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fix: memperbaiki predict tomat, feact:menambahkan file vercel

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muhamad fais aizat 2025-05-15 13:21:06 +07:00
parent cdf9a4ddb3
commit 6d38369c55
7 changed files with 162 additions and 106 deletions
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backend/config/__pycache__/db.cpython-310.pyc
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backend/config/db.py
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@ -1,7 +1,7 @@
from sqlalchemy.orm import sessionmaker
from sqlalchemy import create_engine, MetaData
engine = create_engine("mysql+pymysql://root@localhost:3306/predict")
engine = create_engine("mysql+pymysql://uupkixae2jbw2zse:1bFkfd8pfZ6EZhphAPQa@bdcaorplf9ct8apn6n5o-mysql.services.clever-cloud.com:3306/bdcaorplf9ct8apn6n5o")
meta = MetaData()
conn = engine.connect()

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backend/requirements.txt Normal file
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backend/routes/__pycache__/predictTomat.cpython-310.pyc
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backend/routes/predictTomat.py
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@ -24,13 +24,13 @@ async def read_data(db: Session = Depends(get_db)):
query = text("""
SELECT
(SELECT pt.tanggal
FROM predict.price_tomat AS pt
JOIN predict.result_predict AS rp ON pt.id = rp.id
FROM price_tomat AS pt
JOIN result_predict AS rp ON pt.id = rp.id
ORDER BY rp.id ASC
LIMIT 1 OFFSET 29) AS tanggal_old,
(SELECT tanggal
FROM predict.price_tomat
FROM price_tomat
ORDER BY tanggal DESC
LIMIT 1) AS tanggal_new;
""")
@ -50,125 +50,167 @@ async def read_data(db: Session = Depends(get_db)):
@predict_router.get("/price", response_model=dict, dependencies=[Depends(verify_token)])
def predict_price(db: Session = Depends(get_db)):
existing_data = db.execute(select(resultPredict)).fetchone()
if existing_data:
db.execute(delete(resultPredict)) # Hapus semua data lama
db.commit()
# Ambil data dari database
data = db.execute(select(priceTomat)).fetchall()
settings = db.execute(select(settingPredict).where(settingPredict.c.status == True)).fetchone()
if not data:
raise HTTPException(status_code=404, detail="Data harga tomat tidak ditemukan")
if not settings:
raise HTTPException(status_code=400, detail="Tidak ada konfigurasi prediksi yang aktif")
# Konversi data ke DataFrame
df = pd.DataFrame([{
"id": item.id, # Simpan ID
"Tanggal": item.tanggal,
"Pasar_Bandung": item.pasar_bandung,
"Pasar_Ngunut": item.pasar_ngunut,
"Pasar_Ngemplak": item.pasar_ngemplak,
"RataRata_Kemarin": item.ratarata_kemarin,
"RataRata_Sekarang": item.ratarata_sekarang
} for item in data])
if df.shape[0] < 3:
raise HTTPException(status_code=400, detail="Data tidak cukup untuk melakukan prediksi")
# Preprocessing data
kolom_numerik = ['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'RataRata_Kemarin', 'RataRata_Sekarang']
df[kolom_numerik] = df[kolom_numerik].apply(pd.to_numeric, errors='coerce')
df[kolom_numerik] = df[kolom_numerik].replace(0, np.nan)
# Interpolasi nilai kosong (0 yang sudah jadi NaN)
df[kolom_numerik] = df[kolom_numerik].interpolate(method='linear', limit_direction='both')
# Drop jika masih ada NaN (misalnya di ujung data)
df.dropna(inplace=True)
try:
existing_data = db.execute(select(resultPredict)).fetchone()
if existing_data:
db.execute(delete(resultPredict)) # Hapus semua data lama
db.commit()
# Ambil data dari database
data = db.execute(select(priceTomat)).fetchall()
settings = db.execute(select(settingPredict).where(settingPredict.c.status == True)).fetchone()
if not data:
raise HTTPException(status_code=404, detail="Data harga tomat tidak ditemukan")
if not settings:
raise HTTPException(status_code=400, detail="Tidak ada konfigurasi prediksi yang aktif")
# Konversi data ke DataFrame
df = pd.DataFrame([{
"id": item.id, # Simpan ID
"Tanggal": item.tanggal,
"Pasar_Bandung": item.pasar_bandung,
"Pasar_Ngunut": item.pasar_ngunut,
"Pasar_Ngemplak": item.pasar_ngemplak,
"RataRata_Kemarin": item.ratarata_kemarin,
"RataRata_Sekarang": item.ratarata_sekarang
} for item in data])
if df.shape[0] < 3:
raise HTTPException(status_code=400, detail="Data tidak cukup untuk melakukan prediksi")
# Preprocessing data
kolom_numerik = ['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'RataRata_Kemarin', 'RataRata_Sekarang']
df[kolom_numerik] = df[kolom_numerik].apply(pd.to_numeric, errors='coerce')
df[kolom_numerik] = df[kolom_numerik].replace(0, np.nan)
# Interpolasi nilai kosong (0 yang sudah jadi NaN)
df[kolom_numerik] = df[kolom_numerik].interpolate(method='linear', limit_direction='both')
# Drop jika masih ada NaN (misalnya di ujung data)
df.dropna(inplace=True)
df['Tanggal'] = pd.to_datetime(df['Tanggal'])
# df['Harga_2Hari_Lalu'] = df['Harga_Kemarin'].shift(1)
df.dropna(inplace=True)
# Normalisasi Data
scaler = StandardScaler()
df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'RataRata_Kemarin', 'RataRata_Sekarang']] = scaler.fit_transform(
df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'RataRata_Kemarin', 'RataRata_Sekarang']]
)
df['Tanggal'] = pd.to_datetime(df['Tanggal'])
# df['Harga_2Hari_Lalu'] = df['Harga_Kemarin'].shift(1)
df.dropna(inplace=True)
# Normalisasi Data
scaler = StandardScaler()
df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'RataRata_Kemarin', 'RataRata_Sekarang']] = scaler.fit_transform(
df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'RataRata_Kemarin', 'RataRata_Sekarang']]
)
X = df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'RataRata_Kemarin', ]].values
y = df['RataRata_Sekarang'].values
ids = df['id'].values
tanggal = df['Tanggal'].values
X = df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'RataRata_Kemarin', ]].values
y = df['RataRata_Sekarang'].values
ids = df['id'].values
tanggal = df['Tanggal'].values
X_train, X_test, y_train, y_test, id_train, id_test, tanggal_train, tanggal_test = train_test_split(
X, y, ids, tanggal, test_size=0.2, shuffle=False
)
X_train, X_test, y_train, y_test, id_train, id_test, tanggal_train, tanggal_test = train_test_split(
X, y, ids, tanggal, test_size=0.2, shuffle=False
)
# Ambil parameter model dari database
kernel = settings.nama_kernel
C = float(settings.nilai_c) if settings.nilai_c is not None else 1.0
gamma = float(settings.nilai_gamma) if settings.nilai_gamma not in [None, "auto", "scale"] else settings.nilai_gamma
epsilon = float(settings.nilai_epsilon) if settings.nilai_epsilon is not None else 0.1
degree = int(settings.nilai_degree) if settings.nilai_degree is not None else 3
coef0 = float(settings.nilai_coef) if settings.nilai_coef is not None else 0.0
# Ambil parameter model dari database
kernel = settings.nama_kernel
C = float(settings.nilai_c) if settings.nilai_c is not None else 1.0
gamma = float(settings.nilai_gamma) if settings.nilai_gamma not in [None, "auto", "scale"] else settings.nilai_gamma
epsilon = float(settings.nilai_epsilon) if settings.nilai_epsilon is not None else 0.1
degree = int(settings.nilai_degree) if settings.nilai_degree is not None else 3
coef0 = float(settings.nilai_coef) if settings.nilai_coef is not None else 0.0
if kernel == "linear":
svr = SVR(kernel="linear", C=C, epsilon=epsilon)
if kernel == "linear":
svr = SVR(kernel="linear", C=C, epsilon=epsilon)
elif kernel == "rbf":
svr = SVR(kernel="rbf", C=C, gamma=gamma, epsilon=epsilon)
elif kernel == "rbf":
svr = SVR(kernel="rbf", C=C, gamma=gamma, epsilon=epsilon)
elif kernel == "sigmoid":
svr = SVR(kernel="sigmoid", C=C, gamma=gamma, coef0=coef0, epsilon=epsilon)
elif kernel == "sigmoid":
svr = SVR(kernel="sigmoid", C=C, gamma=gamma, coef0=coef0, epsilon=epsilon)
elif kernel == "poly":
svr = SVR(kernel="poly", C=C, gamma=gamma, coef0=coef0, degree=degree, epsilon=epsilon)
elif kernel == "poly":
svr = SVR(kernel="poly", C=C, gamma=gamma, coef0=coef0, degree=degree, epsilon=epsilon)
# Latih model dengan data latih
svr.fit(X_train, y_train)
# Latih model dengan data latih
svr.fit(X_train, y_train)
# Prediksi untuk data uji
y_pred = svr.predict(X_test)
# Evaluasi Model
mae = mean_absolute_error(y_test, y_pred)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
mape = mean_absolute_percentage_error(y_test, y_pred)
# Prediksi untuk data uji
y_pred = svr.predict(X_test)
# Evaluasi Model
mae = mean_absolute_error(y_test, y_pred)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
mape = mean_absolute_percentage_error(y_test, y_pred)
jumlah_data_dikirim = 0
# Gabungkan hasil prediksi ke data uji
for i in range(len(y_pred)):
id_tomat = id_test[i]
hasil = y_pred[i]
jumlah_data_dikirim = 0
# Gabungkan hasil prediksi ke data uji
# for i in range(len(y_pred)):
# id_tomat = id_test[i]
# hasil = y_pred[i]
# Invers hasil prediksi
dummy_row = np.zeros((1, 5)) # [0, 0, 0, 0, 0, hasil_prediksi]
dummy_row[0][4] = hasil
prediksi_asli = float(scaler.inverse_transform(dummy_row)[0][4])
# # Invers hasil prediksi
# dummy_row = np.zeros((1, 5)) # [0, 0, 0, 0, 0, hasil_prediksi]
# dummy_row[0][4] = hasil
# prediksi_asli = float(scaler.inverse_transform(dummy_row)[0][4])
existing = db.execute(select(resultPredict).where(resultPredict.c.id == id_tomat)).fetchone()
if existing:
# existing = db.execute(select(resultPredict).where(resultPredict.c.id == id_tomat)).fetchone()
# if existing:
# db.execute(
# resultPredict.update()
# .where(resultPredict.c.id == id_tomat)
# .values(hasil_prediksi=prediksi_asli)
# )
# else:
# db.execute(insert(resultPredict).values(id=id_tomat, hasil_prediksi=prediksi_asli))
# jumlah_data_dikirim += 1
insert_data = []
update_data = []
for i in range(len(y_pred)):
id_tomat = id_test[i]
hasil = y_pred[i]
print("start prediction", id_tomat, hasil)
# Invers hasil prediksi
dummy_row = np.zeros((1, 5))
dummy_row[0][4] = hasil
prediksi_asli = float(scaler.inverse_transform(dummy_row)[0][4])
insert_data.append({
"id": id_tomat,
"hasil_prediksi": prediksi_asli
})
print("print data keseluruhan", insert_data)
# Bulk insert
if insert_data:
db.execute(insert(resultPredict), insert_data)
# Bulk update (looped, karena SQLAlchemy core tidak punya bulk update langsung)
for item in update_data:
db.execute(
resultPredict.update()
.where(resultPredict.c.id == id_tomat)
.values(hasil_prediksi=prediksi_asli)
.where(resultPredict.c.id == item["id"])
.values(hasil_prediksi=item["hasil_prediksi"])
)
else:
db.execute(insert(resultPredict).values(id=id_tomat, hasil_prediksi=prediksi_asli))
jumlah_data_dikirim += 1
jumlah_data_dikirim += len(insert_data) + len(update_data)
db.commit()
return {
"Kernel": kernel,
"Evaluasi": { "MAE": mae, "RMSE": rmse, "MAPE": mape },
"Jumlah_data_dikirim": jumlah_data_dikirim,
"Pesan": "Prediksi seluruh data berhasil disimpan ke database"
}
db.commit()
return {
"Kernel": kernel,
"Evaluasi": { "MAE": mae, "RMSE": rmse, "MAPE": mape },
"Jumlah_data_dikirim": jumlah_data_dikirim,
"Pesan": "Prediksi seluruh data berhasil disimpan ke database"
}
except Exception as e:
db.rollback() # penting!
print(f"Error: {e}")
finally:
db.close()

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backend/vercel.json Normal file
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@ -0,0 +1,14 @@
{
"builds":[
{
"src": "index.py",
"use": "@vercel/python"
}
],
"routes": [
{
"src": "/(.*)",
"dest": "index.py"
}
]
}

2
frontend/src/pages/LandingPage/ViewGrafik.jsx
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@ -71,7 +71,7 @@ const ViewGrafik = ({ date, setDate, dataYAxis, setDataYAxis, priceType, setPric
if (!!date && !!tempPriceType) {
if (!date && !tempPriceType) {
setTabelDataAktual([]);
setTabelDataPredict([]);