Create app.py

app.py

@@ -0,0 +1,1103 @@
+# app.py
+
+import pandas as pd
+import gradio as gr
+
+# ---------------------------------------------------
+# HELPERS
+# ---------------------------------------------------
+
+from helper.vintage_helpers import (
+    create_booking_vintage
+)
+
+from helper.data_merger import (
+    merge_acq_perf
+)
+
+# ---------------------------------------------------
+# METRICS
+# ---------------------------------------------------
+
+from metrics.mix_metrics import (
+    calculate_vintage_mix,
+    calculate_limit_mix
+)
+
+# ---------------------------------------------------
+# ANALYTICS
+# ---------------------------------------------------
+
+from analytics.performance_analysis import (
+    generate_metric_view
+)
+
+from analytics.ai_assistant import (
+    generate_ai_answer
+)
+
+# ---------------------------------------------------
+# VISUALIZATIONS - VINTAGE CURVES
+# ---------------------------------------------------
+
+from visualizations.vintage_curves import (
+    generate_delinquency_metric_chart,
+    generate_multi_metric_comparison,
+    generate_segment_delinquency_curve
+)
+
+# ---------------------------------------------------
+# VISUALIZATIONS - SEGMENT RANKING
+# ---------------------------------------------------
+
+from visualizations.segment_ranking import (
+    generate_segment_risk_heatmap,
+    generate_segment_risk_ranking,
+    generate_multi_category_risk_comparison,
+    calculate_portfolio_risk_summary
+)
+
+# ---------------------------------------------------
+# LOAD DATA
+# ---------------------------------------------------
+
+acq = pd.read_csv(
+    "data/acquisition.csv"
+)
+
+perf = pd.read_csv(
+    "data/performance.csv"
+)
+
+# ---------------------------------------------------
+# CREATE BOOKING VINTAGE
+# ---------------------------------------------------
+
+acq = create_booking_vintage(
+    acq,
+    booking_date_col="booking_date"
+)
+
+# ---------------------------------------------------
+# CREATE MASTER PERFORMANCE DATASET
+# ---------------------------------------------------
+
+master_df = merge_acq_perf(
+    acq_df=acq,
+    perf_df=perf
+)
+
+# ---------------------------------------------------
+# ACQUISITION ANALYSIS
+# ---------------------------------------------------
+
+def run_acquisition_analysis(
+    analysis_type,
+    category
+):
+
+    # -----------------------------------------
+    # PORTFOLIO MIX
+    # -----------------------------------------
+
+    if analysis_type == "Portfolio Mix":
+
+        result = (
+            acq.groupby(
+                ["booking_vintage", category]
+            )
+            .agg(
+                count=("account_id", "nunique"),
+                balance=("credit_limit", "sum")
+            )
+            .reset_index()
+        )
+
+        vintage_total = (
+            result.groupby("booking_vintage")["count"]
+            .transform("sum")
+        )
+
+        result["rate"] = (
+            result["count"] / vintage_total
+        ) * 100
+
+        result["rate"] = (
+            result["rate"]
+            .round(2)
+        )
+
+    # -----------------------------------------
+    # CREDIT LINE CONCENTRATION
+    # -----------------------------------------
+
+    elif analysis_type == "Credit Line Concentration":
+
+        result = (
+            acq.groupby(
+                ["booking_vintage", category]
+            )
+            .agg(
+                count=("account_id", "nunique"),
+                balance=("credit_limit", "sum")
+            )
+            .reset_index()
+        )
+
+        vintage_total = (
+            result.groupby("booking_vintage")["balance"]
+            .transform("sum")
+        )
+
+        result["rate"] = (
+            result["balance"] / vintage_total
+        ) * 100
+
+        result["rate"] = (
+            result["rate"]
+            .round(2)
+        )
+
+    else:
+
+        return pd.DataFrame()
+
+    # -----------------------------------------
+    # STANDARDIZED OUTPUT
+    # -----------------------------------------
+
+    result = result.rename(
+        columns={
+            "booking_vintage": "Vintage",
+            category: "Category",
+            "count": "Count",
+            "balance": "Balance",
+            "rate": "Rate"
+        }
+    )
+
+    return result[
+        [
+            "Vintage",
+            "Category",
+            "Count",
+            "Balance",
+            "Rate"
+        ]
+    ]
+
+# ---------------------------------------------------
+# PERFORMANCE ANALYSIS
+# ---------------------------------------------------
+
+def run_performance_analysis(
+    metric_name,
+    view_level
+):
+
+    # -----------------------------------------
+    # VIEW MAPPING
+    # -----------------------------------------
+
+    view_mapping = {
+
+        "Overall": None,
+
+        "Channel":
+        "sourcing_channel",
+
+        "FICO":
+        "fico_band",
+
+        "City Tier":
+        "city_tier",
+
+        "Occupation":
+        "occupation_type"
+    }
+
+    group_col = view_mapping[
+        view_level
+    ]
+
+    # -----------------------------------------
+    # CALL ANALYTICS ENGINE
+    # -----------------------------------------
+
+    result = generate_metric_view(
+        df=master_df,
+        metric_name=metric_name,
+        group_col=group_col
+    )
+
+    # -----------------------------------------
+    # STANDARDIZE OUTPUT
+    # -----------------------------------------
+
+    if group_col is not None:
+
+        result = result.rename(
+            columns={
+                group_col: "Category"
+            }
+        )
+
+    else:
+
+        result["Category"] = "Overall"
+
+    # -----------------------------------------
+    # IDENTIFY RATE COLUMN
+    # -----------------------------------------
+
+    rate_col = [
+        col for col in result.columns
+        if "rate" in col.lower()
+    ][0]
+
+    # -----------------------------------------
+    # OUTPUT FORMAT
+    # -----------------------------------------
+
+    final_result = pd.DataFrame()
+
+    final_result["Vintage"] = (
+        result["booking_vintage"]
+    )
+
+    final_result["Category"] = (
+        result["Category"]
+    )
+
+    final_result["Count"] = (
+        result["total_accounts"]
+    )
+
+    final_result["Balance"] = (
+        result["total_balance"]
+    )
+
+    final_result["Rate"] = (
+        result[rate_col]
+        .round(2)
+    )
+
+    return final_result
+
+# ---------------------------------------------------
+# VINTAGE CURVES ANALYSIS
+# ---------------------------------------------------
+
+def generate_vintage_curve_single(
+    metric_name
+):
+    """Generate single vintage curve for a metric."""
+    try:
+        fig = generate_delinquency_metric_chart(
+            df=master_df,
+            metric_name=metric_name,
+            chart_type="line"
+        )
+        return fig
+    except Exception as e:
+        return f"Error generating vintage curve: {str(e)}"
+
+
+def generate_vintage_curves_comparison():
+    """Generate comparison of all vintage curves."""
+    try:
+        fig = generate_multi_metric_comparison(
+            df=master_df,
+            metrics=["30+@3", "30+@6", "60+@6", "Yr1 NCL"]
+        )
+        return fig
+    except Exception as e:
+        return f"Error generating comparison: {str(e)}"
+
+
+def generate_segmented_vintage_curve(
+    metric_name,
+    category
+):
+    """Generate vintage curve segmented by category."""
+    try:
+        fig = generate_segment_delinquency_curve(
+            df=master_df,
+            metric_name=metric_name,
+            category=category
+        )
+        return fig
+    except Exception as e:
+        return f"Error generating segmented curve: {str(e)}"
+
+# ---------------------------------------------------
+# SEGMENT RANKING ANALYSIS
+# ---------------------------------------------------
+
+def generate_segment_risk_heatmap_chart():
+    """Generate risk heatmap across all segments and metrics."""
+    try:
+        fig = generate_segment_risk_heatmap(
+            df=master_df
+        )
+        return fig
+    except Exception as e:
+        return f"Error generating heatmap: {str(e)}"
+
+
+def generate_high_risk_segments_ranking(
+    metric_name,
+    category
+):
+    """Generate ranking of high-risk segments."""
+    try:
+        fig = generate_segment_risk_ranking(
+            df=master_df,
+            metric_name=metric_name,
+            category=category,
+            top_n=10
+        )
+        return fig
+    except Exception as e:
+        return f"Error generating ranking: {str(e)}"
+
+
+def generate_multi_category_comparison(
+    metric_name
+):
+    """Generate risk comparison across all categories."""
+    try:
+        fig = generate_multi_category_risk_comparison(
+            df=master_df,
+            metric_name=metric_name
+        )
+        return fig
+    except Exception as e:
+        return f"Error generating comparison: {str(e)}"
+
+
+def generate_portfolio_summary():
+    """Generate portfolio risk summary."""
+    try:
+        summary_df = calculate_portfolio_risk_summary(
+            df=master_df
+        )
+        return summary_df
+    except Exception as e:
+        return f"Error generating summary: {str(e)}"
+
+
+def ask_ai_question(question, as_of_month, segment, history):
+    if not question or str(question).strip() == "":
+        return history or [], history or [], "Please enter a question."
+    try:
+        answer = generate_ai_answer(
+            question=question,
+            df=master_df,
+            as_of_month=as_of_month,
+            segment=segment if segment != "" else None
+        )
+    except Exception as exc:
+        answer = f"AI error: {str(exc)}"
+
+    history = history or []
+    history.append((question, answer))
+    return history, history, ""
+
+
+# ---------------------------------------------------
+# PORTFOLIO OVERVIEW (Calendar Snapshot)
+# ---------------------------------------------------
+
+
+def _detect_date_column(df):
+    candidates = [
+        "reporting_month",
+        "observation_date",
+        "observation_month",
+        "obs_date",
+        "date",
+        "calendar_month",
+        "month",
+        "report_date"
+    ]
+    for c in candidates:
+        if c in df.columns:
+            return c
+    return None
+
+
+def get_calendar_months():
+    date_col = _detect_date_column(master_df)
+    if date_col is None:
+        return []
+    ser = pd.to_datetime(master_df[date_col], errors="coerce")
+    months = ser.dt.to_period("M").astype(str).dropna().unique().tolist()
+    months.sort()
+    return months
+
+
+def _filter_master_by_month(as_of_month):
+    # as_of_month expected like "YYYY-MM"
+    date_col = _detect_date_column(master_df)
+    if date_col is None or not as_of_month:
+        return master_df.copy()
+    ser = pd.to_datetime(master_df[date_col], errors="coerce").dt.to_period("M").astype(str)
+    return master_df[ser == as_of_month].copy()
+
+
+def generate_portfolio_overview(as_of_month, segment):
+    """
+    Returns a small DataFrame with key portfolio snapshot metrics for the selected calendar month and segment.
+    Metrics: Total Accounts, Open Accounts (balance>0), Bad Accounts (dpd>=30), Overall NCL Rate (dollar %), Average FICO.
+    """
+    df = _filter_master_by_month(as_of_month)
+
+    # If a segmentation column is provided, return per-segment breakdown
+    valid_segments = [
+        "fico_band",
+        "sourcing_channel",
+        "city_tier",
+        "occupation_type"
+    ]
+
+    if segment in valid_segments and segment in df.columns:
+        grp = segment
+
+        total_accounts = df.groupby(grp)["account_id"].nunique()
+
+        if "balance" in df.columns:
+            open_accounts = (
+                df.loc[df["balance"] > 0].groupby(grp)["account_id"].nunique()
+            )
+            total_balance = df.groupby(grp)["balance"].sum()
+        else:
+            open_accounts = pd.Series(0, index=total_accounts.index)
+            total_balance = pd.Series(0, index=total_accounts.index)
+
+        if "dpd" in df.columns:
+            bad_accounts = (
+                df.loc[df["dpd"].fillna(0) >= 30].groupby(grp)["account_id"].nunique()
+            )
+            bad_balance = (
+                df.assign(_bad_balance=df["balance"].where(df["dpd"].fillna(0) >= 30, 0))
+                .groupby(grp)["_bad_balance"].sum()
+            ) if "balance" in df.columns else pd.Series(0, index=total_accounts.index)
+        else:
+            bad_accounts = pd.Series(0, index=total_accounts.index)
+            bad_balance = pd.Series(0, index=total_accounts.index)
+
+        # NCL if present
+        ncl_cols = [c for c in df.columns if "ncl" in c.lower()]
+        if len(ncl_cols) > 0:
+            total_ncl = df.groupby(grp)[ncl_cols[0]].sum()
+            ncl_rate = (total_ncl / total_balance * 100).round(2).fillna(0)
+        else:
+            ncl_rate = (bad_balance / total_balance * 100).round(2).fillna(0)
+
+        # average fico per group if available
+        if "fico_score" in df.columns:
+            avg_fico = df.groupby(grp)["fico_score"].mean().round(1)
+        else:
+            avg_fico = pd.Series(float("nan"), index=total_accounts.index)
+
+        result = pd.DataFrame({
+            "Segment": total_accounts.index,
+            "Total_Accounts": total_accounts.values,
+            "Open_Accounts": open_accounts.reindex(total_accounts.index).fillna(0).astype(int).values,
+            "Bad_Accounts": bad_accounts.reindex(total_accounts.index).fillna(0).astype(int).values,
+            "Total_Balance": total_balance.reindex(total_accounts.index).fillna(0).values,
+            "NCL_Rate_pct": ncl_rate.reindex(total_accounts.index).fillna(0).values,
+            "Avg_FICO": avg_fico.reindex(total_accounts.index).fillna(float("nan")).values
+        })
+
+        # Sort by NCL rate descending
+        result = result.sort_values("NCL_Rate_pct", ascending=False).reset_index(drop=True)
+
+        return result
+
+    # Default: single-line overview
+    total_accounts = df["account_id"].nunique() if "account_id" in df.columns else 0
+
+    open_accounts = (
+        df.loc[df["balance"] > 0, "account_id"].nunique()
+        if "balance" in df.columns
+        else total_accounts
+    )
+
+    bad_accounts = (
+        df.loc[df["dpd"].fillna(0) >= 30, "account_id"].nunique()
+        if "dpd" in df.columns
+        else 0
+    )
+
+    # overall NCL rate (dollar-based) fallback logic
+    ncl_cols = [c for c in df.columns if "ncl" in c.lower()]
+    overall_ncl_rate = None
+    if len(ncl_cols) > 0 and "balance" in df.columns:
+        ncl_sum = df[ncl_cols[0]].sum(skipna=True)
+        bal_sum = df["balance"].sum(skipna=True)
+        overall_ncl_rate = (ncl_sum / bal_sum * 100) if bal_sum > 0 else None
+    else:
+        # fallback: use bad balance / total balance as proxy
+        if "balance" in df.columns and "dpd" in df.columns:
+            bad_bal = df.loc[df["dpd"].fillna(0) >= 30, "balance"].sum()
+            bal_sum = df["balance"].sum()
+            overall_ncl_rate = (bad_bal / bal_sum * 100) if bal_sum > 0 else None
+
+    if overall_ncl_rate is None:
+        overall_ncl_rate = float("nan")
+    else:
+        overall_ncl_rate = round(overall_ncl_rate, 2)
+
+    # average fico
+    avg_fico = None
+    if "fico_score" in df.columns:
+        avg_fico = round(df["fico_score"].dropna().mean(), 1)
+    elif "fico_band" in df.columns:
+        def band_mid(b):
+            try:
+                parts = b.split("-")
+                return (int(parts[0]) + int(parts[1])) / 2
+            except Exception:
+                return None
+        mid_vals = df["fico_band"].dropna().apply(band_mid).dropna()
+        avg_fico = round(mid_vals.mean(), 1) if not mid_vals.empty else float("nan")
+    else:
+        avg_fico = float("nan")
+
+    overview = pd.DataFrame({
+        "Metric": [
+            "As Of Month",
+            "Total Accounts",
+            "Open Accounts",
+            "Bad Accounts (dpd>=30)",
+            "Overall NCL Rate (%)",
+            "Average FICO"
+        ],
+        "Value": [
+            as_of_month if as_of_month else "All",
+            int(total_accounts),
+            int(open_accounts),
+            int(bad_accounts),
+            overall_ncl_rate,
+            avg_fico
+        ]
+    })
+
+    return overview
+
+# ---------------------------------------------------
+# DYNAMIC DROPDOWNS
+# ---------------------------------------------------
+
+def update_analysis_dropdown(
+    dataset
+):
+
+    # -----------------------------------------
+    # ACQUISITION
+    # -----------------------------------------
+
+    if dataset == "Acquisition":
+
+        return gr.update(
+            choices=[
+                "Portfolio Mix",
+                "Credit Line Concentration"
+            ],
+            value="Portfolio Mix"
+        )
+
+    # -----------------------------------------
+    # PERFORMANCE
+    # -----------------------------------------
+
+    elif dataset == "Performance":
+
+        return gr.update(
+            choices=[
+                "30+@3",
+                "30+@6",
+                "60+@6",
+                "Yr1 NCL"
+            ],
+            value="30+@6"
+        )
+
+
+def update_category_dropdown(
+    dataset
+):
+
+    # -----------------------------------------
+    # ACQUISITION
+    # -----------------------------------------
+
+    if dataset == "Acquisition":
+
+        return gr.update(
+            choices=[
+                "fico_band",
+                "sourcing_channel",
+                "city_tier",
+                "occupation_type"
+            ],
+            value="fico_band"
+        )
+
+    # -----------------------------------------
+    # PERFORMANCE
+    # -----------------------------------------
+
+    elif dataset == "Performance":
+
+        return gr.update(
+            choices=[
+                "Overall",
+                "Channel",
+                "FICO",
+                "City Tier",
+                "Occupation"
+            ],
+            value="Overall"
+        )
+
+# ---------------------------------------------------
+# MASTER ROUTER
+# ---------------------------------------------------
+
+def run_analysis(
+    dataset,
+    analysis,
+    category
+):
+
+    # -----------------------------------------
+    # ACQUISITION
+    # -----------------------------------------
+
+    if dataset == "Acquisition":
+
+        return run_acquisition_analysis(
+            analysis_type=analysis,
+            category=category
+        )
+
+    # -----------------------------------------
+    # PERFORMANCE
+    # -----------------------------------------
+
+    elif dataset == "Performance":
+
+        return run_performance_analysis(
+            metric_name=analysis,
+            view_level=category
+        )
+
+    else:
+
+        return pd.DataFrame()
+
+# ---------------------------------------------------
+# GRADIO UI
+# ---------------------------------------------------
+
+with gr.Blocks() as app:
+
+    gr.Markdown(
+        "# Risk Analytics Manager Agent - Phase 2"
+    )
+
+    with gr.Tabs():
+
+        # =================================================
+        # TAB 1: BASIC ANALYSIS (Phase 1)
+        # =================================================
+
+        with gr.TabItem("📊 Basic Analysis"):
+
+            gr.Markdown(
+                "## Phase 1: Acquisition & Performance Analysis"
+            )
+
+            with gr.Row():
+
+                dataset_dropdown = gr.Dropdown(
+                    choices=[
+                        "Acquisition",
+                        "Performance"
+                    ],
+                    value="Acquisition",
+                    label="Dataset"
+                )
+
+                analysis_dropdown = gr.Dropdown(
+                    choices=[
+                        "Portfolio Mix",
+                        "Credit Line Concentration"
+                    ],
+                    value="Portfolio Mix",
+                    label="Analysis"
+                )
+
+                category_dropdown = gr.Dropdown(
+                    choices=[
+                        "fico_band",
+                        "sourcing_channel",
+                        "city_tier",
+                        "occupation_type"
+                    ],
+                    value="fico_band",
+                    label="Category / View"
+                )
+
+            # -----------------------------------------
+            # DYNAMIC DROPDOWNS
+            # -----------------------------------------
+
+            dataset_dropdown.change(
+                fn=update_analysis_dropdown,
+                inputs=dataset_dropdown,
+                outputs=analysis_dropdown
+            )
+
+            dataset_dropdown.change(
+                fn=update_category_dropdown,
+                inputs=dataset_dropdown,
+                outputs=category_dropdown
+            )
+
+            # -----------------------------------------
+            # RUN BUTTON
+            # -----------------------------------------
+
+            run_button = gr.Button(
+                "Run Analysis",
+                variant="primary"
+            )
+
+            output_table = gr.Dataframe()
+
+            run_button.click(
+                fn=run_analysis,
+                inputs=[
+                    dataset_dropdown,
+                    analysis_dropdown,
+                    category_dropdown
+                ],
+                outputs=output_table
+            )
+
+        # =================================================
+        # TAB 2: VINTAGE CURVES (Phase 2)
+        # =================================================
+
+        with gr.TabItem("📈 Vintage Curves"):
+
+            gr.Markdown(
+                "## Phase 2: Vintage Delinquency Curves Analysis"
+            )
+
+            with gr.Row():
+
+                metric_dropdown = gr.Dropdown(
+                    choices=[
+                        "30+@3",
+                        "30+@6",
+                        "60+@6",
+                        "Yr1 NCL"
+                    ],
+                    value="30+@6",
+                    label="Delinquency Metric"
+                )
+
+                vintage_chart_type = gr.Radio(
+                    choices=["Single Metric", "All Metrics Comparison"],
+                    value="Single Metric",
+                    label="Chart Type"
+                )
+
+            def update_vintage_view(metric, chart_type):
+                if chart_type == "Single Metric":
+                    return generate_vintage_curve_single(metric)
+                else:
+                    return generate_vintage_curves_comparison()
+
+            vintage_chart = gr.Plot(
+                label="Vintage Curve"
+            )
+
+            gen_vintage_btn = gr.Button(
+                "Generate Vintage Curve",
+                variant="primary"
+            )
+
+            gen_vintage_btn.click(
+                fn=update_vintage_view,
+                inputs=[metric_dropdown, vintage_chart_type],
+                outputs=vintage_chart
+            )
+
+            gr.Markdown(
+                "### Segmented Vintage Curves"
+            )
+
+            with gr.Row():
+
+                segment_metric = gr.Dropdown(
+                    choices=[
+                        "30+@3",
+                        "30+@6",
+                        "60+@6",
+                        "Yr1 NCL"
+                    ],
+                    value="30+@6",
+                    label="Metric"
+                )
+
+                segment_category = gr.Dropdown(
+                    choices=[
+                        "fico_band",
+                        "sourcing_channel",
+                        "city_tier",
+                        "occupation_type"
+                    ],
+                    value="fico_band",
+                    label="Category"
+                )
+
+            segmented_chart = gr.Plot(
+                label="Segmented Vintage Curve"
+            )
+
+            gen_segment_btn = gr.Button(
+                "Generate Segmented Curve",
+                variant="primary"
+            )
+
+            gen_segment_btn.click(
+                fn=generate_segmented_vintage_curve,
+                inputs=[segment_metric, segment_category],
+                outputs=segmented_chart
+            )
+
+        # =================================================
+        # TAB 3: SEGMENT RANKING (Phase 2)
+        # =================================================
+
+        with gr.TabItem("⚠️ Segment Ranking"):
+
+            gr.Markdown(
+                "## Phase 2: High-Risk Segment Analysis"
+            )
+
+            # --------- HEATMAP SECTION ---------
+
+            gr.Markdown(
+                "### 🔥 Overall Risk Heatmap"
+            )
+
+            gr.Markdown(
+                "Risk scores across all delinquency metrics and segments"
+            )
+
+            heatmap_chart = gr.Plot(
+                label="Risk Heatmap"
+            )
+
+            gen_heatmap_btn = gr.Button(
+                "Generate Risk Heatmap",
+                variant="primary"
+            )
+
+            gen_heatmap_btn.click(
+                fn=generate_segment_risk_heatmap_chart,
+                outputs=heatmap_chart
+            )
+
+            gr.Markdown(
+                "---"
+            )
+
+            # --------- HIGH-RISK RANKING SECTION ---------
+
+            gr.Markdown(
+                "### 📊 High-Risk Segments Ranking"
+            )
+
+            with gr.Row():
+
+                ranking_metric = gr.Dropdown(
+                    choices=[
+                        "30+@3",
+                        "30+@6",
+                        "60+@6",
+                        "Yr1 NCL"
+                    ],
+                    value="30+@6",
+                    label="Metric"
+                )
+
+                ranking_category = gr.Dropdown(
+                    choices=[
+                        "fico_band",
+                        "sourcing_channel",
+                        "city_tier",
+                        "occupation_type"
+                    ],
+                    value="fico_band",
+                    label="Category"
+                )
+
+            ranking_chart = gr.Plot(
+                label="High-Risk Segments"
+            )
+
+            gen_ranking_btn = gr.Button(
+                "Generate Risk Ranking",
+                variant="primary"
+            )
+
+            gen_ranking_btn.click(
+                fn=generate_high_risk_segments_ranking,
+                inputs=[ranking_metric, ranking_category],
+                outputs=ranking_chart
+            )
+
+            gr.Markdown(
+                "---"
+            )
+
+            # --------- MULTI-CATEGORY COMPARISON ---------
+
+            gr.Markdown(
+                "### 🔀 Cross-Category Risk Comparison"
+            )
+
+            comparison_metric = gr.Dropdown(
+                choices=[
+                    "30+@3",
+                    "30+@6",
+                    "60+@6",
+                    "Yr1 NCL"
+                ],
+                value="30+@6",
+                label="Metric"
+            )
+
+            comparison_chart = gr.Plot(
+                label="Multi-Category Comparison"
+            )
+
+            gen_comparison_btn = gr.Button(
+                "Generate Comparison",
+                variant="primary"
+            )
+
+            gen_comparison_btn.click(
+                fn=generate_multi_category_comparison,
+                inputs=comparison_metric,
+                outputs=comparison_chart
+            )
+
+            gr.Markdown(
+                "---"
+            )
+
+            # --------- PORTFOLIO SUMMARY ---------
+
+            gr.Markdown(
+                "### 📋 Portfolio Risk Summary"
+            )
+
+            summary_table = gr.Dataframe(
+                label="Risk Summary"
+            )
+
+            gen_summary_btn = gr.Button(
+                "Generate Summary",
+                variant="primary"
+            )
+
+            gen_summary_btn.click(
+                fn=generate_portfolio_summary,
+                outputs=summary_table
+            )
+
+        # =================================================
+        # TAB 4: PORTFOLIO OVERVIEW (Calendar Snapshot)
+        # =================================================
+
+        with gr.TabItem("📅 Portfolio Overview"):
+
+            gr.Markdown("## Portfolio Snapshot by Calendar Month")
+
+            with gr.Row():
+                calendar_month_dropdown = gr.Dropdown(
+                    choices=get_calendar_months(),
+                    value=(get_calendar_months()[-1] if len(get_calendar_months()) > 0 else None),
+                    label="Calendar Month (YYYY-MM)"
+                )
+
+                overview_segment_dropdown = gr.Dropdown(
+                    choices=[
+                        "fico_band",
+                        "sourcing_channel",
+                        "city_tier",
+                        "occupation_type"
+                    ],
+                    value="fico_band",
+                    label="Segment (for drill)"
+                )
+
+                gen_overview_btn = gr.Button("Generate Snapshot", variant="primary")
+
+            overview_table = gr.Dataframe(label="Portfolio Overview")
+
+            gen_overview_btn.click(
+                fn=generate_portfolio_overview,
+                inputs=[calendar_month_dropdown, overview_segment_dropdown],
+                outputs=overview_table
+            )
+
+        # =================================================
+        # TAB 5: CHAT WITH YOUR DATA
+        # =================================================
+
+        with gr.TabItem("💬 Chat with Your Data"):
+
+            gr.Markdown(
+                "## Ask questions in plain language and get risk manager insights"
+            )
+
+            with gr.Row():
+                ai_month_dropdown = gr.Dropdown(
+                    choices=get_calendar_months(),
+                    value=(get_calendar_months()[-1] if len(get_calendar_months()) > 0 else None),
+                    label="As Of Month (YYYY-MM)"
+                )
+
+                ai_segment_dropdown = gr.Dropdown(
+                    choices=["", "fico_band", "sourcing_channel", "city_tier", "occupation_type"],
+                    value="",
+                    label="Segment Context"
+                )
+
+            ai_question = gr.Textbox(
+                lines=3,
+                placeholder="Ask about NCL, vintage performance, high-risk segments, or portfolio trends..."
+            )
+
+            ai_chatbot = gr.Chatbot()
+            ai_history = gr.State([])
+
+            ai_ask_btn = gr.Button("Ask AI", variant="primary")
+
+            ai_ask_btn.click(
+                fn=ask_ai_question,
+                inputs=[ai_question, ai_month_dropdown, ai_segment_dropdown, ai_history],
+                outputs=[ai_chatbot, ai_history, ai_question]
+            )
+
+app.launch()