import matplotlib.pyplot as plt
import numpy as np
from six import BytesIO
from PIL import Image
import tensorflow as tf
import tarfile
import wget 
import gradio as gr
from huggingface_hub import snapshot_download
import os 
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


PATH_TO_LABELS = '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)
    # download_dir = os.path.join(download_dir, "saved_model")
    saved_model_dir = os.path.join(download_dir, "saved_model")
    detection_model = tf.saved_model.load(saved_model_dir)
    return detection_model

def load_model2():
    wget.download("https://nyp-aicourse.s3-ap-southeast-1.amazonaws.com/pretrained-models/balloon_model.tar.gz")
    tarfile.open("balloon_model.tar.gz").extractall()
    model_dir = 'saved_model'    
    detection_model = tf.saved_model.load(str(model_dir))
    return detection_model    



def predict(pilimg):

    image_np = pil_image_as_numpy_array(pilimg)
    return predict2(image_np)

def predict2(image_np):

    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=0.60,
        agnostic_mode=False,
        line_thickness=3)

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


REPO_ID = "A23066X/A23066X_model"
detection_model = load_model()
# pil_image = Image.open(image_path)
# image_arr = pil_image_as_numpy_array(pil_image)

# predicted_img = predict(image_arr)
# predicted_img.save('predicted.jpg')

title = "Cauliflower and Beetroot Detection"
description = "Using ssd_resnet50_v1_fpn_640x640_coco17_tpu-8"


gr.Interface(fn=predict,
             title = title,
             description = description,
             css=css_code,
             inputs=gr.Image(type="pil", height=250),
             outputs=gr.Image(type="pil", height=250)
             ).launch(share=True)