streamlit_config.py

import streamlit as st
from PIL import Image
import re
import requests
from io import BytesIO

import segmentation


def init():
 st.set_page_config(page_title="Semantic image segmentation")
 st.session_state["model"] = segmentation.create_model()
 st.session_state["feature_extractor"] = segmentation.create_feature_extractor()


@st.experimental_memo(show_spinner=False)
def process_file(file):
 return segmentation.segment(
 Image.open(file),
 st.session_state["model"],
 st.session_state["feature_extractor"]
 )


def download_button(file, name, format):
 st.download_button(
 label="Download processed image",
 data=file,
 file_name=name,
 mime="image/" + format
 )


def run():
 st.title("Semantic image segmentation")
 img_url = st.text_input('Image URL', 'https://images.unsplash.com/photo-1556911220-bff31c812dba?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=2468&q=80')
 st.caption('Downloading Image...')
 file = requests.get(img_url).content
 if not file:
 return

 placeholder = st.empty()
 placeholder.info(
 "Processing..."
 )

 #image = process_file(file)
 image = file
 placeholder.empty()
 placeholder.image(image)

 filename = file.name
 format = re.findall("\..*$", filename)[0][1:]

 image = Image.fromarray(image)

 buf = BytesIO()
 image.save(buf, format="JPEG")
 byte_image = buf.getvalue()

 download_button(byte_image, filename, format)