# -*- coding: utf-8 -*-
"""
# Introduction #

Thanks to Kaggle mini-courses on computer vision for getting me started on this.
My first trained CNN to classify the card images heavily inspired by Francesco Marazz's CNN (https://www.kaggle.com/fmarazzi/baseline-keras-cnn-roc-fast-10min-0-925-lb) for the Histopathologic Cancer Detection competition (https://www.kaggle.com/c/histopathologic-cancer-detection).

My objective here is to build my first image classifier using Keras. Rare Pokemon cards are highly sought after by collectors, with some even reaching re-sale prices of hundreds of thousands of dollars. It would be important for collectors to tell a genuine card from fake card to avoid getting duped. Experienced collectors are able to tell the different between a genuine and fake card by looking at the features of the back of the card alone, but this might not be so obvious for newcomers. The image classifier described below will aim to classify, with high accuracy (>95%), Pokemon cards as genuine or fake based on the back visuals of the card alone. In future work, the model can be made more robust by including more counterfeit variations, folded, torn, new and old cards in the training, validation and test sets.

# Preprocess Image
User should upload a landscape image with the Pokemon card image positioned upright in the centre of the photo
"""
import gradio as gr

import cv2
from PIL import Image
import tensorflow as tf 
import numpy as np
from tensorflow.keras.models import load_model

def crop_center(img):
    y,x,z = img.shape
    startx = (x-y)//2
    starty = 0
    return img[starty:y,startx:startx+y]

def resizeImage(img):
    r = 256.0 / img.shape[0]
    dim = (int(img.shape[1] * r), 256)
    resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)
    return resized

model = load_model("tf_model.h5")

"""# Define function"""

def pkm_predict(image):
    image = crop_center(image)
    image = resizeImage(image)
    K_test = (image - image.mean()) / image.std()
    K_test = np.reshape(K_test, (1, 256, 256, 3)) 
    predictions = model.predict(K_test)
    predictions = list(map(lambda x: 0 if x<0.5 else 1, predictions)) # get binary values predictions with 0.5 as thresold
    print(predictions)
    if predictions == [1]:
        return "This card is likely genuine." 
    else:
        return "This card is likely counterfeit."

"""# Gradio Launch #"""

image = gr.inputs.Image()
title="Pokemon Card Authenticator"
description="Keras CNN trained on 373 genuine and counterfeit Pokemon Card images to classify Pokemon cards as real and fake based on the back graphics of the card. To use it, simply upload your image or click on one of the examples to load them. Please note that this classifier only works with the back graphics of Pokemon cards; the uploaded image needs to be landscape and the Pokemon card needs to be upright in the middle of the image with white background (See examples)."
examples=[['00395.JPG'], ['00430.JPG']]

iface= gr.Interface(
    fn=pkm_predict,
    inputs=image,
    outputs="text",
    title=title,
    description=description,
    examples=examples
)

iface.launch()