Update app.py

app.py

@@ -1,146 +1,156 @@
-# =[Modules dan Packages]========================
-
-from flask import Flask,render_template,request,jsonify
-import pandas as pd
-import json
-from joblib import load
-import mysql.connector
-from mysql.connector import Error
-import logging
-
-# =[Variabel Global]=============================
-
-app   = Flask(__name__, static_url_path='/static')
-model = None
-
-# =[Routing]=====================================
-
-# [Routing untuk Halaman Utama atau Home]	
-@app.route("/")
-def beranda():
-    return render_template('index.html')
-
-# [Routing untuk API]
-@app.route("/deteksi", methods=['GET', 'POST'])
-def apiDeteksi():
-    if request.method == 'POST':
-        try:
-            # Extract form data
-            nama = request.form['Nama']
-            posisi_harapan = request.form['Posisi_Harapan']
-            # Extract kategori umur sebagai objek
-            kategori_umur_obj = request.form.get('KategoriUmur')
-
-            # Check if kategori_umur_obj has a value
-            if kategori_umur_obj is not None:
-                # Ubah objek JavaScript menjadi dict Python
-                kategori_umur_values = json.loads(kategori_umur_obj)
-            else:
-                # Jika tidak ada nilai, atur sebagai dictionary kosong
-                kategori_umur_values = {}
-            
-            # Python Flask code to access category values from formData
-            kategori_umur_values = {f"KategoriUmur[{i}]": int(request.form.get(f"KategoriUmur[{i}]")) for i in range(9)}
-            kategori_umur_dict = {f"Kategori_Umur_{i}": kategori_umur_values.get(f"KategoriUmur[{i}]", 0) for i in range(9)}
-            pendidikan = int(request.form['Pendidikan'])
-            lama_pengalaman = int(request.form['Lama_Pengalaman'])
-            kesesuaian_posisi_dg_skills = int(request.form['Kesesuaian_Posisi_Skill'])
-            kesesuaian_posisi_dg_pengalaman = int(request.form['Kesesuaian_Posisi_Pengalaman'])
-
-            # Perform prediction
-            data = {**kategori_umur_dict, 
-                    "Pendidikan": pendidikan,
-                    "Lama_Pengalaman": lama_pengalaman,
-                    "Kesesuaian_Posisi_Skill": kesesuaian_posisi_dg_skills,
-                    "Kesesuaian_Posisi_Pengalaman": kesesuaian_posisi_dg_pengalaman}
-            
-            df_test = pd.DataFrame(data, index=[0])
-            print("Data untuk prediksi:")
-            print(df_test)
-            hasil_prediksi = model.predict(df_test[0:1])[0]
-            # Set up logging
-            logging.basicConfig(level=logging.DEBUG)
-
-            # Inside the apiDeteksi function, add logging statements to print values
-            logging.debug("Nama: %s", nama)
-            logging.debug("Kategori Umur: %s", kategori_umur_dict)
-
-            # Map prediction to a string
-            if hasil_prediksi == 1:
-                hasil_prediksi_str = 'Recommended Candidate'
-            elif hasil_prediksi == 0:
-                hasil_prediksi_str = 'Not Recommended Candidate'
-            else:
-                hasil_prediksi_str = 'Data Tidak Sesuai Dengan Format'
-
-            # Save data to the database
-            try:
-                connection = mysql.connector.connect(host='localhost',
-                                                    database='hasil_prediksi',
-                                                    user='root',
-                                                    password='')
-                if connection.is_connected():
-                    cursor = connection.cursor(prepared=True)
-                    insert_query = ("INSERT INTO klasifikasi "
-                    "(nama, kategori_umur_0, kategori_umur_1, kategori_umur_2, "
-                    "kategori_umur_3, kategori_umur_4, kategori_umur_5, kategori_umur_6, "
-                    "kategori_umur_7, kategori_umur_8, pendidikan, lama_pengalaman, "
-                    "kesesuaian_posisi_dg_skills, kesesuaian_posisi_dg_pengalaman, "
-                    "posisi_harapan, hasil_prediksi) "
-                    "VALUES "
-                    "(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)")
-    
-                    cursor.execute(insert_query, (nama,) + tuple(data.values()) + (posisi_harapan, hasil_prediksi_str))
-                    connection.commit()
-                    print("Data berhasil disimpan ke database.")
-            except Error as e:
-                print("Error while connecting to MySQL", e)
-            finally:
-                if connection.is_connected():
-                    cursor.close()
-                    connection.close()
-                    print("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}")
-            return jsonify({
-                "error": "Error during prediction"
-            })
-
-    # If the request is not POST, render the prediksi.html page
-    return render_template('prediksi.html')
-            
-@app.route("/view_tabel", methods=['GET'])
-def view_tabel():
-    try:
-        connection = mysql.connector.connect(host='localhost',
-                                                database='hasil_prediksi',
-                                                user='root',
-                                                password='')
-        if connection.is_connected():
-            cursor = connection.cursor(dictionary=True)
-            cursor.execute("SELECT * FROM klasifikasi")
-            data_tabel = cursor.fetchall()
-            return render_template('view_tabel.html', data_tabel=data_tabel)
-    except Error as e:
-        print("Error while connecting to MySQL", e)
-    finally:
-        if connection.is_connected():
-            cursor.close()
-            connection.close()
-            print("MySQL connection is closed")
-            
-            
-# =[Main]========================================
-
-if __name__ == '__main__':
-	
-	# Load model yang telah ditraining
-	model = load('modelc45_baru1.model')
-
-	# Run Flask di localhost 
-	app.run(host="0.0.0.0", port=5000, debug=True)
+# =[Modules dan Packages]========================
+
+from flask import Flask,render_template,request,jsonify
+import pandas as pd
+import json
+from joblib import load
+import mysql.connector
+from mysql.connector import Error
+import logging
+
+# =[Variabel Global]=============================
+
+app   = Flask(__name__, static_url_path='/static')
+model = Nonefrom
+
+
+# =[Routing]=====================================
+
+# [Routing untuk Halaman Utama atau Home]	
+@app.route("/")
+def beranda():
+    return render_template('index.html')
+
+# [Routing untuk API]
+@app.route("/deteksi", methods=['GET', 'POST'])
+def apiDeteksi():
+    if request.method == 'POST':
+        try:
+            # Extract form data
+            nama = request.form['Nama']
+            posisi_harapan = request.form['Posisi_Harapan']
+            usia = int(request.form['Usia'])
+            # Extract kategori umur sebagai objek
+            kategori_umur_obj = request.form.get('KategoriUmur')
+
+            # Check if kategori_umur_obj has a value
+            if kategori_umur_obj is not None:
+                # Ubah objek JavaScript menjadi dict Python
+                kategori_umur_values = json.loads(kategori_umur_obj)
+            else:
+                # Jika tidak ada nilai, atur sebagai dictionary kosong
+                kategori_umur_values = {}
+            
+            # Python Flask code to access category values from formData
+            kategori_umur_values = {f"KategoriUmur[{i}]": int(request.form.get(f"KategoriUmur[{i}]")) for i in range(1, 11)}
+            kategori_umur_dict = {f"Kategori_Umur_{i}": kategori_umur_values.get(f"KategoriUmur[{i}]", 0) for i in range(1, 11)}
+
+            pendidikan = int(request.form['Pendidikan'])
+            lama_pengalaman = int(request.form['Lama_Pengalaman'])
+            kesesuaian_posisi_dg_skills = int(request.form['Kesesuaian_Posisi_Skill'])
+            kesesuaian_posisi_dg_pengalaman = int(request.form['Kesesuaian_Posisi_Pengalaman'])
+
+            # Perform prediction
+            data = {**kategori_umur_dict, 
+                    "Pendidikan": pendidikan,
+                    "Lama_Pengalaman": lama_pengalaman,
+                    "Kesesuaian_Posisi_Skill": kesesuaian_posisi_dg_skills,
+                    "Kesesuaian_Posisi_Pengalaman": kesesuaian_posisi_dg_pengalaman}
+            
+            df_test = pd.DataFrame(data, index=[0])
+            print("Data untuk prediksi:")
+            print(df_test)
+            hasil_prediksi = model.predict(df_test[0:1])[0]
+            # Set up logging
+            logging.basicConfig(level=logging.DEBUG)
+
+            # Inside the apiDeteksi function, add logging statements to print values
+            logging.debug("Nama: %s", nama)
+            logging.debug("Kategori Umur: %s", kategori_umur_dict)
+
+            # Map prediction to a string
+            if hasil_prediksi == 1:
+                hasil_prediksi_str = 'Recommended Candidate'
+            elif hasil_prediksi == 0:
+                hasil_prediksi_str = 'Not Recommended Candidate'
+            else:
+                hasil_prediksi_str = 'Data Tidak Sesuai Dengan Format'
+
+            # Save data to the database
+            try:
+                connection = mysql.connector.connect(host='sql12.freesqldatabase.com',
+                                                database='sql12711336',
+                                                user='sql12711336',
+                                                password='ufDVxditnx')
+                if connection.is_connected():
+                    cursor = connection.cursor(prepared=True)
+                    insert_query = ("INSERT INTO klasifikasi "
+                    "(nama, kategori_umur_1, kategori_umur_2, "
+                    "kategori_umur_3, kategori_umur_4, kategori_umur_5, kategori_umur_6, "
+                    "kategori_umur_7, kategori_umur_8, kategori_umur_9, kategori_umur_10, pendidikan, lama_pengalaman, "
+                    "kesesuaian_posisi_dg_skills, kesesuaian_posisi_dg_pengalaman, "
+                    "posisi_harapan, usia, hasil_prediksi) "
+                    "VALUES "
+                    "(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)")
+    
+                    cursor.execute(insert_query, (nama,) + tuple(data.values()) + (posisi_harapan, usia, hasil_prediksi_str))
+                    connection.commit()
+                    print("Data berhasil disimpan ke database.")
+            except Error as e:
+                print("Error while connecting to MySQL", e)
+            finally:
+                if connection.is_connected():
+                    cursor.close()
+                    connection.close()
+                    print("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}")
+            return jsonify({
+                "error": "Error during prediction"
+            })
+
+    # If the request is not POST, render the prediksi.html page
+    return render_template('prediksi.html')
+            
+@app.route("/view_tabel", methods=['GET'])
+def view_tabel():
+    try:
+        connection = mysql.connector.connect(host='sql12.freesqldatabase.com',
+                                                database='sql12711336',
+                                                user='sql12711336',
+                                                password='ufDVxditnx')
+        if connection.is_connected():
+            cursor = connection.cursor(dictionary=True)
+            cursor.execute("SELECT * FROM klasifikasi")
+            data_tabel = cursor.fetchall()
+            
+            # Konversi nilai pendidikan, lama_pengalaman, kesesuaian_posisi_dg_skills, dan kesesuaian_posisi_dg_pengalaman
+            for row in data_tabel:
+                row['pendidikan'] = ">= S1" if row['pendidikan'] == 1 else "<S1"
+                row['lama_pengalaman'] = ">1 Tahun" if row['lama_pengalaman'] == 1 else "<1 Tahun"
+                row['kesesuaian_posisi_dg_skills'] = "Sesuai Posisi Harapan" if row['kesesuaian_posisi_dg_skills'] == 1 else "Tidak Sesuai Posisi Harapan"
+                row['kesesuaian_posisi_dg_pengalaman'] = "Sesuai Posisi Harapan" if row['kesesuaian_posisi_dg_pengalaman'] == 1 else "Tidak Sesuai Posisi Harapan"
+            
+            return render_template('view_tabel.html', data_tabel=data_tabel)
+    except Error as e:
+        print("Error while connecting to MySQL", e)
+    finally:
+        if connection.is_connected():
+            cursor.close()
+            connection.close()
+            print("MySQL connection is closed")
+            
+# =[Main]========================================
+
+if __name__ == '__main__':
+	
+	# Load model yang telah ditraining
+	model = load('model_scratch_2.model')
+
+	# Run Flask di localhost 
+	app.run(host="localhost", port=5000, debug=True)