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main.py

@@ -179,111 +179,111 @@ def get_images():
 # -------------------------------------------------------------------------
 
 # 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})
+# 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})
 
 
 # -------------------------------------------------------------------------
@@ -291,37 +291,37 @@ def do_training():
 # -------------------------------------------------------------------------
 
 
-@app.post('/upload/model', tags=["Model"])
-def upload_model():
-    if 'file' not in request.files:
-        return {'message': 'No file part in the request'}, 400
+# @app.post('/upload/model', tags=["Model"])
+# def upload_model():
+#     if 'file' not in request.files:
+#         return {'message': 'No file part in the request'}, 400
 
-    file = request.files['file']
+#     file = request.files['file']
 
-    if file.filename == '':
-        return {'message': 'No file selected for uploading'}, 400
+#     if file.filename == '':
+#         return {'message': 'No file selected for uploading'}, 400
 
-    if file and FileProcess.allowed_file(file.filename):
-        filename = secure_filename(file.filename)
-        filepath = os.path.join(app.config['UPLOAD_MODEL'], filename)
-        file.save(filepath)
+#     if file and FileProcess.allowed_file(file.filename):
+#         filename = secure_filename(file.filename)
+#         filepath = os.path.join(app.config['UPLOAD_MODEL'], filename)
+#         file.save(filepath)
 
-        return {'message': 'File successfully uploaded'}
+#         return {'message': 'File successfully uploaded'}
 
-    return {'message': 'File failed to uploaded'}
+#     return {'message': 'File failed to uploaded'}
 
-@app.post('/selected_models')
-def select_models(index: int):
-    global selected_model
-    try:
-        global selected_model
-        selected_model = tf.keras.models.load_model(f'models/fc_model_{index}.h5')
+# @app.post('/selected_models')
+# def select_models(index: int):
+#     global selected_model
+#     try:
+#         global selected_model
+#         selected_model = tf.keras.models.load_model(f'models/fc_model_{index}.h5')
 
-        # Lakukan sesuatu dengan indeks yang diterima
+#         # Lakukan sesuatu dengan indeks yang diterima
 
-        return {'message': 'Request berhasil diterima'}
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f'Error: {str(e)}')
+#         return {'message': 'Request berhasil diterima'}
+#     except Exception as e:
+#         raise HTTPException(status_code=500, detail=f'Error: {str(e)}')
 
 if __name__ == '__main__':
     import uvicorn