Update app.py

app.py

@@ -7,6 +7,7 @@ from joblib import load
 import mysql.connector
 from mysql.connector import Error
 import logging
+import traceback
 
 # =[Variabel Global]=============================
 app = Flask(__name__, static_url_path='/static')
@@ -47,6 +48,16 @@ def apiDeteksi():
             kesesuaian_posisi_dg_skills = int(request.form['Kesesuaian_Posisi_Skill'])
             kesesuaian_posisi_dg_pengalaman = int(request.form['Kesesuaian_Posisi_Pengalaman'])
 
+            # Log input data
+            logging.debug(f"Nama: {nama}")
+            logging.debug(f"Posisi Harapan: {posisi_harapan}")
+            logging.debug(f"Usia: {usia}")
+            logging.debug(f"Pendidikan: {pendidikan}")
+            logging.debug(f"Lama Pengalaman: {lama_pengalaman}")
+            logging.debug(f"Kesesuaian Posisi Skill: {kesesuaian_posisi_dg_skills}")
+            logging.debug(f"Kesesuaian Posisi Pengalaman: {kesesuaian_posisi_dg_pengalaman}")
+            logging.debug(f"Kategori Umur: {kategori_umur_dict}")
+
             # Perform prediction
             data = {**kategori_umur_dict,
                     "Pendidikan": pendidikan,
@@ -55,10 +66,6 @@ def apiDeteksi():
                     "Kesesuaian_Posisi_Pengalaman": kesesuaian_posisi_dg_pengalaman}
 
             df_test = pd.DataFrame(data, index=[0])
-            print("Data untuk prediksi:")
-            print(df_test)
-
-            # Debug log to check the DataFrame
             logging.debug(f"DataFrame for prediction:\n{df_test}")
 
             # Check if model is loaded
@@ -68,7 +75,7 @@ def apiDeteksi():
             # Perform prediction
             hasil_prediksi = model.predict(df_test)[0]
 
-            # Debug log to check the prediction result
+            # Log prediction result
             logging.debug(f"Prediction result: {hasil_prediksi}")
 
             # Map prediction to a string
@@ -98,21 +105,22 @@ def apiDeteksi():
 
                     cursor.execute(insert_query, (nama,) + tuple(data.values()) + (posisi_harapan, usia, hasil_prediksi_str))
                     connection.commit()
-                    print("Data berhasil disimpan ke database.")
+                    logging.debug("Data berhasil disimpan ke database.")
             except Error as e:
-                print("Error while connecting to MySQL", e)
+                logging.error(f"Error while connecting to MySQL: {e}")
             finally:
                 if connection.is_connected():
                     cursor.close()
                     connection.close()
-                    print("MySQL connection is closed")
+                    logging.debug("MySQL connection is closed")
 
             # Return prediction result as JSON
             return jsonify({
                 "prediksi": hasil_prediksi_str
             })
         except Exception as e:
-            print(f"Error during prediction: {e}")
+            logging.error(f"Error during prediction: {e}")
+            logging.error(traceback.format_exc())
             return jsonify({
                 "error": "Error during prediction"
             })
@@ -141,12 +149,12 @@ def view_tabel():
 
             return render_template('view_tabel.html', data_tabel=data_tabel)
     except Error as e:
-        print("Error while connecting to MySQL", e)
+        logging.error(f"Error while connecting to MySQL: {e}")
     finally:
         if connection.is_connected():
             cursor.close()
             connection.close()
-            print("MySQL connection is closed")
+            logging.debug("MySQL connection is closed")
 
 # =[Main]========================================
 
@@ -154,9 +162,9 @@ if __name__ == '__main__':
     # Load model yang telah ditraining
     try:
         model = load('model_scratch_2.model')
-        print("Model loaded successfully")
+        logging.debug("Model loaded successfully")
     except Exception as e:
-        print(f"Error loading model: {e}")
+        logging.error(f"Error loading model: {e}")
 
     # Run Flask di localhost
-    app.run(host="localhost", port=5000, debug=True)
+    app.run(host="localhost", port=5000, debug=True)