import os
import numpy as np
import random

import segmentation_models as sm

from matplotlib import pyplot as plt

import tensorflow as tf


from tensorflow.keras import backend as K
from tensorflow.keras.models import load_model

import gradio as gr

def jaccard_coef(y_true, y_pred):
  y_true_flatten = K.flatten(y_true)
  y_pred_flatten = K.flatten(y_pred)
  intersection = K.sum(y_true_flatten * y_pred_flatten)
  final_coef_value = (intersection + 1.0) / (K.sum(y_true_flatten) + K.sum(y_pred_flatten) - intersection + 1.0)
  return final_coef
  
weights = [0.1666, 0.1666, 0.1666, 0.1666, 0.1666, 0.1666]
dice_loss = sm.losses.DiceLoss(class_weights = weights)
focal_loss = sm.losses.CategoricalFocalLoss()
total_loss = dice_loss + focal_loss

saved_model = load_model('satellite_segmentation_model.h5',
                         custom_objects=({'dice_loss_plus_focal_loss': total_loss, 'jaccard_coef': jaccard_coef}))
                         
def process_input_image(image_source):
  image = np.expand_dims(image_source, 0)

  prediction = saved_model.predict(image)
  predicted_image = np.argmax(prediction, axis=3)

  predicted_image = predicted_image[0, :, :]
  predicted_image = predicted_image * 50
  return 'Predicted Masked Image', predicted_image
  
my_app = gr.Blocks()

with my_app:
  gr.Markdown("Statellite Image Segmentation: Built with Gradio")
  with gr.Tabs():
    with gr.TabItem("Select your image"):
      with gr.Row():
        with gr.Column():
            img_source = gr.Image(label="Please select source Image", shape=(256, 256))
            source_image_loader = gr.Button("Load above Image")
        with gr.Column():
            output_label = gr.Label(label="Image Info")
            img_output = gr.Image(label="Image Output")
    source_image_loader.click(
        process_input_image,
        [
            img_source
        ],
        [
            output_label,
            img_output
        ]
    )
    
my_app.launch(debug = True)