import os
import keras
import numpy as np
from keras.applications.imagenet_utils import decode_predictions
from classification_models.keras import Classifiers

class StockPredictor:

    RESOURCE_PATH = None
    stock2Vector = None

    def __init__(self):
        return

    def getDataset(self, df):
        VECTOR_SIZE = 299
        TOTAL_X, TOTAL_Y = [], []
        for key in df:
            if key == "date":
                continue
            elif key == "label":
                TOTAL_Y.append(df[key].tolist())
            else:
                TOTAL_X.append(df[key].tolist())

        SIZE_WIDTH = len(TOTAL_X[0])
        SIZE_HEIGHT = len(TOTAL_X)
        X = []
        for i in range(VECTOR_SIZE, SIZE_WIDTH):
            temp_X, temp_Y = np.zeros((VECTOR_SIZE, VECTOR_SIZE)), np.zeros(0)
            for j in range(SIZE_HEIGHT):
                temp_X[j][0:VECTOR_SIZE] = TOTAL_X[j][i - VECTOR_SIZE:i]
            temp_X = np.stack([temp_X, temp_X, temp_X], axis=-1)
            X.append(temp_X)

        X = np.asarray(X[len(X)-1])

        return X

    def predict(self, df, minmax_df, isRealTime=False):
        X = self.getDataset(df)

        # build model
        n_classes = 3
        Inceptionresnetv2, preprocess_input = Classifiers.get('inceptionresnetv2')
        X = preprocess_input(X)
        base_model = Inceptionresnetv2(input_shape=(299, 299, 3), include_top=False)
        model = keras.models.Model(inputs=[base_model.input])

        checkpoint_filename = os.path.join(self.RESOURCE_PATH, "model", "stock.ckpt")
        model.load_weights(checkpoint_filename)

        y = model.predict(X)

        # result
        print(decode_predictions(y))


        return