Update app.py

app.py

@@ -67,6 +67,16 @@ def predict_lifecycle(category, product_name, price, rating, num_reviews, stock_
     prediction = product_model.predict(processed_input)[0]
     return f"Predicted Product Lifecycle: {round(prediction, 2)} years"
 
+def predict_price(product_name, category, base_price, competitor_price, demand, stock, reviews, rating, season, discount):
+    category = label_encoders["Category"].transform([category])[0]
+    demand = label_encoders["Demand"].transform([demand])[0]
+    season = label_encoders["Season"].transform([season])[0]
+    product_name = label_encoders["Product Name"].transform([product_name])[0]
+    features = np.array([base_price, competitor_price, stock, reviews, rating, discount]).reshape(1, -1)
+    scaled_features = scaler.transform(features)
+    final_features = np.concatenate((scaled_features.flatten(), [category, demand, season, product_name])).reshape(1, -1)
+    return f"Optimal Price: ₹{round(dynamic_pricing_model.predict(final_features)[0], 2)}"
+
 def recommend_products(category):
     recommended = recommendation_knn.kneighbors([[category]], return_distance=False)
     return recommended.tolist()