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 cv2

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)
    saved_model_dir = os.path.join(download_dir, "saved_model")
    detection_model = tf.saved_model.load(saved_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=.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

def detect_video(video):
    # Create a video capture object
    cap = cv2.VideoCapture(video)

    nb_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    frame_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    frame_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    fps = cap.get(cv2.CAP_PROP_FPS)
    
    # Process frames in a loop
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
    for i in tqdm(range(nb_frames)):
        ret, image_np = video_reader.read()
        input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.uint8)
        results = detection_model(input_tensor)
        image_np_with_detections = viz_utils.visualize_boxes_and_labels_on_image_array(
                  image_np,
                  results['detection_boxes'][0].numpy(),
                  (results['detection_classes'][0].numpy()+ label_id_offset).astype(int),
                  results['detection_scores'][0].numpy(),
                  category_index,
                  use_normalized_coordinates=True,
                  max_boxes_to_draw=200,
                  min_score_thresh=.50,
                  agnostic_mode=False,
                  line_thickness=2)


        # Yield the processed frame
        yield image_np_with_detections

    # Release resources
    cap.release()

    
REPO_ID = "apailang/mytfodmodel"
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')

test1 = os.path.join(os.path.dirname(__file__), "data/test1.jpeg")  
test2 = os.path.join(os.path.dirname(__file__), "data/test2.jpeg")  
test3 = os.path.join(os.path.dirname(__file__), "data/test3.jpeg")  
test4 = os.path.join(os.path.dirname(__file__), "data/test4.jpeg")
test5 = os.path.join(os.path.dirname(__file__), "data/test5.jpeg")
test6 = os.path.join(os.path.dirname(__file__), "data/test6.jpeg")
test7 = os.path.join(os.path.dirname(__file__), "data/test7.jpeg")
test8 = os.path.join(os.path.dirname(__file__), "data/test8.jpeg")
test9 = os.path.join(os.path.dirname(__file__), "data/test9.jpeg")
test10 = os.path.join(os.path.dirname(__file__), "data/test10.jpeg")
test11 = os.path.join(os.path.dirname(__file__), "data/test11.jpeg")
test12 = os.path.join(os.path.dirname(__file__), "data/test12.jpeg")

tts_demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=gr.Image(type="pil"),
    title="Image Prediction Interface",
    description="Upload a Image for prediction",
    examples=[[test1],[test2],[test3],[test4],[test5],[test6],[test7],[test8],[test9],[test10],[test11],[test12],],
    cache_examples=True
    )#.launch(share=True)


a = os.path.join(os.path.dirname(__file__), "data/a.mp4")  # Video
b = os.path.join(os.path.dirname(__file__), "data/b.mp4")  # Video
c = os.path.join(os.path.dirname(__file__), "data/c.mp4")  # Video

stt_demo = gr.Interface(
    fn=detect_video, #detect_video
    inputs=gr.Video(),
    outputs=gr.Video(),
    examples=[
        [a],
        [b],
        [c],
    ],
    cache_examples=True
)
demo = gr.TabbedInterface([tts_demo, stt_demo], ["Image", "Video"])

if __name__ == "__main__":
    demo.launch()