Update app.py

app.py

@@ -3,59 +3,46 @@ import pickle
 import numpy as np
 import matplotlib.pyplot as plt
 
-# Load trained model
+# Load model
 with open("DecisionTreeClassifier.pkl", "rb") as file:
     model = pickle.load(file)
 
-# Prediction function
 def predict_churn(age, gender, tenure, usage, support, delay,
                   subscription, contract, spend, interaction):
     try:
-        # 🔹 Gender encoding
         gender_val = 1 if gender == "Female" else 0
 
-        # 🔹 One-hot encoding (MUST match training columns)
         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 array (order matters!)
         input_data = np.array([[ 
-            age,
-            gender_val,
-            tenure,
-            usage,
-            support,
-            delay,
-            spend,
-            interaction,
-            sub_premium,
-            sub_standard,
-            contract_monthly,
-            contract_quarterly
+            age, gender_val, tenure, usage, support, delay,
+            spend, interaction,
+            sub_premium, sub_standard,
+            contract_monthly, contract_quarterly
         ]])
 
-        # 🔹 Prediction
         pred = model.predict(input_data)[0]
         prob = model.predict_proba(input_data)[0][1]
 
-        result = "Churn ⚠️" if pred == 1 else "No Churn 🙂"
+        result = "⚠️ Likely to Churn" if pred == 1 else "✅ Stable Customer"
 
-        # 🔹 Risk Level
+        # Risk label
         if prob > 0.7:
-            risk = "High Risk 🔴"
+            risk = "🔴 High Risk"
         elif prob > 0.4:
-            risk = "Medium Risk 🟠"
+            risk = "🟠 Medium Risk"
         else:
-            risk = "Low Risk 🟢"
+            risk = "🟢 Low Risk"
 
-        # 🔹 Graph
+        # Styled graph
         fig, ax = plt.subplots()
         ax.bar(["No Churn", "Churn"], [1 - prob, prob])
-        ax.set_title("Churn Probability")
-        ax.set_ylabel("Probability")
+        ax.set_title("Churn Probability Analysis")
+        ax.set_ylim(0, 1)
 
         return result, f"{prob*100:.2f}%", risk, fig
 
@@ -63,29 +50,49 @@ def predict_churn(age, gender, tenure, usage, support, delay,
         return f"Error: {str(e)}", "", "", None
 
 
-# 🎨 Gradio UI
-interface = gr.Interface(
-    fn=predict_churn,
-    inputs=[
-        gr.Number(label="Age"),
-        gr.Dropdown(["Male", "Female"], label="Gender"),
-        gr.Number(label="Tenure"),
-        gr.Number(label="Usage Frequency"),
-        gr.Number(label="Support Calls"),
-        gr.Number(label="Payment Delay"),
-        gr.Dropdown(["Basic", "Standard", "Premium"], label="Subscription Type"),
-        gr.Dropdown(["Monthly", "Quarterly", "Yearly"], label="Contract Length"),
-        gr.Number(label="Total Spend"),
-        gr.Number(label="Last Interaction")
-    ],
-    outputs=[
-        gr.Text(label="Prediction"),
-        gr.Text(label="Churn Probability"),
-        gr.Text(label="Risk Level"),
-        gr.Plot(label="Graph")
-    ],
-    title="📊 Customer Churn Prediction System",
-    description="Enter customer details to predict churn probability and risk level"
-)
-
-interface.launch()
+# 🎨 Custom CSS
+custom_css = """
+body {background-color: #0f172a; color: white;}
+.gradio-container {max-width: 900px; margin: auto;}
+h1 {text-align: center; color: #38bdf8;}
+"""
+
+# 🚀 UI with Blocks
+with gr.Blocks(css=custom_css) as demo:
+
+    gr.Markdown("# 📊 Customer Churn Prediction Dashboard")
+    gr.Markdown("### Analyze customer behavior and predict churn risk")
+
+    with gr.Row():
+        with gr.Column():
+            age = gr.Slider(18, 80, label="Age")
+            gender = gr.Radio(["Male", "Female"], label="Gender")
+            tenure = gr.Slider(0, 60, label="Tenure (Months)")
+            usage = gr.Slider(0, 50, label="Usage Frequency")
+
+        with gr.Column():
+            support = gr.Slider(0, 20, label="Support Calls")
+            delay = gr.Slider(0, 30, label="Payment Delay")
+            subscription = gr.Radio(["Basic", "Standard", "Premium"], label="Subscription Type")
+            contract = gr.Radio(["Monthly", "Quarterly", "Yearly"], label="Contract Length")
+
+    spend = gr.Slider(0, 10000, label="Total Spend")
+    interaction = gr.Slider(0, 100, label="Last Interaction")
+
+    predict_btn = gr.Button("🔍 Predict Churn", variant="primary")
+
+    gr.Markdown("## 📈 Results")
+
+    result = gr.Textbox(label="Prediction")
+    prob = gr.Textbox(label="Churn Probability")
+    risk = gr.Textbox(label="Risk Level")
+    graph = gr.Plot()
+
+    predict_btn.click(
+        fn=predict_churn,
+        inputs=[age, gender, tenure, usage, support, delay,
+                subscription, contract, spend, interaction],
+        outputs=[result, prob, risk, graph]
+    )
+
+demo.launch()