app.py

import  os
import tempfile
import json
import numpy as np
import gradio as gr
import cv2
from drexel_metadata.gen_metadata import gen_metadata
from PIL import Image
import urllib.request
from huggingface_hub import hf_hub_download


# Download model if not already cached locally
hf_hub_download(repo_id="imageomics/Drexel-metadata-generator", filename="model_final.pth", local_dir="output/enhanced")


EXAMPLE_URLS = [
   'http://www.tubri.org/HDR/INHS/INHS_FISH_59422.jpg',
   'http://www.tubri.org/HDR/INHS/INHS_FISH_76560.jpg'
]
EXAMPLES = []
for example_url in EXAMPLE_URLS:
    file_name = os.path.basename(example_url)
    urllib.request.urlretrieve(example_url, file_name)
    # According to the docs examples should be a nested list
    EXAMPLES.append([file_name])


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(tmpfile)

    return visfname, maskfname, json_metadata


def try_remove_preamble(readme_md):
    # Try to remove the huggingface preamble from README markdown
    idx = readme_md.find("#")
    if idx >= 0:
       return readme_md[idx:]
    return readme_md


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


dm_app = gr.Interface(
     description=read_app_header_markdown(),
     fn=run_inference,
     # Input shows markdown explaining and app and a single image upload panel
     inputs=[
        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
     examples=EXAMPLES,
)
dm_app.launch(server_name="0.0.0.0")