''' ----------------------------------------
* Creation Time : Sun Aug 28 21:38:58 2022
* Last Modified : Sun Aug 28 21:41:36 2022
* Author : Charles N. Christensen
* Github : github.com/charlesnchr
----------------------------------------'''

from turtle import title
import gradio as gr
from huggingface_hub import from_pretrained_keras
import tensorflow as tf
import numpy as np
from PIL import Image
import io
import base64


model = tf.keras.models.load_model("./tf_model.h5")


def predict(image):
    img = np.array(image)
    original_shape = img.shape[:2]

    im = tf.image.resize(img, (128, 128))
    im = tf.cast(im, tf.float32) / 255.0
    pred_mask = model.predict(im[tf.newaxis, ...])


    # take the best performing class for each pixel
    # the output of argmax looks like this [[1, 2, 0], ...]
    pred_mask_arg = tf.argmax(pred_mask, axis=-1)


    # convert the prediction mask into binary masks for each class
    binary_masks = {}

    # when we take tf.argmax() over pred_mask, it becomes a tensor object
    # the shape becomes TensorShape object, looking like this TensorShape([128])
    # we need to take get shape, convert to list and take the best one

    rows = pred_mask_arg[0][1].get_shape().as_list()[0]
    cols = pred_mask_arg[0][2].get_shape().as_list()[0]

    for cls in range(pred_mask.shape[-1]):

        binary_masks[f"mask_{cls}"] = np.zeros(shape = (pred_mask.shape[1], pred_mask.shape[2])) #create masks for each class

        for row in range(rows):

            for col in range(cols):

                if pred_mask_arg[0][row][col] == cls:

                    binary_masks[f"mask_{cls}"][row][col] = 1
                else:
                    binary_masks[f"mask_{cls}"][row][col] = 0

        mask = binary_masks[f"mask_{cls}"]
        mask *= 255

    mask = np.array(Image.fromarray(mask).convert("L"))
    mask = tf.image.resize(mask[..., tf.newaxis], original_shape)
    mask = tf.cast(mask, tf.uint8)
    mask = mask.numpy().squeeze()

    return mask


title = '<h1 style="text-align: center;">Segment Pets</h1>'

description = """
## About
This space demonstrates the use of a semantic segmentation model to segment pets and classify them
according to the pixels.
## 🚀 To run
Upload a pet image and hit submit or select one from the given examples
"""

inputs = gr.inputs.Image(label="Upload a pet image", type = 'pil', optional=False)
outputs = [
    gr.outputs.Image(label="Segmentation")
    # , gr.outputs.Textbox(type="auto",label="Pet Prediction")
]

examples = [
    "./examples/dogcat.jpeg",
]



interface = gr.Interface(fn=predict,
    inputs=inputs,
    outputs=outputs,
    title = title,
    description=description,
    examples=examples
    )
interface.launch()