Upload 2 files

app (2).py

@@ -0,0 +1,102 @@
+import gradio as gr
+import joblib
+import pandas as pd
+
+# Load trained model
+model = joblib.load("grit_elite_predictor.pkl")
+
+# Prediction function
+def predict(total_spend, recency_days, used_discount, visit_time, num_items, num_prices, num_channels):
+    visit_morning = int(visit_time == "Morning (7–10am)")
+    visit_noon = int(visit_time == "Midday (10am–1pm)")
+    visit_afternoon = int(visit_time == "Afternoon (1–5pm)")
+
+    input_data = pd.DataFrame([{
+        'TotalSpend_Capped': total_spend,
+        'Recency': recency_days,
+        'DiscountPct': 0.20 if used_discount else 0.0,
+        'NumUniqueItems': num_items,
+        'NumUniquePricePoints': num_prices,
+        'NumUniqueChannels': num_channels,
+        'Visit_Morning': visit_morning,
+        'Visit_Noon': visit_noon,
+        'Visit_Afternoon': visit_afternoon,
+    }])
+
+    prob = model.predict_proba(input_data)[0][1]
+    pred = int(prob >= 0.25)
+    return f"Prediction: {'Elite' if pred == 1 else 'Non-Elite'} (Confidence: {prob:.2%})"
+
+# Custom CSS
+custom_css = """
+body {
+    background-color: #fef6ee !important;
+    font-family: 'Raleway', sans-serif;
+}
+h1 {
+    text-align: center;
+    font-size: 32px;
+    color: #151515;
+    margin-top: 10px;
+    margin-bottom: 20px;
+}
+label, .gr-checkbox label, .gr-radio label {
+    color: white !important;
+}
+button {
+    background-color: #4b4b4b !important;
+    color: white !important;
+}
+#logo-container {
+    padding: 20px;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+}
+#logo-container img {
+    max-height: 80px;
+    width: auto;
+    max-width: 100%;
+    object-fit: contain;
+}
+"""
+
+# App layout
+with gr.Blocks(css=custom_css) as demo:
+    gr.Markdown("<h1>Grit Elite Customer Predictor</h1>")
+
+    with gr.Row():
+        with gr.Column():
+            total_spend = gr.Slider(0, 500, step=10, label="Total Spend (First 90 Days)")
+            recency_days = gr.Slider(0, 90, step=1, label="Recency (Days Between First & Last Visit)")
+            used_discount = gr.Checkbox(label="Used Loyalty Discount?")
+            visit_time = gr.Radio(["Morning (7–10am)", "Midday (10am–1pm)", "Afternoon (1–5pm)"], label="Typical Visit Time")
+            num_items = gr.Slider(1, 15, step=1, label="Number of Unique Items")
+            num_prices = gr.Slider(1, 15, step=1, label="Unique Price Points Tried")
+            num_channels = gr.Slider(1, 4, step=1, label="Channels Used (POS, Mobile, Web, etc.)")
+
+            with gr.Row():
+                clear_btn = gr.Button("Clear")
+                submit_btn = gr.Button("Submit")
+
+        with gr.Column():
+            with gr.Group():
+                with gr.Row(elem_id="logo-container"):
+                    gr.Image("GC-Logo-Gold2.png", show_label=False, show_download_button=False)
+                output = gr.Textbox(label="Prediction Output", interactive=False)
+                gr.HTML(
+                    '<iframe style="border-radius:12px" src="https://open.spotify.com/embed/playlist/37A3Lq5UUDp7za3PzRUBed?utm_source=generator" width="100%" height="80" frameBorder="0" allowfullscreen="" allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture" loading="lazy"></iframe>'
+                )
+
+    submit_btn.click(
+        fn=predict,
+        inputs=[total_spend, recency_days, used_discount, visit_time, num_items, num_prices, num_channels],
+        outputs=output
+    )
+    clear_btn.click(
+        fn=lambda: [0, 0, False, None, 1, 1, 1],
+        inputs=[],
+        outputs=[total_spend, recency_days, used_discount, visit_time, num_items, num_prices, num_channels]
+    )
+
+demo.launch()

requirements (2).txt

@@ -0,0 +1,10 @@
+gradio==3.44.1
+Pillow
+yake
+pandas
+scikit-learn
+shap
+xgboost
+matplotlib
+numpy
+streamlit