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"""
Streamlit app
"""
import sys
import streamlit as st
from PIL import Image
from constants import CLASSES, OUTPUT_IMG_FILEPATH
sys.path.append("./efficientdet")
from efficientdet.efficientdet import plot_results
from trash_detector import detect_trash
def initial_config():
"""
Initial configuration of streamlit page
"""
st.set_page_config(
page_title="Waste Classifier",
page_icon="♻️",
)
def render():
"""
Render the streamlit app
"""
st.title("Waste classifier")
st.markdown("""Classify your waste into different classes""")
# Image loader and button
uploaded_file = st.file_uploader(
"Upload image with trash", type=["jpg", "jpeg", "png", "gif", "bmp"]
)
classify_button = st.button("Classify trash")
if classify_button:
if not uploaded_file:
st.error("Upload an image")
else:
# Create two columns
col1, col2 = st.columns(2)
# Column 1: Uploaded image
with col1:
st.write("Uploaded image")
st.image(
uploaded_file, caption="Uploaded Image.", use_column_width=True
)
# Column 2: Classified image
with col2:
with st.spinner(text="Classifying the trash..."):
img = Image.open(uploaded_file).convert("RGB")
cls_prob, bboxes_final = detect_trash(img)
# plot and save demo image
plot_results(
img, cls_prob, bboxes_final, CLASSES, OUTPUT_IMG_FILEPATH
)
output_img = Image.open(OUTPUT_IMG_FILEPATH)
st.write("Classified image")
st.image(
output_img, caption="Classified Image.", use_column_width=True
)
if __name__ == "__main__":
initial_config()
render()
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