Update app.py

app.py

@@ -1,12 +1,13 @@
 import gradio as gr
 import pickle
 import numpy as np
+import pandas as pd
 import matplotlib.pyplot as plt
 import matplotlib
 matplotlib.use("Agg")
 
 # =========================
-# Load model
+# 🔹 Load Model
 # =========================
 try:
     with open("DecisionTreeClassifier.pkl", "rb") as f:
@@ -14,18 +15,116 @@ try:
 except:
     model = None
 
+# =========================
+# 🔹 Load Dataset (REAL or DUMMY)
+# =========================
+try:
+    data = pd.read_csv("churn_data.csv")  # 🔥 Replace with your dataset
+except:
+    # fallback dummy dataset
+    np.random.seed(42)
+    data = pd.DataFrame({
+        "gender": np.random.choice(["Male","Female"], 300),
+        "tenure": np.random.randint(1, 60, 300),
+        "MonthlyCharges": np.random.randint(500, 8000, 300),
+        "Contract": np.random.choice(["Monthly","Quarterly","Yearly"], 300),
+        "Churn": np.random.choice([0,1], 300)
+    })
+
+# =========================
+# 🔹 KPI FUNCTION
+# =========================
+def get_kpis(df):
+    total = len(df)
+    churn_rate = df["Churn"].mean() * 100
+    avg_spend = df["MonthlyCharges"].mean()
+    avg_tenure = df["tenure"].mean()
+
+    return f"""
+### 📊 Key Metrics
+- Total Customers: **{total}**
+- Churn Rate: **{churn_rate:.2f}%**
+- Avg Spend: **₹{avg_spend:.0f}**
+- Avg Tenure: **{avg_tenure:.1f} months**
+"""
 
 # =========================
-# Prediction + Full Analysis
+# 🔹 FILTER FUNCTION
 # =========================
-def analyze_customer(age, gender, tenure, usage, support, delay,
-                     subscription, contract, spend, interaction):
+def apply_filters(gender, contract):
+    df = data.copy()
+
+    if gender != "All":
+        df = df[df["gender"] == gender]
+
+    if contract != "All":
+        df = df[df["Contract"] == contract]
+
+    return df
+
+# =========================
+# 🔹 CHARTS
+# =========================
+def churn_dist(df):
+    fig, ax = plt.subplots()
+    counts = df["Churn"].value_counts()
+    ax.bar(["No Churn","Churn"], counts)
+    ax.set_title("Churn Distribution")
+    plt.close(fig)
+    return fig
+
+def contract_chart(df):
+    fig, ax = plt.subplots()
+    pd.crosstab(df["Contract"], df["Churn"]).plot(kind="bar", ax=ax)
+    ax.set_title("Churn by Contract")
+    plt.close(fig)
+    return fig
+
+def tenure_chart(df):
+    fig, ax = plt.subplots()
+    ax.scatter(df["tenure"], df["Churn"])
+    ax.set_title("Tenure vs Churn")
+    plt.close(fig)
+    return fig
+
+def risk_pie(df):
+    fig, ax = plt.subplots()
+    counts = df["Churn"].value_counts()
+    ax.pie(counts, labels=["No Churn","Churn"], autopct="%1.1f%%")
+    ax.set_title("Risk Segmentation")
+    plt.close(fig)
+    return fig
+
+# =========================
+# 🔹 FEATURE IMPORTANCE
+# =========================
+def feature_importance():
+    if model is None or not hasattr(model, "feature_importances_"):
+        return None
+
+    features = [
+        "age","gender","tenure","usage","support","delay",
+        "spend","interaction",
+        "sub_premium","sub_standard",
+        "contract_monthly","contract_quarterly"
+    ]
+
+    fig, ax = plt.subplots()
+    ax.barh(features, model.feature_importances_)
+    ax.set_title("Feature Importance")
+    plt.close(fig)
+    return fig
+
+# =========================
+# 🔹 PREDICTION
+# =========================
+def predict_churn(age, gender, tenure, usage, support, delay,
+                 subscription, contract, spend, interaction):
 
     try:
         if model is None:
-            return "Model not loaded ❌", "", "", None, None, None
+            return "Model not loaded ❌", "", "", None, ""
 
-        # Encoding
         gender_val = 1 if gender == "Female" else 0
 
         sub_premium = 1 if subscription == "Premium" else 0
@@ -50,7 +149,6 @@ def analyze_customer(age, gender, tenure, usage, support, delay,
 
         result = "⚠️ Likely to Churn" if pred == 1 else "✅ Stable Customer"
 
-        # Risk level
         if prob > 0.7:
             risk = "🔴 High Risk"
         elif prob > 0.4:
@@ -58,51 +156,14 @@ def analyze_customer(age, gender, tenure, usage, support, delay,
         else:
             risk = "🟢 Low Risk"
 
-        # =========================
-        # 📊 Chart 1: Probability
-        # =========================
-        fig1, ax1 = plt.subplots()
-        ax1.bar(["No Churn", "Churn"], [1-prob, prob])
-        ax1.set_ylim(0, 1)
-        ax1.set_title("Churn Probability")
-        plt.close(fig1)
-
-        # =========================
-        # 📊 Chart 2: Feature Values
-        # =========================
-        features = [
-            "Age","Tenure","Usage","Support",
-            "Delay","Spend","Interaction"
-        ]
-        values = [
-            age, tenure, usage, support,
-            delay, spend, interaction
-        ]
-
-        fig2, ax2 = plt.subplots()
-        ax2.barh(features, values)
-        ax2.set_title("Customer Profile")
-        plt.close(fig2)
-
-        # =========================
-        # 📊 Chart 3: Risk Indicators
-        # =========================
-        risk_scores = [
-            delay/30,
-            support/20,
-            (6-tenure)/6 if tenure < 6 else 0
-        ]
-
-        labels = ["Payment Delay", "Support Calls", "Low Tenure"]
-
-        fig3, ax3 = plt.subplots()
-        ax3.bar(labels, risk_scores)
-        ax3.set_title("Risk Factors")
-        plt.close(fig3)
-
-        # =========================
-        # 🧠 Explanation
-        # =========================
+        # probability chart
+        fig, ax = plt.subplots()
+        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")
@@ -110,54 +171,95 @@ def analyze_customer(age, gender, tenure, usage, support, delay,
 
         explanation = "\n".join(reasons) if reasons else "No strong risk indicators"
 
-        return result, f"{prob*100:.2f}%", risk, fig1, fig2, fig3, explanation
+        return result, f"{prob*100:.2f}%", risk, fig, explanation
 
     except Exception as e:
-        return f"Error: {str(e)}", "", "", None, None, None, ""
-
+        return f"Error: {str(e)}", "", "", None, ""
 
 # =========================
-# UI
+# 🎨 UI
 # =========================
 with gr.Blocks() as demo:
 
-    gr.Markdown("# 🚀 Customer Churn Interactive Analyzer")
-
-    with gr.Row():
-        with gr.Column():
-            age = gr.Slider(18, 80, value=30, label="Age")
-            gender = gr.Radio(["Male", "Female"], value="Male")
-            tenure = gr.Slider(0, 60, value=12, label="Tenure")
-            usage = gr.Slider(0, 50, value=10, label="Usage")
-
-        with gr.Column():
-            support = gr.Slider(0, 20, value=2, label="Support Calls")
-            delay = gr.Slider(0, 30, value=5, label="Payment Delay")
+    gr.Markdown("# 🚀 Customer Churn Analytics Dashboard")
+
+    # ---------------------
+    # 📊 DASHBOARD TAB
+    # ---------------------
+    with gr.Tab("📊 Dashboard"):
+
+        gender_filter = gr.Dropdown(["All","Male","Female"], value="All", label="Filter by Gender")
+        contract_filter = gr.Dropdown(["All","Monthly","Quarterly","Yearly"], value="All", label="Filter by Contract")
+
+        kpi_text = gr.Markdown()
+        chart1 = gr.Plot()
+        chart2 = gr.Plot()
+        chart3 = gr.Plot()
+        chart4 = gr.Plot()
+
+        def update_dashboard(g, c):
+            df = apply_filters(g, c)
+            return (
+                get_kpis(df),
+                churn_dist(df),
+                contract_chart(df),
+                tenure_chart(df),
+                risk_pie(df)
+            )
+
+        gender_filter.change(update_dashboard, [gender_filter, contract_filter],
+                             [kpi_text, chart1, chart2, chart3, chart4])
+
+        contract_filter.change(update_dashboard, [gender_filter, contract_filter],
+                               [kpi_text, chart1, chart2, chart3, chart4])
+
+        # initial load
+        demo.load(update_dashboard, [gender_filter, contract_filter],
+                  [kpi_text, chart1, chart2, chart3, chart4])
+
+    # ---------------------
+    # 🔍 PREDICTION TAB
+    # ---------------------
+    with gr.Tab("🔍 Prediction"):
+
+        with gr.Row():
+            age = gr.Slider(18,80,value=30)
+            gender = gr.Radio(["Male","Female"], value="Male")
+            tenure = gr.Slider(0,60,value=12)
+            usage = gr.Slider(0,50,value=10)
+
+        with gr.Row():
+            support = gr.Slider(0,20,value=2)
+            delay = gr.Slider(0,30,value=5)
             subscription = gr.Radio(["Basic","Standard","Premium"], value="Basic")
             contract = gr.Radio(["Monthly","Quarterly","Yearly"], value="Monthly")
 
-    spend = gr.Slider(0, 10000, value=2000)
-    interaction = gr.Slider(0, 100, value=20)
-
-    btn = gr.Button("Analyze Customer")
+        spend = gr.Slider(0,10000,value=2000)
+        interaction = gr.Slider(0,100,value=20)
 
-    gr.Markdown("## 📊 Results")
+        btn = gr.Button("Predict")
 
-    result = gr.Textbox(label="Prediction")
-    prob = gr.Textbox(label="Probability")
-    risk = gr.Textbox(label="Risk Level")
+        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?")
 
-    graph1 = gr.Plot(label="Churn Probability")
-    graph2 = gr.Plot(label="Customer Profile")
-    graph3 = gr.Plot(label="Risk Factors")
+        btn.click(
+            predict_churn,
+            inputs=[age, gender, tenure, usage, support, delay,
+                    subscription, contract, spend, interaction],
+            outputs=[result, prob, risk, graph, explanation]
+        )
 
-    explanation = gr.Textbox(label="Why this prediction?")
-
-    btn.click(
-        analyze_customer,
-        inputs=[age, gender, tenure, usage, support, delay,
-                subscription, contract, spend, interaction],
-        outputs=[result, prob, risk, graph1, graph2, graph3, explanation]
-    )
+    # ---------------------
+    # 📈 INSIGHTS TAB
+    # ---------------------
+    with gr.Tab("📈 Insights"):
+        gr.Markdown("### Feature Importance")
+        gr.Plot(feature_importance())
 
+# =========================
+# 🚀 LAUNCH
+# =========================
 demo.launch(debug=True)