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app.py

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+import streamlit as st
+from PIL import Image
+import torch
+from torchvision import transforms
+from transformers import AutoModelForImageSegmentation
+import io
+import os
+
+# Set matmul precision (important for performance on some systems)
+torch.set_float32_matmul_precision(["high", "highest"][0])
+
+# Load the model (outside the function for efficiency)
+@st.cache_resource  # Cache the model to avoid reloading on every run
+def load_model():
+    model = AutoModelForImageSegmentation.from_pretrained("ZhengPeng7/BiRefNet", trust_remote_code=True)
+    model.to("cuda" if torch.cuda.is_available() else "cpu") # Use CUDA if available
+    return model
+
+birefnet = load_model()
+
+# Image transformation
+transform_image = transforms.Compose([
+    transforms.Resize((1024, 1024)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+])
+
+@st.cache_data # Cache the processed images.
+def process(image):
+    image_size = image.size
+    input_images = transform_image(image).unsqueeze(0).to("cuda" if torch.cuda.is_available() else "cpu")
+    with torch.no_grad():
+        preds = birefnet(input_images)[-1].sigmoid().cpu()
+    pred = preds[0].squeeze()
+    pred_pil = transforms.ToPILImage()(pred)
+    mask = pred_pil.resize(image_size)
+    image.putalpha(mask)
+    return image
+
+def process_file(uploaded_file):
+    try:
+        image = Image.open(uploaded_file).convert("RGB")
+        transparent = process(image)
+
+        # Convert to bytes for download
+        img_bytes = io.BytesIO()
+        transparent.save(img_bytes, format="PNG")
+        img_bytes = img_bytes.getvalue()
+
+        return img_bytes, transparent # Return bytes for download and PIL image for display
+
+    except Exception as e:
+        st.error(f"An error occurred: {e}")
+        return None, None
+
+
+st.title("Background Removal Tool")
+
+# Tabs for different input methods
+tabs = ["Image Upload", "URL Input", "File Output"]
+selected_tab = st.sidebar.radio("Select Input Method", tabs)
+
+if selected_tab == "Image Upload":
+    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file).convert("RGB")
+        processed_image = process(image)
+        st.image(processed_image, caption="Processed Image")
+
+elif selected_tab == "URL Input":
+    image_url = st.text_input("Paste an image URL")
+    if image_url:
+        try:
+            import requests
+            from io import BytesIO
+            response = requests.get(image_url, stream=True)
+            response.raise_for_status()  # Raise an exception for bad status codes
+            image = Image.open(BytesIO(response.content)).convert("RGB")
+            processed_image = process(image)
+            st.image(processed_image, caption="Processed Image from URL")
+        except requests.exceptions.RequestException as e:
+            st.error(f"Error fetching image from URL: {e}")
+        except Exception as e:
+            st.error(f"Error processing image: {e}")
+
+
+elif selected_tab == "File Output":
+    uploaded_file = st.file_uploader("Upload an image for file output", type=["jpg", "jpeg", "png"])
+    if uploaded_file is not None:
+        file_bytes, processed_image = process_file(uploaded_file)
+        if file_bytes:
+            st.image(processed_image, caption="Processed Image") # Display the image
+            st.download_button(
+                label="Download PNG",
+                data=file_bytes,
+                file_name=f"{uploaded_file.name.rsplit('.', 1)[0]}.png",
+                mime="image/png",
+            )

requirements.txt

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+Pillow==11.1.0
+Requests==2.32.3
+streamlit==1.42.0
+torch==2.6.0
+torchvision==0.21.0
+transformers==4.48.3