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import sys
import types
import warnings
# Suppress specific timm warning
warnings.filterwarnings("ignore", category=FutureWarning, module="timm.models.layers")
# Monkey-patch torch.classes path issue
if not hasattr(sys.modules.get("torch"), "__path__"):
torch_classes = types.SimpleNamespace(_path=[])
sys.modules["torch.classes"] = torch_classes
import streamlit as st
import cv2
import numpy as np
from io import BytesIO
from PIL import Image
from transparent_background import Remover
def process_image(image):
try:
remover = Remover()
img = Image.open(image).convert("RGB")
with st.spinner("Processing image..."):
out = remover.process(img, type="rgba")
return out # `out` is already a PIL Image
except Exception as e:
st.error(f"An error occurred: {str(e)}")
return None
def main():
st.title("Image Upload and Processing App")
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png", "tif", "tiff"])
if uploaded_file is not None:
try:
image = Image.open(uploaded_file)
# Convert unsupported image modes
if image.mode == 'I;16':
image = image.point(lambda i: i * (1.0 / 256)).convert('RGB')
st.image(image, caption="Uploaded Image", use_container_width=True)
processed_pil = process_image(uploaded_file)
if processed_pil:
st.image(processed_pil, caption="Processed Image", use_container_width=True)
buf = BytesIO()
processed_pil.save(buf, format="PNG")
byte_im = buf.getvalue()
st.download_button(
label="Download Processed Image",
data=byte_im,
file_name="processed_image.png",
mime="image/png"
)
except Exception as e:
st.error(f"Error loading image: {e}")
if __name__ == "__main__":
main()
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