app.py

import gradio as gr
import pickle
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use("Agg")

# =========================
# 🔹 Load Model
# =========================
try:
    with open("DecisionTreeClassifier.pkl", "rb") as f:
        model = pickle.load(f)
except:
    model = None


# =========================
# 🔹 DASHBOARD ANALYSIS
# =========================
def dashboard_analysis(age, gender, tenure, usage, support, delay,
                      subscription, contract, spend, interaction):

    try:
        # Convert inputs
        age = float(age)
        tenure = float(tenure)
        usage = float(usage)
        support = float(support)
        delay = float(delay)
        spend = float(spend)
        interaction = float(interaction)

        # KPI Summary
        kpi = f"""
### 📊 Customer Summary
- Age: **{age}**
- Gender: **{gender}**
- Tenure: **{tenure} months**
- Usage: **{usage}**
- Support Calls: **{support}**
- Payment Delay: **{delay}**
- Subscription: **{subscription}**
- Contract Type: **{contract}**
- Total Spend: **₹{spend}**
- Interaction Score: **{interaction}**
"""

        # Chart 1: Customer Profile
        fig1, ax1 = plt.subplots()
        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 & Interaction
        fig2, ax2 = plt.subplots()

        ax2.bar(["Spend", "Interaction"], [spend, interaction])
        ax2.set_title("Financial & Interaction")
        plt.close(fig2)

        # Chart 3: Risk Indicators
        risk_scores = [
            delay / 30,
            support / 20,
            (6 - tenure) / 6 if tenure < 6 else 0
        ]

        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
        fig4, ax4 = plt.subplots()

        sub_map = {
            "Basic": 1,
            "Standard": 2,
            "Premium": 3
        }

        ax4.bar(["Subscription Level"], [sub_map[subscription]])
        ax4.set_title("Subscription Level")
        plt.close(fig4)

        return kpi, fig1, fig2, fig3, fig4

    except Exception as e:
        return f"Error: {str(e)}", None, None, None, None


# =========================
# 🔹 PREDICTION FUNCTION
# =========================
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
        age = float(age)
        tenure = float(tenure)
        usage = float(usage)
        support = float(support)
        delay = float(delay)
        spend = float(spend)
        interaction = float(interaction)

        # 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

        # 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"):
            prob = model.predict_proba(input_data)[0][1]
        else:
            prob = 0.5

        # Result
        result = (
            "⚠️ Likely to Churn"
            if pred == 1
            else "✅ Stable Customer"
        )

        # Risk Level
        if prob > 0.7:
            risk = "🔴 High Risk"
        elif prob > 0.4:
            risk = "🟠 Medium Risk"
        else:
            risk = "🟢 Low Risk"

        # 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")

        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, ""


# =========================
# 🎨 UI
# =========================
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",
                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",
                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(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
            ]
        )

    # =====================================================
    # 🔍 PREDICTION TAB
    # =====================================================
    with gr.Tab("🔍 Prediction"):

        with gr.Row():

            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",
                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(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
            ]
        )

    # =====================================================
    # 📈 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",
                "Premium Subscription",
                "Standard Subscription",
                "Monthly Contract",
                "Quarterly Contract"
            ]

            ax.barh(
                features,
                model.feature_importances_
            )

            ax.set_title("Feature Importance")

            plt.close(fig)

            gr.Plot(fig)

        else:
            gr.Markdown(
                "⚠️ Feature importance not available"
            )

# =========================
# 🚀 Launch App
# =========================
demo.launch(debug=True)