Upload app.py

app.py

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+import gradio as gr
+import pickle
+import numpy as np
+
+# Load model and scaler
+with open("marketing_model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+with open("scaler.pkl", "rb") as f:
+    scaler = pickle.load(f)
+
+
+def predict_campaign(
+    education, marital_status, income, kidhome, teenhome, recency,
+    wines, fruits, meat, fish, sweets, gold,
+    deals, web, catalog, store, visits,
+    cmp3, cmp4, cmp5, cmp1, cmp2,
+    complain, cost_contact, revenue,
+    age
+):
+
+    total_spending = wines + fruits + meat + fish + sweets + gold
+
+    features = np.array([
+        education, marital_status, income, kidhome, teenhome, recency,
+        wines, fruits, meat, fish, sweets, gold,
+        deals, web, catalog, store, visits,
+        cmp3, cmp4, cmp5, cmp1, cmp2,
+        complain, cost_contact, revenue,
+        age, total_spending
+    ]).reshape(1, -1)
+
+    features = scaler.transform(features)
+
+    prediction = model.predict(features)[0]
+
+    if prediction == 1:
+        return "✅ Customer will accept the marketing campaign"
+    else:
+        return "❌ Customer will NOT accept the campaign"
+
+
+interface = gr.Interface(
+    fn=predict_campaign,
+    inputs=[
+        gr.Number(label="Education"),
+        gr.Number(label="Marital Status"),
+        gr.Number(label="Income"),
+        gr.Number(label="Kidhome"),
+        gr.Number(label="Teenhome"),
+        gr.Number(label="Recency"),
+
+        gr.Number(label="Wine Spending"),
+        gr.Number(label="Fruit Spending"),
+        gr.Number(label="Meat Spending"),
+        gr.Number(label="Fish Spending"),
+        gr.Number(label="Sweet Spending"),
+        gr.Number(label="Gold Spending"),
+
+        gr.Number(label="Deals Purchases"),
+        gr.Number(label="Web Purchases"),
+        gr.Number(label="Catalog Purchases"),
+        gr.Number(label="Store Purchases"),
+        gr.Number(label="Web Visits Per Month"),
+
+        gr.Number(label="Accepted Campaign 3"),
+        gr.Number(label="Accepted Campaign 4"),
+        gr.Number(label="Accepted Campaign 5"),
+        gr.Number(label="Accepted Campaign 1"),
+        gr.Number(label="Accepted Campaign 2"),
+
+        gr.Number(label="Complain"),
+        gr.Number(label="Cost Contact"),
+        gr.Number(label="Revenue"),
+        gr.Number(label="Age"),
+    ],
+    outputs=gr.Textbox(label="Prediction"),
+    title="Sales Analytics & Marketing Automation",
+    description="Predict whether a customer will accept a marketing campaign"
+)
+
+interface.launch()