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app.py

@@ -33,7 +33,7 @@ def predict_store_total_sales():
     # Extract relevant features from the JSON data
     sample = {
         'Product_Weight': store_data['product_weight'],
-        'Product_Sugar_Content': store_data['product_suger_content'],
+        'Product_Sugar_Content': store_data['product_sugar_content'],
         'Product_Allocated_Area': store_data['product_allocated_area'],
         'Product_Type': store_data['product_type'],
         'Product_MRP': store_data['product_mrp'],
@@ -51,8 +51,6 @@ def predict_store_total_sales():
     # Make prediction (get log_sales)
     predicted_log_total_sales = model.predict(input_data).tolist()[0]
 
-    st.write("predicted_log_total_sales:", {predicted_log_total_sales})
-
     # Calculate actual price
     #predicted_total_sales = np.exp(predicted_log_total_sales)