import matplotlib.pyplot as plt
import numpy as np
from six import BytesIO
from PIL import Image
import tensorflow as tf
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as viz_utils
from object_detection.utils import ops as utils_op
import tarfile
import wget
import gradio as gr
from huggingface_hub import snapshot_download
import os
import pathlib

REPO_ID = 'liewchooichin/hb'
PATH_TO_LABELS = 'data/label_map.pbtxt'
category_index = label_map_util.create_category_index_from_labelmap(
    PATH_TO_LABELS, use_display_name=True)


def pil_image_as_numpy_array(pilimg):

    img_array = tf.keras.utils.img_to_array(pilimg)
    img_array = np.expand_dims(img_array, axis=0)
    return img_array


def load_image_into_numpy_array(path):

    image = None
    image_data = tf.io.gfile.GFile(path, 'rb').read()
    image = Image.open(BytesIO(image_data))
    return pil_image_as_numpy_array(image)


def load_model():
    download_dir = snapshot_download(repo_id=REPO_ID)
    print(f"{download_dir=}")
    saved_model_dir = os.path.join(download_dir, "saved_model")
    print(f"{saved_model_dir=}")
    detection_model = tf.saved_model.load(saved_model_dir)
    return detection_model


def predict(pilimg):

    image_np = pil_image_as_numpy_array(pilimg)

    results = detection_model(image_np)

    # different object detection models have additional results
    result = {key: value.numpy() for key, value in results.items()}

    label_id_offset = 0
    image_np_with_detections = image_np.copy()

    viz_utils.visualize_boxes_and_labels_on_image_array(
        image_np_with_detections[0],
        result['detection_boxes'][0],
        (result['detection_classes'][0] + label_id_offset).astype(int),
        result['detection_scores'][0],
        category_index,
        use_normalized_coordinates=True,
        max_boxes_to_draw=200,
        min_score_thresh=.60,
        agnostic_mode=False,
        line_thickness=2)

    result_pil_img = tf.keras.utils.array_to_img(image_np_with_detections[0])

    return result_pil_img


# My model in the HF repo
detection_model = load_model()

data_dir = "test_samples"  # contain the samples
sample_dir = os.path.join(os.path.dirname(__file__), data_dir)
sample_files = list(pathlib.Path(sample_dir).glob("*.jpg"))
print(f"Sample files: {sample_files}")
examples = [
    sample_files[0],
    sample_files[1],
    sample_files[2],
    sample_files[3],
]
title = "Detecting hamster and butterfly"
description = "Using TensorFlow Object Detection API."

gr.Interface(
    title=title,
    description=description,
    fn=predict,
    inputs=gr.Image(type="pil", sources=["upload", "clipboard"]),
    outputs=gr.Image(type="pil", interactive=False),
    examples=examples

).launch(share=True)