from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
from flask import Flask, render_template, request
from werkzeug.utils import secure_filename
import os

model = load_model('Trained.h5')


def return_prediction(model, image):

  # Preprocess image
  img = Image.open(image).convert('RGB').resize((83, 83))
  img = np.array(img).reshape(1, 83, 83, 3) / 255

  # Predict the class
  classes = ['laptop', 'phone']
  pred = model.predict(img)
  class_ind = round(pred[0][0])

  # return prediction
  return classes[class_ind]


####### Start of the App

app = Flask(__name__)


@app.route("/")
def index():
  return render_template('upload.html')


@app.route("/uploader", methods=['GET', 'POST'])
def upload_file():
  if request.method == 'POST':

    # storing file
    #f = request.files['file']
    #fname = secure_filename(f.filename)
    #f.save("assests/" + fname)

    # prediction
    result = return_prediction(model,
                               request.files['file'])  #"assests/" + fname)
    return result
  return "nothing"


if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860)