Upload 4 files

config.toml

@@ -0,0 +1,2 @@
+[server]
+maxUploadSize = 5

main.py

@@ -0,0 +1,137 @@
+import streamlit as st
+from PIL import Image
+import torch
+from torchvision import transforms
+from transformers import AutoModelForImageSegmentation
+import io
+import os
+import sys
+
+# Import allowed image extensions
+IMAGE_EXTENSIONS = [
+    ".bmp",
+    ".dng",
+    ".jpeg",
+    ".jpg",
+    ".mpo",
+    ".png",
+    ".tif",
+    ".tiff",
+    ".webp",
+    ".pfm",
+]
+
+# Setup constants
+OUTPUT_FOLDER = 'output_images'
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Create output folder if it doesn't exist
+if not os.path.exists(OUTPUT_FOLDER):
+    os.makedirs(OUTPUT_FOLDER)
+
+@st.cache_resource
+def load_model():
+    """Load the BiRefNet model with caching"""
+    try:
+        torch.set_float32_matmul_precision("high")
+        model = AutoModelForImageSegmentation.from_pretrained(
+            "ZhengPeng7/BiRefNet_lite", 
+            trust_remote_code=True
+        )
+        return model.to(DEVICE)
+    except Exception as e:
+        st.error(f"Error loading model: {str(e)}")
+        raise
+
+def process_image(image, model):
+    """Process a single image and remove its background"""
+    # Define image transformation pipeline
+    transform_image = transforms.Compose([
+        transforms.Resize((1024, 1024)),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+    
+    # Prepare image
+    image = image.convert("RGB")
+    original_size = image.size
+    input_tensor = transform_image(image).unsqueeze(0).to(DEVICE)
+    
+    # Process image
+    with torch.no_grad():
+        preds = model(input_tensor)[-1].sigmoid().cpu()
+        pred = preds[0].squeeze()
+        mask = transforms.ToPILImage()(pred).resize(original_size)
+        
+        # Apply mask to original image
+        result = image.copy()
+        result.putalpha(mask)
+        
+    return result
+
+def sidebar_data():
+    # Title and introduction
+    st.sidebar.title("Kenapa?")
+    st.sidebar.write("""
+    Saya nggak akan upload ini kalau web hapus background yang di post di IMPHNEN dijadiin berbayar.
+    """)
+    st.sidebar.markdown("Menggunakan model dari [Birefnet](https://github.com/ZhengPeng7/BiRefNet) versi lite yang ukurannya 170mb hihihi.")
+
+    # System information
+    st.sidebar.markdown("### SysInfo()")
+    st.sidebar.code(f"""
+Python: {sys.version.split()[0]}
+Torch: {torch.__version__}
+Torchvision: {torch.__version__}
+""")
+
+
+def main():
+    st.title("Web untuk hapus background")
+    st.write("Upload gambar untuk diproses")
+
+    sidebar_data()
+    
+
+    # File uploader with supported extensions
+    uploaded_file = st.file_uploader(
+        "Pilih gambar...",
+        type=[ext.replace(".", "") for ext in IMAGE_EXTENSIONS]
+    )
+    
+    if uploaded_file is not None:
+        try:
+            # Load the model (will use cached version if already loaded)
+            model = load_model()
+            
+            # Load and display original image
+            image = Image.open(io.BytesIO(uploaded_file.read()))
+            col1, col2 = st.columns(2)
+            
+            with col1:
+                st.subheader("Gambar asli")
+                st.image(image, use_container_width=True)  # Updated parameter
+            
+            # Process image and display result
+            with st.spinner("Removing background..."):
+                result_image = process_image(image, model)
+            
+            with col2:
+                st.subheader("Hasilnya")
+                st.image(result_image, use_container_width=True)  # Updated parameter
+            
+            # Add download button for processed image
+            buf = io.BytesIO()
+            result_image.save(buf, format='PNG')
+            st.download_button(
+                label="Download hasil",
+                data=buf.getvalue(),
+                file_name="processed_image.png",
+                mime="image/png"
+            )
+            
+        except Exception as e:
+            st.error(f"An error occurred: {str(e)}")
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,59 @@
+altair==5.5.0
+attrs==24.3.0
+blinker==1.9.0
+cachetools==5.5.0
+certifi==2024.12.14
+charset-normalizer==3.4.1
+click==8.1.8
+einops==0.8.0
+filelock==3.13.1
+fsspec==2024.2.0
+gitdb==4.0.11
+GitPython==3.1.43
+huggingface-hub==0.27.0
+idna==3.10
+Jinja2==3.1.3
+jsonschema==4.23.0
+jsonschema-specifications==2024.10.1
+kornia==0.7.4
+kornia_rs==0.1.7
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+mdurl==0.1.2
+mpmath==1.3.0
+narwhals==1.19.1
+networkx==3.2.1
+numpy==1.26.3
+packaging==24.2
+pandas==2.2.3
+pillow==10.2.0
+protobuf==5.29.2
+pyarrow==18.1.0
+pydeck==0.9.1
+Pygments==2.18.0
+python-dateutil==2.9.0.post0
+pytz==2024.2
+PyYAML==6.0.2
+referencing==0.35.1
+regex==2024.11.6
+requests==2.32.3
+rich==13.9.4
+rpds-py==0.22.3
+safetensors==0.4.5
+six==1.17.0
+smmap==5.0.1
+streamlit==1.41.1
+sympy==1.13.1
+tenacity==9.0.0
+timm==1.0.12
+tokenizers==0.21.0
+toml==0.10.2
+torch==2.4.1+cpu
+torchvision==0.19.1+cpu
+tornado==6.4.2
+tqdm==4.67.1
+transformers==4.47.1
+typing_extensions==4.12.2
+tzdata==2024.2
+urllib3==2.3.0
+watchdog==6.0.0

runtime.txt

@@ -0,0 +1 @@
+python=3.9