Update app.py

app.py

@@ -0,0 +1,87 @@
+import gradio as gr
+import pandas as pd
+from catboost import CatBoostRegressor
+import os
+
+# 1. Load the model
+# Make sure 'catboost_skincare_model.cbm' is uploaded to the same Space
+model = CatBoostRegressor()
+if os.path.exists("catboost_skincare_model.cbm"):
+    model.load_model("catboost_skincare_model.cbm")
+else:
+    print("Model file not found!")
+
+def predict_rating(brand, product, cat1, cat2, origin, age_range, skin_type, concern, ing1, ing2, ing3):
+    # 2. Feature Engineering: Age Mapping
+    age_map = {
+        '18 and Under': 'Teens',
+        '19 - 24': 'Young Adult',
+        '25 - 29': 'Young Adult',
+        '30 - 34': 'Adult',
+        '35 - 39': 'Adult',
+        '40 - 44': 'Mature',
+        '45 and Above': 'Mature'
+    }
+    age_segment = age_map.get(age_range, 'Young Adult')
+
+    # 3. Feature Engineering: Ingredient Flags
+    all_ings = f"{ing1} {ing2} {ing3}".lower()
+    
+    def check_ing(term):
+        return "1" if term in all_ings else "0"
+
+    # 4. Create DataFrame for prediction (Must match training column order)
+    input_df = pd.DataFrame([{
+        'brand_name': brand,
+        'product_name': product,
+        '1st_category': cat1,
+        '2nd_category': cat2,
+        'brand_country_origin': origin,
+        'age': age_range,
+        'skin_type': skin_type,
+        'skin_concern': concern,
+        'ingredient_1': ing1,
+        'ingredient_2': ing2,
+        'ingredient_3': ing3,
+        # Engineered features the model expects
+        'age_segment': age_segment,
+        'has_niacinamide': check_ing('niacinamide'),
+        'has_retinol': check_ing('retinol'),
+        'has_salicylic_acid': check_ing('salicylic'),
+        'has_hyaluronic_acid': check_ing('hyaluronic'),
+        'has_vitamin_c': check_ing('vitamin c'),
+        'skin_profile': f"{skin_type}_{concern}",
+        'product_skin_fit': f"{cat2}_{skin_type}"
+    }])
+
+    # 5. Predict
+    prediction = model.predict(input_df)
+    
+    # Return rounded rating
+    return round(float(prediction[0]), 2)
+
+# 6. Define the Gradio Interface
+# We define the labels so the "View API" button on HF shows clear names
+inputs = [
+    gr.Textbox(label="Brand Name"),
+    gr.Textbox(label="Product Name"),
+    gr.Textbox(label="1st Category"),
+    gr.Textbox(label="2nd Category"),
+    gr.Textbox(label="Country of Origin"),
+    gr.Dropdown(choices=['18 and Under', '19 - 24', '25 - 29', '30 - 34', '35 - 39', '40 - 44', '45 and Above'], label="Age Range"),
+    gr.Textbox(label="Skin Type"),
+    gr.Textbox(label="Skin Concern"),
+    gr.Textbox(label="Ingredient 1"),
+    gr.Textbox(label="Ingredient 2"),
+    gr.Textbox(label="Ingredient 3")
+]
+
+demo = gr.Interface(
+    fn=predict_rating,
+    inputs=inputs,
+    outputs=gr.Number(label="Predicted Rating"),
+    title="Skincare Rating Predictor API"
+)
+
+if __name__ == "__main__":
+    demo.launch()