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 

def greet(name):
    return "Hello " + name + "!!"

#iface = gr.Interface(fn=greet, inputs="text", outputs="text")
#iface.launch()



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)
    saved_model_dir = os.path.join(download_dir, "saved_model.pb")
    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)

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')

REPO_ID = "23A066X"
gr.Interface(fn=predict,
             inputs=[gr.Image(type="pil",label="Input Image"),gr.Textbox(placeholder="0.50",label="Set the confidence threshold (0.00-1.00)")],
             outputs=gr.Image(type="pil",label="Output Image"), 
             title="Cauliflower and Beetroot Detection",
             description="Model: ssd_resnet50_v1_fpn_640x640_coco17_tpu-8",
             theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky")
             ).launch(share=True)