import os
import requests
import json
import time
import gradio as gr

from utils import get_token
from obshandler import OBSHandler

url = os.environ["URL_NODE"]

obs = OBSHandler()


def detect_image(image):
    print("image: ", image)

    user_name = "huggingface"
    upload_path = user_name + "/" + str(time.time()) + "/input.jpg"
    obs.upload_file(upload_path, image)

    token = get_token()

    requests_json = {"file_path": upload_path}
    headers = {"Content-Type": "application/json", "X-Auth-Token": token}
    resp = requests.post(url,
                         json=requests_json,
                         headers=headers,
                         verify=False)
    resp = json.loads(resp.text)
    gen_url = resp["result"]
    return gen_url


def read_content(file_path):
    with open(file_path, 'r', encoding='utf-8') as f:
        content = f.read()
    return content


example_images = [
    os.path.join(os.path.dirname(__file__), "examples/00.jpg"),
    os.path.join(os.path.dirname(__file__), "examples/01.jpg"),
    os.path.join(os.path.dirname(__file__), "examples/02.jpg"),
    os.path.join(os.path.dirname(__file__), "examples/03.jpg"),
    os.path.join(os.path.dirname(__file__), "examples/04.jpg"),
    os.path.join(os.path.dirname(__file__), "examples/05.jpg")
]

default_image = example_images[0]

css = """
.gradio-container {background-image: url('file=./background.jpg'); background-size:cover; background-repeat: no-repeat;}
"""

# warm up
# detect_image()

with gr.Blocks(css=css) as demo:
    gr.HTML(read_content("./header.html"))
    gr.Markdown("# MindSpore Wuhan.LuoJiaNET")
    gr.Markdown(
        "`Wuhan.LuoJiaNET` is the first domestic autonomous and controllable machine learning framework for remote sensing in the field of remote sensing,"
        " jointly developed by` Wuhan University` and `Huawei's Ascend AI team`, which has the characteristics of large image size,"
        " multiple data channels, and large scale variation of remote sensing data."
        " It is compatible with existing deep learning frameworks and provides a user-friendly,"
        " drag-and-drop interactive network structure to build an interface."
        " It can shield the differences between different hardware devices and manage a diversified remote sensing image sample library,"
        " LuoJiaSET, to achieve efficient storage and management of remote multi-source sensing image samples."
    )

    with gr.Tab("目标识别 (Object Detection)"):
        with gr.Row():
            image_input = gr.Image(type="filepath",
                                   value=default_image,
                                   interactive=False)
            image_output = gr.Image(type="filepath")

        gr.Examples(
            examples=example_images,
            inputs=image_input,
        )
        image_button = gr.Button("Detect")

    with gr.Accordion("Open for More!"):
        gr.Markdown(
            "- If you want to know more about the foundation models of MindSpore, please visit "
            "[The Foundation Models Platform for Mindspore](https://xihe.mindspore.cn/)"
        )
        gr.Markdown(
            "- If you want to know more about Wuhan.LuoJiaNET, please visit "
            "[Wuhan.LuoJiaNET](https://github.com/WHULuoJiaTeam/luojianet)")
        gr.Markdown(
            "- Try [Wukong-LuojiaNET model on the Foundation Models Platform for Mindspore]"
            "(https://xihe.mindspore.cn/modelzoo/luojia)")

    image_button.click(detect_image,
                       inputs=[image_input],
                       outputs=[image_output])

demo.queue(concurrency_count=5)
demo.launch(enable_queue=True)