import cv2
from fastai.vision.all import *
import gradio as gr

fnames = get_image_files("./albumentations/original")
def label_func(fn): return "./albumentations/labelled/"f"{fn.stem}.png"
codes = np.loadtxt('labels.txt', dtype=str)
w, h = 768, 1152
img_size = (w,h)
im_size = (h,w)

dls = SegmentationDataLoaders.from_label_func(
    ".", bs=3, fnames = fnames, label_func = label_func, codes = codes,
    item_tfms=Resize(img_size)
)

learn = unet_learner(dls, resnet34)
learn.load('learn')

def predict_segmentation(img):
    # Convert the input image to grayscale
    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # Resize the image to the size of the training images
    resized_img = cv2.resize(gray_img, im_size)
    # Predict the segmentation mask
    pred = learn.predict(resized_img)
    # Convert the predicted mask to a color image
    color_pred = pred[0].show(ctx=None, cmap='gray', alpha=None)
    # Convert the color image to a numpy array
    color_pred_array = np.array(color_pred)
    # Convert the numpy array back to a PIL image
    output_image = Image.fromarray(color_pred_array)
    return output_image

input_image = gr.inputs.Image()
output_image = gr.outputs.Image(type='pil')
app = gr.Interface(fn=predict_segmentation, inputs=input_image, outputs=output_image, title='Microstructure Segmentation', description='Segment the input image into grain and background.')
app.launch()