Fixing Dockerfile and adding files

.streamlit/config.toml

@@ -0,0 +1,15 @@
+[server]
+# The port where the server will listen for browser connections.
+port = 8501
+# Enable static file serving (set to true if you want to serve static files).
+enableStaticServing = false
+
+[theme]
+base = "dark"
+primaryColor = "#8D1DB9"
+backgroundColor = "#191414"
+secondaryBackgroundColor = "#282828"
+font = "sans-serif"
+headingFont = "sans-serif"
+codeFont = "monospace"
+textColor = "#ffffff"

Dockerfile

@@ -2,20 +2,25 @@ FROM python:3.9-slim
 
 WORKDIR /app
 
+# Install OS-level dependencies (removed software-properties-common)
 RUN apt-get update && apt-get install -y \
     build-essential \
     curl \
-    software-properties-common \
     git \
-    && rm -rf /var/lib/apt/lists/*
+ && rm -rf /var/lib/apt/lists/*
 
+# Copy dependency list and install
 COPY requirements.txt ./
-COPY src/ ./src/
+RUN pip3 install --no-cache-dir -r requirements.txt
 
-RUN pip3 install -r requirements.txt
+# Copy the rest of your project into the container
+COPY . .
 
+# Expose Streamlit's default port
 EXPOSE 8501
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+# Healthcheck for Spaces
+HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health || exit 1
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# Run your main app file
+ENTRYPOINT ["streamlit", "run", "KPI_Dashboard.py", "--server.port=8501", "--server.address=0.0.0.0"]

KPI_Dashboard.py

@@ -0,0 +1,470 @@
+import streamlit as st
+import pandas as pd
+import plotly.graph_objects as go
+from streamlit_extras.metric_cards import style_metric_cards
+import json
+import warnings
+
+warnings.filterwarnings("ignore")
+
+# -----------------------------
+# Page Configuration
+# -----------------------------
+st.set_page_config(
+    page_title="KPI Dashboard",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+# -----------------------------
+# Inject Custom CSS
+# -----------------------------
+with open("assets/styles.css", encoding="utf-8") as f:
+    st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
+
+# -----------------------------
+# Styling Metric Cards
+# -----------------------------
+style_metric_cards(
+    background_color="#141212",
+    border_color="#8D1DB9",
+    border_size_px=1,
+    border_radius_px=10,
+    border_left_color="#8D1DB9",
+    box_shadow=True
+)
+
+st.markdown("""
+    <style>
+        /* increase the size of the value and the label in the metric cards */
+        [data-testid="stMetricLabel"] p {
+            font-size: 26px !important;
+            font-weight: 300 !important;
+            color: white !important;
+        }
+            
+        [data-testid="stMetricValue"] {
+            font-size: 50px;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+# -----------------------------
+# Header Section
+# -----------------------------
+col_title, col_logo = st.columns([4, 1])
+with col_title: 
+    st.markdown("""
+    <style>
+        .stImage > img {
+            width: 200px; /* Increase size */
+            float: right; /* Align to the right */
+            border-radius: 10px;
+            box-shadow: 5px 5px 10px rgba(0, 0, 0, 0.2);
+            transition: transform 0.3s ease-in-out;
+        }
+    </style>
+    """, unsafe_allow_html=True)
+    st.image("assets/bank-logo.png", width=90)
+
+with col_logo:
+    st.markdown("""
+    <style>
+        .stImage > img {
+            width: 200px; /* Increase size */
+            float: right; /* Align to the right */
+            border-radius: 10px;
+            box-shadow: 5px 5px 10px rgba(0, 0, 0, 0.2);
+            transition: transform 0.3s ease-in-out;
+        }
+    </style>
+    """, unsafe_allow_html=True)
+    st.image("assets/ailabs_logo.png", width=250)
+
+col_title, col_loan_line = st.columns([4, 1])
+with col_title:
+    st.markdown(
+        "<h1 style='text-align: left; color: #ffffff;'>Early Detection Dashboard for Credit Risk</h1>",
+        unsafe_allow_html=True
+    )
+
+with col_loan_line:
+    st.markdown("""
+    <style>
+        .stImage > img {
+            width: 200px; /* Increase size */
+            float: right; /* Align to the right */
+            border-radius: 10px;
+            box-shadow: 5px 5px 10px rgba(0, 0, 0, 0.2);
+            transition: transform 0.3s ease-in-out;
+        }
+    </style>
+    """, unsafe_allow_html=True)
+    st.image("assets/loan_line.png", width=350)
+
+st.subheader("Product Type: :violet[Personal Loans]")
+st.markdown("---")
+
+# -----------------------------
+# Define Risk Score Bands
+# -----------------------------
+data = {
+    "Risk Level": ["High Risk", "Medium Risk", "Low Risk"],
+    "Score Range": ["0 - 350", "351 - 650", "651 - 999"],
+    "Description": [
+        "High probability of risk.",
+        "Moderate probability of risk.",
+        "Low probability of risk."
+    ]
+}
+df_risk_bands = pd.DataFrame(data)
+
+def highlight_risk(row):
+    color = ''
+    if row['Risk Level'] == 'High Risk':
+        color = 'background-color: #BD100D; color: white;'
+    elif row['Risk Level'] == 'Medium Risk':
+        color = 'background-color: #B88F12; color: white;'
+    elif row['Risk Level'] == 'Low Risk':
+        color = 'background-color: #3A800F; color: white;'
+    return [color]*len(row)
+
+df_risk_bands = df_risk_bands.style.apply(highlight_risk, axis=1)
+
+# -----------------------------
+# Load  Portfolio Data
+# -----------------------------
+def load_data():
+    # df = pd.read_csv("data/time_series_customer_loan_data_with_shap.csv")
+    df = pd.read_csv("data/kenya_personal_loan_data_with_shap.csv")
+    df["Top Contributors"] = df["Top Contributors"].apply(json.loads)
+    df['Date'] = pd.to_datetime(df['Date'])
+    # df['Loan Status'] = df['Delinquency Bucket'].apply(lambda x: 'Current' if x in [0, 1] else 'Delinquent') # defining the delinquency status
+    return df
+
+df = load_data()
+
+target_date = '2026-01-31'
+last_month_df = df[df['Date'] == target_date] # filter the dataframe for the target_date
+
+# -----------------------------
+# Compute KPIs
+# -----------------------------
+
+# Portfolio Composition
+total_number_of_accounts = df['Account ID'].nunique()
+total_ending_balance = df["Outstanding Balance"].sum()
+average_loan_size = df["Initial Loan Amount"].mean()
+
+payments_sum_by_date = df.groupby('Date')['Monthly Payments'].sum().reset_index()
+payments_sum_by_date = df.groupby('Date')['Monthly Payments'].sum().reset_index()
+match = payments_sum_by_date[payments_sum_by_date['Date'] == target_date]
+if not match.empty:
+    payments_within_the_month = match['Monthly Payments'].values[0]
+else:
+    payments_within_the_month = 0
+
+# Delinquency Risk Metrics
+# total_number_of_dq_accounts = last_month_df[last_month_df['Loan Bucket'].isin([2,3,4,5,6,7,8])].shape[0]
+total_number_of_dq_accounts = last_month_df[last_month_df['Delinquency Status'] == 'Delinquent'].shape[0]
+average_risk_score = round(last_month_df["Risk Score"].mean(), 2)
+
+risk_counts = last_month_df["Risk Category"].value_counts().to_dict()
+
+## High risk metrics
+high_risk_count = risk_counts.get("High Risk", 0)
+high_risk_percentage = (high_risk_count / total_number_of_accounts) * 100 if total_number_of_accounts > 0 else 0
+high_risk_percentage = round(high_risk_percentage, 1)
+high_risk_average_score = round(last_month_df[last_month_df["Risk Category"] == "High Risk"]["Risk Score"].mean(), 2) if high_risk_count > 0 else 0
+high_risk_average_loan_amount = round(last_month_df[last_month_df["Risk Category"] == "High Risk"]["Initial Loan Amount"].mean(), 2) if high_risk_count > 0 else 0
+
+## Medium risk metrics
+medium_risk_count = risk_counts.get("Medium Risk", 0)
+medium_risk_percentage = (medium_risk_count / total_number_of_accounts) * 100 if total_number_of_accounts > 0 else 0
+medium_risk_percentage = round(medium_risk_percentage, 1)
+medium_risk_average_score = round(last_month_df[last_month_df["Risk Category"] == "Medium Risk"]["Risk Score"].mean(), 2) if medium_risk_count > 0 else 0
+medium_risk_average_loan_amount = round(last_month_df[last_month_df["Risk Category"] == "Medium Risk"]["Initial Loan Amount"].mean(), 2) if medium_risk_count > 0 else 0
+
+## Low risk metrics
+low_risk_count = risk_counts.get("Low Risk", 0)
+low_risk_percentage = (low_risk_count / total_number_of_accounts) * 100 if total_number_of_accounts > 0 else 0
+low_risk_percentage = round(low_risk_percentage, 1)
+low_risk_average_score = round(last_month_df[last_month_df["Risk Category"] == "Low Risk"]["Risk Score"].mean(), 2) if low_risk_count > 0 else 0
+low_risk_average_loan_amount = round(last_month_df[last_month_df["Risk Category"] == "Low Risk"]["Initial Loan Amount"].mean(), 2) if low_risk_count > 0 else 0
+
+## Pie chart for percentages of accounts in each loan bucket
+loan_bucket_counts = last_month_df['Delinquency Bucket'].value_counts().sort_index()
+
+## Pie chart for proportion percentage of outstanding balance by delinquency bucket
+outstanding_balance_by_bucket = last_month_df.groupby('Delinquency Bucket')['Outstanding Balance'].sum().sort_index()
+
+## Pie chart for account distribution by payment behaviour
+payment_behaviour_counts = last_month_df['Payment Behaviour'].value_counts().sort_index()
+
+## Pie chart for account distribution by collection strategy
+collection_strategy_counts = last_month_df['Recommended Risk Action'].value_counts().sort_index()
+
+## Risk Score Distribution by Deciles
+last_month_df["Risk Decile"] = pd.qcut(last_month_df['Risk Score'], q=10, labels=[f'D{i+1}' for i in range(10)])
+grouped_deciles = last_month_df.groupby(['Risk Decile', 'Delinquency Status']).size().reset_index(name='Count')
+pivot_df_score_deciles = grouped_deciles.pivot(index='Risk Decile', columns='Delinquency Status', values='Count').fillna(0)
+pivot_df_score_deciles = pivot_df_score_deciles.reindex([f'D{i+1}' for i in range(10)])  # Ensure consistent decile order
+
+# -----------------------------
+# KPI Summary Cards
+# -----------------------------
+st.header("Portfolio Composition- as of :violet[August 2025]")
+
+kpi1, kpi2, kpi3, kpi4 = st.columns(4)
+kpi1.metric(label="Total Number of Accounts", value=total_number_of_accounts, border=True)
+kpi2.metric(label="Total Outstanding Balance", value=f"USD {total_ending_balance:,.2f}", border=True)
+kpi3.metric(label="Average Loan Size", value=f"USD {average_loan_size:,.2f}", border=True)
+kpi4.metric(label="Payments", value=f"USD {payments_within_the_month:,.2f}", border=True)
+
+kpi5, kpi6, kpi7 = st.columns(3)
+kpi5.metric(label="Average Loan Amount (High Risk Accounts)", value=f"USD {high_risk_average_loan_amount:,.2f}", border=True)
+kpi6.metric(label="Average Loan Amount (Medium Risk Accounts)", value=f"USD {medium_risk_average_loan_amount:,.2f}", border=True)
+kpi7.metric(label="Average Loan Amount (Low Risk Accounts)", value=f"USD {low_risk_average_loan_amount:,.2f}", border=True)
+
+col_pred, col_mid, col_lim = st.columns([3, 1, 3])
+
+# Displaying the metrics for the prediction month
+with col_pred:
+    st.header("Forecasted Metrics for :violet[February 2026]")
+
+    st.metric(label="Total Number of Delinquent Accounts", value=total_number_of_dq_accounts, border=True)
+    st.metric(label="Average Risk Score (Portfolio Level)", value=558.57, border=True)
+
+    st.metric(label="High Risk Accounts (%)", value=f"{high_risk_percentage}%", border=True)
+    st.metric(label="Average Risk Score (High Risk Accounts)", value=high_risk_average_score, border=True)
+
+    st.metric(label="Medium Risk Accounts (%)", value=f"{medium_risk_percentage}%", border=True)
+    st.metric(label="Average Risk Score (Medium Risk Accounts)", value=medium_risk_average_score, border=True)
+
+    st.metric(label="Low Risk Accounts (%)", value=f"{low_risk_percentage}%", border=True)
+    st.metric(label="Average Risk Score (Low Risk Accounts)", value=low_risk_average_score, border=True)
+
+    # -----------------------------
+    # Account Distribution by Delinquency Bucket
+    # -----------------------------
+    green_shades = {
+        '0-30': '#92ff7b',
+        '30+ DPD': '#92ff7b'
+    }
+
+    red_shade_base = '#fe6262'
+
+    # Define colors based on label
+    colors = []
+    for label in loan_bucket_counts.index.astype(str):
+        if label in green_shades:
+            colors.append(green_shades[label])
+        else:
+            colors.append(red_shade_base)
+
+    bucket_wise_account_percentages = go.Figure(
+        data=[
+            go.Pie(
+                labels=loan_bucket_counts.index.astype(str),
+                values=loan_bucket_counts.values,
+                textinfo='label+percent',
+                textposition='inside',
+                marker=dict(colors=colors, line=dict(color='white', width=2)),
+                hovertemplate='<b>Delinquency Bucket:</b> %{label}<br><b>Customers:</b> %{value}<br><b>Share:</b> %{percent}',
+                sort=False
+            )
+        ]
+    )
+
+    st.subheader("Accounts Distribution by Delinquency Bucket")
+    bucket_wise_account_percentages.update_layout(
+        template='plotly_white',
+        margin=dict(t=80, b=50, l=50, r=50)
+    )
+    st.plotly_chart(bucket_wise_account_percentages, use_container_width=True)
+
+    # -----------------------------
+    # Accounts Distribution by Collection Strategy
+    # -----------------------------
+    collection_strategy_colors = {
+        'No Action Required': '#92ff7b',
+        'Monitor': '#92ff7b',
+        'Payment reminder email': '#ff9966',
+        'Payment reminder call': '#ff9966',
+        'Debt Relief Plan': '#fe6262',
+        'Downgrade Account': '#fe6262'
+    }
+
+    colors = []
+    for label in collection_strategy_counts.index.astype(str):
+        colors.append(collection_strategy_colors.get(label, '#d3d3d3'))  # default grey if not mapped
+
+    collection_strategy_percentages = go.Figure(
+        data=[
+            go.Pie(
+                labels=collection_strategy_counts.index.astype(str),
+                values=collection_strategy_counts.values,
+                textinfo='label+percent',
+                textposition='inside',
+                marker=dict(colors=colors, line=dict(color="white", width=2)),
+                hovertemplate='<b>Collection Strategy:</b> %{label}<br><b>Share:</b> %{percent}',
+                sort=False
+            )
+        ]
+    )
+
+    st.subheader("Accounts Distribution by Collection Strategy")
+    collection_strategy_percentages.update_layout(
+        template='plotly_white',
+        margin=dict(t=80, b=50, l=10, r=100)
+    )
+
+    st.plotly_chart(collection_strategy_percentages, use_container_width=True)
+
+with col_mid:
+    st.markdown(
+        """
+        <div style="display: flex; justify-content: center;">
+            <div style="height: 225vh; border-left: 2px solid white;"></div>
+        </div>
+        """,
+        unsafe_allow_html=True
+    )
+
+# Displaying the metrics for the last input month
+with col_lim:
+    st.header("Metrics as of :violet[August 2025]")
+
+    st.metric(label="Total Number of Delinquent Accounts", value=34, border=True)
+    st.metric(label="Average Risk Score (Portfolio Level)", value=523.13, border=True)
+
+    st.metric(label="High Risk Accounts (%)", value=f"{34.0}%", border=True)
+    st.metric(label="Average Risk Score (High Risk Accounts)", value=146.90, border=True)
+
+    st.metric(label="Medium Risk Accounts (%)", value=f"{26.0}%", border=True)
+    st.metric(label="Average Risk Score (Medium Risk Accounts)", value=493.14, border=True)
+
+    st.metric(label="Low Risk Accounts (%)", value=f"{40.0}%", border=True)
+    st.metric(label="Average Risk Score (Low Risk Accounts)", value=843.54, border=True)
+
+    # -----------------------------
+    # Account Distribution by Proportion of Outstanding Balance
+    # -----------------------------
+    green_shades = {
+        '0-30': '#92ff7b',
+        '30+ DPD': '#92ff7b'
+    }
+
+    red_shade_base = '#fe6262'
+
+    # Define colors based on label
+    colors = []
+    for label in outstanding_balance_by_bucket.index.astype(str):
+        if label in green_shades:
+            colors.append(green_shades[label])
+        else:
+            colors.append(red_shade_base)
+
+    bucket_wise_outstanding_balance_percentages = go.Figure(
+        data=[
+            go.Pie(
+                labels=outstanding_balance_by_bucket.index.astype(str),
+                values=outstanding_balance_by_bucket.values,
+                textinfo='label+percent',
+                textposition='inside',
+                marker=dict(colors=colors, line=dict(color='white', width=2)),
+                hovertemplate='<b>Delinquency Bucket:</b> %{label}<br><b>Outstanding Balance:</b> %{value}<br><b>Share:</b> %{percent}',
+                sort=False
+            )
+        ]
+    )
+
+    st.subheader("Accounts Distribution by Proportion of Ending Balance")
+    bucket_wise_outstanding_balance_percentages.update_layout(
+        template='plotly_white',
+        margin=dict(t=80, b=50, l=50, r=50)
+    )
+
+    st.plotly_chart(bucket_wise_outstanding_balance_percentages, use_container_width=True)
+
+    # -----------------------------
+    # Account Distribution by Payment Behaviour
+    # -----------------------------
+    payment_behaviour_colors = {
+        'On Time': '#92ff7b',
+        'Late Payer': '#ff9966',
+        'Irregular Payer': '#ff9966',
+        'Non Payer': '#fe6262'
+    }
+
+    # Assign colors based on the label
+    colors = []
+    for label in payment_behaviour_counts.index.astype(str):
+        colors.append(payment_behaviour_colors.get(label, '#d3d3d3'))  # default grey if not mapped
+
+    bucket_wise_payment_behaviour_percentages = go.Figure(
+        data=[
+            go.Pie(
+                labels=payment_behaviour_counts.index.astype(str),
+                values=payment_behaviour_counts.values,
+                textinfo='label+percent',
+                textposition='inside',
+                marker=dict(colors=colors, line=dict(color="white", width=2)),
+                hovertemplate='<b>Payment Behaviour:</b> %{label}<br><b>Share:</b> %{percent}',
+                sort=False
+            )
+        ]
+    )
+
+    st.markdown("### Accounts Distribution by Payment Behaviour")
+    bucket_wise_payment_behaviour_percentages.update_layout(
+        template='plotly_white',
+        margin=dict(t=80, b=50, l=50, r=50)
+    )
+
+    st.plotly_chart(bucket_wise_payment_behaviour_percentages, use_container_width=True)
+
+# -----------------------------
+# Account Distribution by Risk Score vs Loan Status (Decile Wise Snapshot)- as of :violet[February 2026]
+# -----------------------------
+colors = {
+    'Current': '#92ff7b',
+    'Delinquent': '#fe6262'
+}
+
+decile_score_distribution = go.Figure()
+
+for status in ['Current', 'Delinquent']:
+    decile_score_distribution.add_trace(
+        go.Bar(
+            x=pivot_df_score_deciles.index,
+            y=pivot_df_score_deciles[status],
+            name=status,
+            marker_color=colors[status],
+            width=0.4,
+        )
+    )
+
+decile_score_distribution.update_layout(
+    barmode='group',
+    title={
+        'text': 'Account Distribution by Risk Score vs Delinquency Status (Decile Wise Snapshot)- Forecasted for February 2026',
+        'x': 0.5,
+        'xanchor': 'center',
+        'font': dict(size=29, family=', sans-serif')
+    },
+    xaxis_title='Risk Score Decile',
+    yaxis_title='Number of Customers',
+    template='plotly_white',
+    legend=dict(
+        # title='Loan Status',
+        orientation='h',
+        yanchor='bottom',
+        y=-0.3,
+        xanchor='center',
+        x=0.5,
+        font=dict(size=12)
+    ),
+    margin=dict(t=80, b=100, l=50, r=50)
+)
+st.plotly_chart(decile_score_distribution, use_container_width=True)

README.md

@@ -10,11 +10,4 @@ tags:
 pinned: false
 short_description: Early Detection Dashboard for Credit Risk
 license: apache-2.0
----
-
-# Welcome to Streamlit!
-
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
-
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+---