Update app.py

app.py

@@ -1,13 +1,12 @@
 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:
@@ -15,116 +14,18 @@ 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**
-"""
 
 # =========================
-# 🔹 FILTER FUNCTION
+# Prediction + Full Analysis
 # =========================
-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):
+def analyze_customer(age, gender, tenure, usage, support, delay,
+                     subscription, contract, spend, interaction):
 
     try:
         if model is None:
-            return "Model not loaded ❌", "", "", None, ""
+            return "Model not loaded ❌", "", "", None, None, None
 
+        # Encoding
         gender_val = 1 if gender == "Female" else 0
 
         sub_premium = 1 if subscription == "Premium" else 0
@@ -149,6 +50,7 @@ def predict_churn(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:
@@ -156,14 +58,51 @@ def predict_churn(age, gender, tenure, usage, support, delay,
         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
+        # =========================
+        # 📊 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
+        # =========================
         reasons = []
         if delay > 15: reasons.append("High payment delay")
         if tenure < 6: reasons.append("Low tenure")
@@ -171,95 +110,54 @@ def predict_churn(age, gender, tenure, usage, support, delay,
 
         explanation = "\n".join(reasons) if reasons else "No strong risk indicators"
 
-        return result, f"{prob*100:.2f}%", risk, fig, explanation
+        return result, f"{prob*100:.2f}%", risk, fig1, fig2, fig3, explanation
 
     except Exception as e:
-        return f"Error: {str(e)}", "", "", None, ""
+        return f"Error: {str(e)}", "", "", None, None, None, ""
+
 
 # =========================
-# 🎨 UI
+# UI
 # =========================
 with gr.Blocks() as demo:
 
-    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)
+    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")
             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)
+    spend = gr.Slider(0, 10000, value=2000)
+    interaction = gr.Slider(0, 100, value=20)
 
-        btn = gr.Button("Predict")
+    btn = gr.Button("Analyze Customer")
 
-        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?")
+    gr.Markdown("## 📊 Results")
 
-        btn.click(
-            predict_churn,
-            inputs=[age, gender, tenure, usage, support, delay,
-                    subscription, contract, spend, interaction],
-            outputs=[result, prob, risk, graph, explanation]
-        )
+    result = gr.Textbox(label="Prediction")
+    prob = gr.Textbox(label="Probability")
+    risk = gr.Textbox(label="Risk Level")
 
-    # ---------------------
-    # 📈 INSIGHTS TAB
-    # ---------------------
-    with gr.Tab("📈 Insights"):
-        gr.Markdown("### Feature Importance")
-        gr.Plot(feature_importance())
+    graph1 = gr.Plot(label="Churn Probability")
+    graph2 = gr.Plot(label="Customer Profile")
+    graph3 = gr.Plot(label="Risk Factors")
+
+    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]
+    )
 
-# =========================
-# 🚀 LAUNCH
-# =========================
 demo.launch(debug=True)