import os
import streamlit as st  #Web App
from PIL import Image #Image Processing
import numpy as np #Image Processing 
import torch

#title
st.title("Hololive Waifu Classification")

image = st.text_input('Image URL', '')
# st.write('URL: ', title)

#image uploader
# image = st.file_uploader(label = "Upload your image here",type=['png','jpg','jpeg'])
def check_suffix(file='yolov5s.pt', suffix=('.pt',), msg=''):
    # Check file(s) for acceptable suffix
    if file and suffix:
        if isinstance(suffix, str):
            suffix = [suffix]
        for f in file if isinstance(file, (list, tuple)) else [file]:
            s = Path(f).suffix.lower()  # file suffix
            if len(s):
                assert s in suffix, f"{msg}{f} acceptable suffix is {suffix}"


def check_file(file, suffix=''):
    # Search/download file (if necessary) and return path
    check_suffix(file, suffix)  # optional
    file = str(file)  # convert to str()
    if os.path.isfile(file) or not file:  # exists
        return file
    elif file.startswith(('http:/', 'https:/')):  # download
        url = file  # warning: Pathlib turns :// -> :/
        file = Path(urllib.parse.unquote(file).split('?')[0]).name  # '%2F' to '/', split https://url.com/file.txt?auth
        if os.path.isfile(file):
            print(f'Found {url} locally at {file}')  # file already exists
        else:
            print(f'Downloading {url} to {file}...')
            torch.hub.download_url_to_file(url, file)
            assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}'  # check
        return file
    elif file.startswith('clearml://'):  # ClearML Dataset ID
        assert 'clearml' in sys.modules, "ClearML is not installed, so cannot use ClearML dataset. Try running 'pip install clearml'."
        return file
    else:  # search
        files = []
        for d in 'data', 'models', 'utils':  # search directories
            files.extend(glob.glob(str(ROOT / d / '**' / file), recursive=True))  # find file
        assert len(files), f'File not found: {file}'  # assert file was found
        assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}"  # assert unique
        return files[0]

@st.cache
def load_model(): 
    model = torch.hub.load('ultralytics/yolov5', 'yolov5x6', path='best.pt')  # local model

# model = load_model() #load model

if image != '':
    image = check_file(image)
    st.write('File: ', image)
    input_image = Image.open(image) #read image
    st.image(input_image) #display image

    #with st.spinner("🤖 AI is at Work! "):
        

        #result = reader.readtext(np.array(input_image))

        #result_text = [] #empty list for results


        #for text in result:
            #result_text.append(text[1])

        #st.write(result_text)
    #st.success("Here you go!")
    #st.balloons()
#else:
    #st.write("Upload an Image")

st.caption("authors: neko941")