import tensorflow as tf
import numpy as np
import gradio as gr
import matplotlib.pyplot as plt
from huggingface_hub import from_pretrained_keras
import os
import sys

print('Loading model...')
model = from_pretrained_keras("mostafapasha/ribs-segmentation-model", compile=False)
print('Successfully loaded model...')
examples = [['examples/VinDr_RibCXR_train_056.png', 0.2], ['examples/VinDr_RibCXR_train_179.png', 0.8]]


def infer(img, threshold):
    if np.ndim(img) != 2:
        img = img[:, :, 1]
    img = img.reshape(1, img.shape[0], img.shape[1], 1)
    logits = model(img, training=False)
    prob = tf.sigmoid(logits)
    pred = tf.cast(prob > threshold, dtype=tf.float32)
    pred = np.array(pred.numpy())[0,:,:,0]
    return pred

gr_input = [gr.inputs.Image(label="Image", type="numpy", shape=(512, 512)), gr.inputs.Slider(minimum=0, maximum=1, step=0.05, default=0.5, label="Segmentation Threshold")
]

gr_output = [gr.outputs.Image(type="pil",label="Segmentation Mask"),
]

iface = gr.Interface(fn=infer, title='ribs segmentation model', description='Keras implementation of ResUNET++ for xray ribs segmentation', inputs=gr_input, outputs=gr_output, examples=examples, flagging_dir="flagged").launch()