Update appv1.py

appv1.py

@@ -1,111 +1,451 @@
+# app.py
+
 import pandas as pd
 import gradio as gr
 
-from analytics.monitoring_analysis import generate_monitoring_table
+# ---------------------------------------------------
+# HELPERS
+# ---------------------------------------------------
 
-from helper.vintage_helpers import(
+from helper.vintage_helpers import (
     create_booking_vintage
 )
 
-from helper.data_merger import(
+from helper.data_merger import (
     merge_acq_perf
 )
 
+# ---------------------------------------------------
+# METRICS
+# ---------------------------------------------------
+
 from metrics.mix_metrics import (
-    calculate_count_mix,
+    calculate_vintage_mix,
     calculate_limit_mix
 )
 
-# ------------------------
-# Load Data
-# ------------------------
-acq = pd.read_csv("data/acquisition.csv")
-perf = pd.read_csv("data/performance.csv")
+# ---------------------------------------------------
+# ANALYTICS
+# ---------------------------------------------------
+
+from analytics.performance_analysis import (
+    generate_metric_view
+)
+
+# ---------------------------------------------------
+# LOAD DATA
+# ---------------------------------------------------
+
+acq = pd.read_csv(
+    "data/acquisition.csv"
+)
+
+perf = pd.read_csv(
+    "data/performance.csv"
+)
+
+# ---------------------------------------------------
+# CREATE BOOKING VINTAGE
+# ---------------------------------------------------
 
-acq=  create_booking_vintage(
+acq = create_booking_vintage(
     acq,
     booking_date_col="booking_date"
 )
 
-perf = merge_acq_perf(
-    acq,
-    perf
+# ---------------------------------------------------
+# 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
+    # -----------------------------------------
 
-# ------------------------
-# Core Engine
-# ------------------------
-def run_analysis(dataset, analysis_type, group_col):
+    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
+
+# ---------------------------------------------------
+# DYNAMIC DROPDOWNS
+# ---------------------------------------------------
+
+def update_analysis_dropdown(
+    dataset
+):
+
+    # -----------------------------------------
+    # ACQUISITION
+    # -----------------------------------------
 
     if dataset == "Acquisition":
 
-        df = acq
+        return gr.update(
+            choices=[
+                "Portfolio Mix",
+                "Credit Line Concentration"
+            ],
+            value="Portfolio Mix"
+        )
+
+    # -----------------------------------------
+    # PERFORMANCE
+    # -----------------------------------------
+
+    elif dataset == "Performance":
+
+        return gr.update(
+            choices=[
+                "30+@3",
+                "30+@6",
+                "30+@9",
+                "Yr1 NCL"
+            ],
+            value="30+@6"
+        )
 
-        if analysis_type == "Count Mix":
-            return calculate_count_mix(df, group_col)
 
-        elif analysis_type == "Limit Mix":
-            return calculate_limit_mix(df, group_col)
+def update_category_dropdown(
+    dataset
+):
 
-        else:
-            return "Invalid analysis type for Acquisition dataset"
+    # -----------------------------------------
+    # ACQUISITION
+    # -----------------------------------------
+
+    if dataset == "Acquisition":
+
+        return gr.update(
+            choices=[
+                "fico_band",
+                "sourcing_channel",
+                "city_tier",
+                "occupation_type"
+            ],
+            value="fico_band"
+        )
+
+    # -----------------------------------------
+    # PERFORMANCE
+    # -----------------------------------------
 
     elif dataset == "Performance":
 
-        df = perf
+        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
+    # -----------------------------------------
 
-        if analysis_type == "Vintage Monitoring Table":
-            return generate_monitoring_table(df)
+    elif dataset == "Performance":
 
-        else:
-            return "Invalid analysis type for Performance dataset"
+        return run_performance_analysis(
+            metric_name=analysis,
+            view_level=category
+        )
 
     else:
-        return "Invalid dataset selection"
 
+        return pd.DataFrame()
+
+# ---------------------------------------------------
+# GRADIO UI
+# ---------------------------------------------------
 
-# ------------------------
-# Gradio UI
-# ------------------------
 with gr.Blocks() as app:
 
-    gr.Markdown("# 📊 Risk Analytics Manager Agent (V1)")
+    gr.Markdown(
+        "# Risk Analytics Manager Agent"
+    )
 
     with gr.Row():
 
-        dataset = gr.Dropdown(
-            choices=["Acquisition", "Performance"],
-            label="Select Dataset"
+        dataset_dropdown = gr.Dropdown(
+            choices=[
+                "Acquisition",
+                "Performance"
+            ],
+            value="Acquisition",
+            label="Dataset"
         )
 
-        analysis_type = gr.Dropdown(
+        analysis_dropdown = gr.Dropdown(
             choices=[
-                "Count Mix",
-                "Limit Mix",
-                "Vintage Monitoring Table"
+                "Portfolio Mix",
+                "Credit Line Concentration"
             ],
-            label="Select Analysis"
+            value="Portfolio Mix",
+            label="Analysis"
         )
 
-        group_col = gr.Dropdown(
+        category_dropdown = gr.Dropdown(
             choices=[
-                "sourcing_channel",
                 "fico_band",
+                "sourcing_channel",
                 "city_tier",
                 "occupation_type"
             ],
-            label="Group Column (for Mix)"
+            value="fico_band",
+            label="Category / View"
         )
 
-    run_btn = gr.Button("Run Analysis")
+    # -----------------------------------------
+    # 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"
+    )
 
-    output = gr.Dataframe()
+    output_table = gr.Dataframe()
 
-    run_btn.click(
+    run_button.click(
         fn=run_analysis,
-        inputs=[dataset, analysis_type, group_col],
-        outputs=output
+        inputs=[
+            dataset_dropdown,
+            analysis_dropdown,
+            category_dropdown
+        ],
+        outputs=output_table
     )
 
 app.launch()