sa

main.py

@@ -65,60 +65,60 @@ async def root():
 data_processor = DataProcessing()
 data_train_pred = TrainPred()
 
-import random
-def preprocessing(filepath):
-    folder_path = './static/temporary'
-
-    shutil.rmtree(folder_path)
-    os.mkdir(folder_path)
-
-    # data_processor.detect_landmark(data_processor.face_cropping_pred(filepath))
-    data_processor.enhance_contrast_histeq(data_processor.face_cropping_pred(filepath))
-
-    files = os.listdir(folder_path)
-    index = 0
-    for file_name in files:
-        file_ext = os.path.splitext(file_name)[1]
-        new_file_name = str(index) + "_" + str(random.randint(1, 100000)) + file_ext
-        os.rename(os.path.join(folder_path, file_name), os.path.join(folder_path, new_file_name))
-        index += 1
-
-    print("Tungu sampai selesaiii")
-
-train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1 / 255.)
-test_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1 / 255.)
-
-## -------------------------------------------------------------------------
-##                   API UNTUK MELAKUKAN PROSES PREDIKSI
-## -------------------------------------------------------------------------
-
-@app.post('/upload/file',tags=["Predicting"])
-async def upload_file(picture: UploadFile):
-    file_extension = picture.filename.split('.')[-1].lower()
-
-
-    if file_extension not in ALLOWED_EXTENSIONS:
-        raise HTTPException(status_code=400, detail='Invalid file extension')
-
-    os.makedirs(UPLOAD_FOLDER, exist_ok=True)
-    file_path = os.path.join(UPLOAD_FOLDER, secure_filename(picture.filename))
-    with open(file_path, 'wb') as f:
-        f.write(picture.file.read())
-    try:
-        processed_img = preprocessing(cv2.imread(file_path))
-    except Exception as e:
-        os.remove(file_path)
-        raise HTTPException(status_code=500, detail=f'Error processing image: {str(e)}')
-
-    return JSONResponse(content={'message': 'File successfully uploaded'}, status_code=200)
-
-@app.get('/get_images', tags=["Predicting"])
-def get_images():
-    folder_path = "./static/temporary"
-    files = [f for f in os.listdir(folder_path) if os.path.isfile(os.path.join(folder_path, f))]
-    urls = []
-    for i in range(0, 3):
-        url = f'{public_url}/static/temporary/{files[i]}'
-        urls.append(url)
-        bentuk, persentase = data_train_pred.prediction(selected_model)
-    return {'urls': urls, 'bentuk_wajah':bentuk[0], 'persen':persentase}
+# import random
+# def preprocessing(filepath):
+#     folder_path = './static/temporary'
+
+#     shutil.rmtree(folder_path)
+#     os.mkdir(folder_path)
+
+#     # data_processor.detect_landmark(data_processor.face_cropping_pred(filepath))
+#     data_processor.enhance_contrast_histeq(data_processor.face_cropping_pred(filepath))
+
+#     files = os.listdir(folder_path)
+#     index = 0
+#     for file_name in files:
+#         file_ext = os.path.splitext(file_name)[1]
+#         new_file_name = str(index) + "_" + str(random.randint(1, 100000)) + file_ext
+#         os.rename(os.path.join(folder_path, file_name), os.path.join(folder_path, new_file_name))
+#         index += 1
+
+#     print("Tungu sampai selesaiii")
+
+# train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1 / 255.)
+# test_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1 / 255.)
+
+# ## -------------------------------------------------------------------------
+# ##                   API UNTUK MELAKUKAN PROSES PREDIKSI
+# ## -------------------------------------------------------------------------
+
+# @app.post('/upload/file',tags=["Predicting"])
+# async def upload_file(picture: UploadFile):
+#     file_extension = picture.filename.split('.')[-1].lower()
+
+
+#     if file_extension not in ALLOWED_EXTENSIONS:
+#         raise HTTPException(status_code=400, detail='Invalid file extension')
+
+#     os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+#     file_path = os.path.join(UPLOAD_FOLDER, secure_filename(picture.filename))
+#     with open(file_path, 'wb') as f:
+#         f.write(picture.file.read())
+#     try:
+#         processed_img = preprocessing(cv2.imread(file_path))
+#     except Exception as e:
+#         os.remove(file_path)
+#         raise HTTPException(status_code=500, detail=f'Error processing image: {str(e)}')
+
+#     return JSONResponse(content={'message': 'File successfully uploaded'}, status_code=200)
+
+# @app.get('/get_images', tags=["Predicting"])
+# def get_images():
+#     folder_path = "./static/temporary"
+#     files = [f for f in os.listdir(folder_path) if os.path.isfile(os.path.join(folder_path, f))]
+#     urls = []
+#     for i in range(0, 3):
+#         url = f'{public_url}/static/temporary/{files[i]}'
+#         urls.append(url)
+#         bentuk, persentase = data_train_pred.prediction(selected_model)
+#     return {'urls': urls, 'bentuk_wajah':bentuk[0], 'persen':persentase}