import os
import json
import numpy as np
import gradio as gr
import cv2
from drexel_metadata.gen_metadata import gen_metadata
from PIL import Image


def create_temp_file_path(prefix, suffix):
    with tempfile.NamedTemporaryFile(prefix=prefix, suffix=suffix, delete=False) as tmpfile:
        return tmpfile.name


def run_inference(input_img):
    # input_mg: NumPy array with the shape (width, height, 3)

    # Save input_mg as a temporary file
    tmpfile = create_temp_file_path(prefix="input_", suffix, ".png")
    im = Image.fromarray(input_img)
    im.save(tmpfile)

    # Create temp filenames for output images
    visfname = create_temp_file_path(prefix="vis_", suffix=".png")
    maskfname = create_temp_file_path(prefix="mask_", suffix=".png")

    # Run inference
    result = gen_metadata(tmpfile, device='cpu', maskfname=maskfname, visfname=visfname)
    json_metadata = json.dumps(result)

    # Cleanup
    os.remove(tempfile)

    return visfname, maskfname, json_metadata


def read_app_header_markdown():
   with open('app_header.md') as infile:
       return infile.read()


dm_app = gr.Interface(
     fn=run_inference,
     # Input shows markdown explaining and app and a single image upload panel
     inputs=[
        gr.Markdown(read_app_header_markdown()),
        gr.Image()
     ],
     # Output consists of a visualization image, a masked image, and JSON metadata
     outputs=[
        gr.Image(label='visualization'),
        gr.Image(label='mask'),
        gr.JSON(label="JSON metadata")
     ],
     allow_flagging="never" # Do not save user's results or prompt for users to save the results
)
dm_app.launch()