initial push (app.py, model, requirements)

shelfy_purchase_rate_v2/app.py

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+import gradio as gr
+import pandas as pd
+import joblib
+
+# Load model
+model = joblib.load("shelfy_purchase_rate_v2.joblib")
+
+def predict_conversion(product_name, views, carts, unique_users, avg_price, carts_lag_1, carts_lag_3):
+    # Convert to float (Gradio passes strings)
+    views = float(views or 0)
+    carts = float(carts or 0) 
+    unique_users = float(unique_users or 0)
+    avg_price = float(avg_price or 0)
+    carts_lag_1 = float(carts_lag_1 or 0)
+    carts_lag_3 = float(carts_lag_3 or 0)
+    
+    # Calculate ratios (same as training)
+    cart_intent = carts / (views + 1)
+    user_intensity = unique_users / (views + 1)
+    
+    data = pd.DataFrame({
+        'views': [views],
+        'carts': [carts],
+        'unique_users': [unique_users],
+        'avg_price': [avg_price],
+        'carts_lag_1': [carts_lag_1],
+        'carts_lag_3': [carts_lag_3],
+        'cart_intent': [cart_intent],
+        'user_intensity': [user_intensity]
+    })
+    
+    future_rate = model.predict(data)[0]
+    
+    return (
+        f"{future_rate:.2%}", 
+        f"${future_rate * views * avg_price:.0f}",
+        "Good Demand" if future_rate >= 0.025 else "Medium Demand" if future_rate >= 0.015 else "Low Demand"
+    )
+
+demo = gr.Interface(
+    fn=predict_conversion,
+    inputs=[
+        gr.Textbox("iPhone Case", label="Product Name"),
+        gr.Number(1500, label="Views (Today)"), 
+        gr.Number(45, label="Carts (Today)"),
+        gr.Number(320, label="Unique Users"),
+        gr.Number(26, label="Avg Price $"),
+        gr.Number(40, label="Yesterday Carts"), 
+        gr.Number(35, label="3 Days Ago Carts")
+    ],
+    outputs=[
+        gr.Label(label="Predicted Conversion (Tomorrow)"),
+        gr.Label(label="Revenue Potential"), 
+        gr.Label(label="Demand")
+    ],
+    title="🛒 Shelfy: Tomorrow's Best Sellers",
+    description="Top 10% predictions captured 25.4% of sales!"
+)
+
+demo.launch()

shelfy_purchase_rate_v2/requirements.txt

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+gradio==4.44.0
+xgboost
+pandas
+joblib
+scikit-learn

shelfy_purchase_rate_v2/shelfy_purchase_rate_v2.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9c69101767b9c73a12c57ceb042cd54d362f35475b87b8883cd2aec1c7884e24
+size 798259