Update app.py

app.py

@@ -3,46 +3,59 @@ import pickle
 import numpy as np
 import matplotlib.pyplot as plt
 
-# Load model
+# Load trained 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 = "⚠️ Likely to Churn" if pred == 1 else "✅ Stable Customer"
+        result = "Churn ⚠️" if pred == 1 else "No Churn 🙂"
 
-        # Risk label
+        # 🔹 Risk Level
         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 🟢"
 
-        # Styled graph
+        # 🔹 Graph
         fig, ax = plt.subplots()
         ax.bar(["No Churn", "Churn"], [1 - prob, prob])
-        ax.set_title("Churn Probability Analysis")
-        ax.set_ylim(0, 1)
+        ax.set_title("Churn Probability")
+        ax.set_ylabel("Probability")
 
         return result, f"{prob*100:.2f}%", risk, fig
 
@@ -50,49 +63,29 @@ def predict_churn(age, gender, tenure, usage, support, delay,
         return f"Error: {str(e)}", "", "", None
 
 
-# 🎨 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()
+# 🎨 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()