server=flask_app, url_base_pathname='/device_browser/')
+
+    dash_app.layout = html.Div([
+        html.H3('Device and Browser Analysis'),
+        dcc.Graph(id='device-browser-graph')
+    ])
+
+    @dash_app.callback(
+        Output('device-browser-graph', 'figure'),
+        [Input('device-browser-graph', 'id')]
+    )
+    def update_device_browser_analysis(_):
+        with flask_app.app_context():
+            # Query for device and browser data
+            device_browser_data = (
+                db.session.query(Features.device_id, Features.browser, func.count(Features.class_).label('fraud_cases'))
+                .filter(Features.class_ == 1)  # Assuming 'class' indicates fraud status
+                .group_by(Features.device_id, Features.browser)
+                .all()
+            )
+            device_browser_df = pd.DataFrame(device_browser_data, columns=['device_id', 'browser', 'fraud_cases'])
+
+        fig = px.bar(device_browser_df, x='device_id', y='fraud_cases', color='browser', title='Fraud by Device and Browser')
+        return fig
+
+    return dash_app

app/data.py

@@ -15,6 +15,10 @@
 from mlflow import pyfunc
 import xgboost as xgb
 from dash import Dash, dcc, html, Input, Output
+from flask import Blueprint
+from dash import Dash
+import dash_core_components as dcc
+import dash_html_components as html
 
 load_dotenv()
 
@@ -108,36 +112,3 @@ def result():
     print("Prediction: ", prediction)
 
     return render_template('data/result.html', prediction=prediction, ID=ID)
-
-from flask import Blueprint
-from dash import Dash
-import dash_core_components as dcc
-import dash_html_components as html
-
-# Create a Flask Blueprint
-bp = Blueprint("data", __name__)
-
-# Do not link Dash directly to the Blueprint
-# Instead, define Dash within a function where you have access to the main Flask app
-
-def create_dash_app(flask_app):
-    # Create the Dash app and pass the Flask app as the server
-    dash_app = Dash(__name__, server=flask_app, url_base_pathname='/dash/')
-
-    # Define Dash layout
-    dash_app.layout = html.Div(children=[
-        html.H1('Dash App'),
-        dcc.Graph(
-            id='example-graph',
-            figure={
-                'data': [
-                    {'x': [1, 2, 3], 'y': [4, 1, 2], 'type': 'bar', 'name': 'Sample'}
-                ],
-                'layout': {
-                    'title': 'Sample Data Visualization'
-                }
-            }
-        )
-    ])
-
-    return dash_app

app/models.py

@@ -20,3 +20,20 @@ class Transaction(db.Model):
     upper_bound_ip_address = db.Column(db.Float, nullable=False)
     country = db.Column(db.String, nullable=False)
 
+class Features(db.Model):
+    __tablename__ = 'features'
+
+    user_id = db.Column(db.Integer, primary_key=True)
+    signup_time = db.Column(db.DateTime)
+    purchase_time = db.Column(db.DateTime)
+    purchase_value = db.Column(db.Float)
+    device_id = db.Column(db.String)
+    source = db.Column(db.String)
+    browser = db.Column(db.String)
+    sex = db.Column(db.String)
+    age = db.Column(db.Integer)
+    ip_address = db.Column(db.String)
+    class_ = db.Column(db.Integer)  # Fraud class: 0 = non-fraud, 1 = fraud
+    lower_bound_ip_address = db.Column(db.BigInteger)
+    upper_bound_ip_address = db.Column(db.BigInteger)
+    country = db.Column(db.String)