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app.py

@@ -47,9 +47,9 @@ def predict_store_total_sales():
     # Convert the extracted data into a Pandas DataFrame
     input_data = pd.DataFrame([sample])
 
-    st.write("Converted Json:", input_data.to_dict(orient='records')[0])
+    #st.write("Converted Json:", input_data.to_dict(orient='records')[0])
     # Make prediction (get log_sales)
-    predicted_log_total_sales = model.predict(input_data)[0]
+    predicted_log_total_sales = model.predict(input_data).tolist()[0]
 
     # Calculate actual price
     #predicted_total_sales = np.exp(predicted_log_total_sales)
@@ -57,7 +57,7 @@ def predict_store_total_sales():
     predicted_total_sales = predicted_log_total_sales
 
     # Convert predicted_price to Python float
-    predicted_total_sales = round(float(predicted_total_sales), 2)
+    #predicted_total_sales = round(float(predicted_total_sales), 2)
     # The conversion above is needed as we convert the model prediction (log total sales) to actual sales using np.exp, which returns predictions as NumPy float32 values.
     # When we send this value directly within a JSON response, Flask's jsonify function encounters a datatype error