Update app.py

app.py

@@ -35,42 +35,56 @@ def dashboard_analysis(age, gender, tenure, usage, support, delay,
         kpi = f"""
 ### 📊 Customer Summary
 - Age: **{age}**
+- Gender: **{gender}**
 - Tenure: **{tenure} months**
-- Spend: **₹{spend}**
-- Contract: **{contract}**
+- Usage: **{usage}**
+- Support Calls: **{support}**
+- Payment Delay: **{delay}**
 - Subscription: **{subscription}**
+- Contract Type: **{contract}**
+- Total Spend: **₹{spend}**
+- Interaction Score: **{interaction}**
 """
 
-        # Chart 1: Profile
+        # Chart 1: Customer Profile
         fig1, ax1 = plt.subplots()
-        features = ["Age","Tenure","Usage","Support","Delay"]
+        features = ["Age", "Tenure", "Usage", "Support", "Delay"]
         values = [age, tenure, usage, support, delay]
+
         ax1.bar(features, values)
         ax1.set_title("Customer Profile")
         plt.close(fig1)
 
-        # Chart 2: Financial
+        # Chart 2: Financial & Interaction
         fig2, ax2 = plt.subplots()
-        ax2.bar(["Spend","Interaction"], [spend, interaction])
+
+        ax2.bar(["Spend", "Interaction"], [spend, interaction])
         ax2.set_title("Financial & Interaction")
         plt.close(fig2)
 
-        # Chart 3: Risk indicators
+        # Chart 3: Risk Indicators
         risk_scores = [
-            delay/30,
-            support/20,
-            (6-tenure)/6 if tenure < 6 else 0
+            delay / 30,
+            support / 20,
+            (6 - tenure) / 6 if tenure < 6 else 0
         ]
-        labels = ["Delay Risk","Support Risk","Tenure Risk"]
+
+        labels = ["Delay Risk", "Support Risk", "Tenure Risk"]
 
         fig3, ax3 = plt.subplots()
         ax3.bar(labels, risk_scores)
         ax3.set_title("Risk Indicators")
         plt.close(fig3)
 
-        # Chart 4: Subscription level
+        # Chart 4: Subscription Level
         fig4, ax4 = plt.subplots()
-        sub_map = {"Basic":1, "Standard":2, "Premium":3}
+
+        sub_map = {
+            "Basic": 1,
+            "Standard": 2,
+            "Premium": 3
+        }
+
         ax4.bar(["Subscription Level"], [sub_map[subscription]])
         ax4.set_title("Subscription Level")
         plt.close(fig4)
@@ -88,10 +102,11 @@ def predict_churn(age, gender, tenure, usage, support, delay,
                   subscription, contract, spend, interaction):
 
     try:
+
         if model is None:
             return "Model not loaded ❌", "", "", None, ""
 
-        # Convert inputs
+        # Convert Inputs
         age = float(age)
         tenure = float(tenure)
         usage = float(usage)
@@ -102,18 +117,30 @@ def predict_churn(age, gender, tenure, usage, support, delay,
 
         # Encoding
         gender_val = 1 if gender == "Female" else 0
+
         sub_premium = 1 if subscription == "Premium" else 0
         sub_standard = 1 if subscription == "Standard" else 0
+
         contract_monthly = 1 if contract == "Monthly" else 0
         contract_quarterly = 1 if contract == "Quarterly" else 0
 
-        input_data = np.array([[ 
-            age, gender_val, tenure, usage, support, delay,
-            spend, interaction,
-            sub_premium, sub_standard,
-            contract_monthly, contract_quarterly
+        # Model Input
+        input_data = np.array([[
+            age,
+            gender_val,
+            tenure,
+            usage,
+            support,
+            delay,
+            spend,
+            interaction,
+            sub_premium,
+            sub_standard,
+            contract_monthly,
+            contract_quarterly
         ]])
 
+        # Prediction
         pred = model.predict(input_data)[0]
 
         if hasattr(model, "predict_proba"):
@@ -121,8 +148,14 @@ def predict_churn(age, gender, tenure, usage, support, delay,
         else:
             prob = 0.5
 
-        result = "⚠️ Likely to Churn" if pred == 1 else "✅ Stable Customer"
+        # Result
+        result = (
+            "⚠️ Likely to Churn"
+            if pred == 1
+            else "✅ Stable Customer"
+        )
 
+        # Risk Level
         if prob > 0.7:
             risk = "🔴 High Risk"
         elif prob > 0.4:
@@ -130,22 +163,44 @@ def predict_churn(age, gender, tenure, usage, support, delay,
         else:
             risk = "🟢 Low Risk"
 
-        # Probability chart
+        # Probability Chart
         fig, ax = plt.subplots()
-        ax.bar(["No Churn","Churn"], [1-prob, prob])
-        ax.set_ylim(0,1)
+
+        ax.bar(
+            ["No Churn", "Churn"],
+            [1 - prob, prob]
+        )
+
+        ax.set_ylim(0, 1)
         ax.set_title("Prediction Probability")
+
         plt.close(fig)
 
         # Explanation
         reasons = []
-        if delay > 15: reasons.append("High payment delay")
-        if tenure < 6: reasons.append("Low tenure")
-        if support > 5: reasons.append("Too many support calls")
 
-        explanation = "\n".join(reasons) if reasons else "No strong risk indicators"
+        if delay > 15:
+            reasons.append("High payment delay")
 
-        return result, f"{prob*100:.2f}%", risk, fig, explanation
+        if tenure < 6:
+            reasons.append("Low tenure")
+
+        if support > 5:
+            reasons.append("Too many support calls")
+
+        explanation = (
+            "\n".join(reasons)
+            if reasons
+            else "No strong risk indicators"
+        )
+
+        return (
+            result,
+            f"{prob * 100:.2f}%",
+            risk,
+            fig,
+            explanation
+        )
 
     except Exception as e:
         return f"Error: {str(e)}", "", "", None, ""
@@ -158,97 +213,236 @@ with gr.Blocks() as demo:
 
     gr.Markdown("# 🚀 Customer Churn Interactive Dashboard")
 
-    # ---------------------
+    # =====================================================
     # 📊 DASHBOARD TAB
-    # ---------------------
+    # =====================================================
     with gr.Tab("📊 Dashboard"):
 
         with gr.Row():
-            d_age = gr.Number(value=30, label="Age")
-            d_gender = gr.Dropdown(["Male","Female"], value="Male")
-            d_tenure = gr.Number(value=12, label="Tenure")
-            d_usage = gr.Number(value=10, label="Usage")
+
+            d_age = gr.Number(
+                value=30,
+                label="Age"
+            )
+
+            d_gender = gr.Dropdown(
+                ["Male", "Female"],
+                value="Male",
+                label="Gender"
+            )
+
+            d_tenure = gr.Number(
+                value=12,
+                label="Tenure"
+            )
+
+            d_usage = gr.Number(
+                value=10,
+                label="Usage"
+            )
 
         with gr.Row():
-            d_support = gr.Number(value=2, label="Support Calls")
-            d_delay = gr.Number(value=5, label="Payment Delay")
-            d_subscription = gr.Dropdown(["Basic","Standard","Premium"], value="Basic")
-            d_contract = gr.Dropdown(["Monthly","Quarterly","Yearly"], value="Monthly")
 
-        d_spend = gr.Number(value=2000, label="Total Spend")
-        d_interaction = gr.Number(value=20, label="Interaction")
+            d_support = gr.Number(
+                value=2,
+                label="Support Calls"
+            )
+
+            d_delay = gr.Number(
+                value=5,
+                label="Payment Delay"
+            )
+
+            d_subscription = gr.Dropdown(
+                ["Basic", "Standard", "Premium"],
+                value="Basic",
+                label="Subscription"
+            )
+
+            d_contract = gr.Dropdown(
+                ["Monthly", "Quarterly", "Yearly"],
+                value="Monthly",
+                label="Contract Type"
+            )
+
+        d_spend = gr.Number(
+            value=2000,
+            label="Total Spend"
+        )
+
+        d_interaction = gr.Number(
+            value=20,
+            label="Interaction"
+        )
 
         analyze_btn = gr.Button("Analyze Dashboard")
 
         kpi_text = gr.Markdown()
-        chart1 = gr.Plot()
-        chart2 = gr.Plot()
-        chart3 = gr.Plot()
-        chart4 = gr.Plot()
+
+        chart1 = gr.Plot(label="Customer Profile")
+        chart2 = gr.Plot(label="Financial Analysis")
+        chart3 = gr.Plot(label="Risk Indicators")
+        chart4 = gr.Plot(label="Subscription Analysis")
 
         analyze_btn.click(
             dashboard_analysis,
-            inputs=[d_age, d_gender, d_tenure, d_usage, d_support, d_delay,
-                    d_subscription, d_contract, d_spend, d_interaction],
-            outputs=[kpi_text, chart1, chart2, chart3, chart4]
+            inputs=[
+                d_age,
+                d_gender,
+                d_tenure,
+                d_usage,
+                d_support,
+                d_delay,
+                d_subscription,
+                d_contract,
+                d_spend,
+                d_interaction
+            ],
+            outputs=[
+                kpi_text,
+                chart1,
+                chart2,
+                chart3,
+                chart4
+            ]
         )
 
-    # ---------------------
+    # =====================================================
     # 🔍 PREDICTION TAB
-    # ---------------------
+    # =====================================================
     with gr.Tab("🔍 Prediction"):
 
         with gr.Row():
-            age = gr.Number(value=30, label="Age")
-            gender = gr.Dropdown(["Male","Female"], value="Male")
-            tenure = gr.Number(value=12, label="Tenure")
-            usage = gr.Number(value=10, label="Usage")
+
+            age = gr.Number(
+                value=30,
+                label="Age"
+            )
+
+            gender = gr.Dropdown(
+                ["Male", "Female"],
+                value="Male",
+                label="Gender"
+            )
+
+            tenure = gr.Number(
+                value=12,
+                label="Tenure"
+            )
+
+            usage = gr.Number(
+                value=10,
+                label="Usage"
+            )
 
         with gr.Row():
-            support = gr.Number(value=2, label="Support Calls")
-            delay = gr.Number(value=5, label="Payment Delay")
-            subscription = gr.Dropdown(["Basic","Standard","Premium"], value="Basic")
-            contract = gr.Dropdown(["Monthly","Quarterly","Yearly"], value="Monthly")
 
-        spend = gr.Number(value=2000, label="Total Spend")
-        interaction = gr.Number(value=20, label="Interaction")
+            support = gr.Number(
+                value=2,
+                label="Support Calls"
+            )
+
+            delay = gr.Number(
+                value=5,
+                label="Payment Delay"
+            )
+
+            subscription = gr.Dropdown(
+                ["Basic", "Standard", "Premium"],
+                value="Basic",
+                label="Subscription"
+            )
+
+            contract = gr.Dropdown(
+                ["Monthly", "Quarterly", "Yearly"],
+                value="Monthly",
+                label="Contract Type"
+            )
+
+        spend = gr.Number(
+            value=2000,
+            label="Total Spend"
+        )
+
+        interaction = gr.Number(
+            value=20,
+            label="Interaction"
+        )
 
         btn = gr.Button("Predict")
 
         result = gr.Textbox(label="Prediction")
         prob = gr.Textbox(label="Probability")
         risk = gr.Textbox(label="Risk Level")
-        graph = gr.Plot()
-        explanation = gr.Textbox(label="Why this prediction?")
+        graph = gr.Plot(label="Prediction Graph")
+        explanation = gr.Textbox(
+            label="Why this prediction?"
+        )
 
         btn.click(
             predict_churn,
-            inputs=[age, gender, tenure, usage, support, delay,
-                    subscription, contract, spend, interaction],
-            outputs=[result, prob, risk, graph, explanation]
+            inputs=[
+                age,
+                gender,
+                tenure,
+                usage,
+                support,
+                delay,
+                subscription,
+                contract,
+                spend,
+                interaction
+            ],
+            outputs=[
+                result,
+                prob,
+                risk,
+                graph,
+                explanation
+            ]
         )
 
-    # ---------------------
+    # =====================================================
     # 📈 INSIGHTS TAB
-    # ---------------------
+    # =====================================================
     with gr.Tab("📈 Insights"):
 
         if model is not None and hasattr(model, "feature_importances_"):
+
             fig, ax = plt.subplots()
+
             features = [
-                "age","gender","tenure","usage","support","delay",
-                "spend","interaction",
-                "sub_premium","sub_standard",
-                "contract_monthly","contract_quarterly"
+                "Age",
+                "Gender",
+                "Tenure",
+                "Usage",
+                "Support",
+                "Delay",
+                "Spend",
+                "Interaction",
+                "Premium Subscription",
+                "Standard Subscription",
+                "Monthly Contract",
+                "Quarterly Contract"
             ]
-            ax.barh(features, model.feature_importances_)
+
+            ax.barh(
+                features,
+                model.feature_importances_
+            )
+
             ax.set_title("Feature Importance")
+
             plt.close(fig)
+
             gr.Plot(fig)
+
         else:
-            gr.Markdown("⚠️ Feature importance not available")
+            gr.Markdown(
+                "⚠️ Feature importance not available"
+            )
 
 # =========================
-# 🚀 LAUNCH
+# 🚀 Launch App
 # =========================
 demo.launch(debug=True)