ada

main.py

@@ -60,11 +60,11 @@ async def root():
     return {"message": "Hello, World!", "ngrok_url": ngrok_url}
 
 
-# #-----------------------------------------------------
-#
+#-----------------------------------------------------
+
 data_processor = DataProcessing()
 data_train_pred = TrainPred()
-#
+
 import random
 def preprocessing(filepath):
     folder_path = './static/temporary'
@@ -122,115 +122,3 @@ def get_images():
         urls.append(url)
         bentuk, persentase = data_train_pred.prediction(selected_model)
     return {'urls': urls, 'bentuk_wajah':bentuk[0], 'persen':persentase}
-
-
-## -------------------------------------------------------------------------
-##                   API UNTUK MELAKUKAN PROSES TRAINING
-## -------------------------------------------------------------------------
-
-# Model pydantic untuk validasi body
-class TrainingParams(BaseModel):
-    optimizer: str
-    epoch: int
-    batchSize: int
-
-@app.post('/upload/dataset', tags=["Training"])
-async def upload_data(dataset: UploadFile):
-    if dataset.filename == '':
-        raise HTTPException(status_code=400, detail='No file selected for uploading')
-
-    # Buat path lengkap untuk menyimpan file
-    file_path = os.path.join(UPLOAD_FOLDER, dataset.filename)
-
-    # Simpan file ke folder yang ditentukan
-    with open(file_path, "wb") as file_object:
-        file_object.write(dataset.file.read())
-
-    # Panggil fungsi untuk mengekstrak file jika perlu
-    FileProcess.extract_zip(file_path)
-
-    return {'message': 'File successfully uploaded'}
-
-@app.post('/set_params', tags=["Training"])
-async def set_params(request: Request, params: TrainingParams):
-    global optimizer, epoch, batch_size
-
-    optimizer = params.optimizer
-    epoch = params.epoch
-    batch_size = params.batchSize
-
-    response = {'message': 'Set parameter sukses'}
-    return response
-
-@app.get('/get_info_data', tags=["Training"])
-def get_info_prepro():
-    global optimizer, epoch, batch_size
-    training_counts = GetLoadData.get_training_file_counts().json
-    testing_counts = GetLoadData.get_testing_file_counts().json
-    response = {
-        "optimizer": optimizer,
-        "epoch": epoch,
-        "batch_size": batch_size,
-        "training_counts": training_counts,
-        "testing_counts": testing_counts
-    }
-    return response
-
-@app.get('/get_images_preprocess', tags=["Training"])
-def get_random_images_crop():
-    images_face_landmark = GetLoadData.get_random_images(tahap="Face Landmark",public_url=public_url)
-    images_face_extraction = GetLoadData.get_random_images(tahap="landmark Extraction", public_url=public_url)
-
-    response = {
-        "face_landmark": images_face_landmark,
-        "landmark_extraction": images_face_extraction
-    }
-    return response
-
-@app.get('/do_preprocessing', tags=["Training"])
-async def do_preprocessing():
-    try:
-        data_train_pred.do_pre1(test="")
-        data_train_pred.do_pre2(test="")
-        return {'message': 'Preprocessing sukses'}
-    except Exception as e:
-        # Tangani kesalahan dan kembalikan respons kesalahan
-        error_message = f'Error during preprocessing: {str(e)}'
-        raise HTTPException(status_code=500, detail=error_message)
-
-@app.get('/do_training', tags=["Training"])
-def do_training():
-    global epoch
-    folder = ""
-    if (face_landmark_img == True and landmark_extraction_img == True):
-        folder = "Landmark Extraction"
-    elif (face_landmark_img == True and landmark_extraction_img == False):
-        folder = "Face Landmark"
-    # --------------------------------------------------------------
-    train_dataset_path = f"./static/dataset/{folder}/Training/"
-    test_dataset_path = f"./static/dataset/{folder}/Testing/"
-
-    train_image_df, test_image_df = GetLoadData.load_image_dataset(train_dataset_path, test_dataset_path)
-
-    train_gen, test_gen = data_train_pred.data_configuration(train_image_df, test_image_df)
-    model = data_train_pred.model_architecture()
-
-    result = data_train_pred.train_model(model, train_gen, test_gen, epoch)
-
-    # Mengambil nilai akurasi training dan validation dari objek result
-    train_acc = result.history['accuracy'][-1]
-    val_acc = result.history['val_accuracy'][-1]
-
-    # Plot accuracy
-    data_train_pred.plot_accuracy(result=result, epoch=epoch)
-    acc_url = f'{public_url}/static/accuracy_plot.png'
-
-    # Plot loss
-    data_train_pred.plot_loss(result=result, epoch=epoch)
-    loss_url = f'{public_url}/static/loss_plot.png'
-
-    # Confusion Matrix
-    data_train_pred.plot_confusion_matrix(model, test_gen)
-    conf_url = f'{public_url}/static/confusion_matrix.png'
-
-    return jsonify({'train_acc': train_acc, 'val_acc': val_acc, 'plot_acc': acc_url, 'plot_loss':loss_url,'conf':conf_url})