Resolve conflicts: remove unnecessary files for security

.dockerignore

@@ -0,0 +1,60 @@
+# Git
+.git
+.gitignore
+
+# Python cache
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+
+# Virtual environments
+vreyesam_env/
+venv/
+env/
+ENV/
+
+# Build artifacts
+build/
+dist/
+*.egg-info/
+
+# Data and models (downloaded during build)
+VRBiomSegM/
+segment-anything-2/
+
+# Outputs
+*.jpg
+*.png
+*.jpeg
+loss_plots/
+predictions/
+results/
+output/
+VREyeSAM_results/
+
+# Jupyter
+.ipynb_checkpoints/
+*.ipynb
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Documentation
+docs/`
+*.md
+!README.md
+
+# Training scripts (not needed for deployment)
+Training.py
+Test.py
+Inference.py

.gitignore

@@ -0,0 +1,62 @@
+# Virtual Environment
+vreyesam_env/
+venv/
+env/
+ENV/
+
+# Python cache and compiled files
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Large external directories
+segment-anything-2/
+
+# Model checkpoints and weights
+*.torch
+*.pth
+*.pt
+
+# Data directories
+VRBiomSegM/
+
+# Output files
+*.jpg
+*.png
+*.jpeg
+loss_plots/
+predictions/
+results/
+output/
+
+# Jupyter Notebook
+.ipynb_checkpoints/
+*.ipynb
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+Thumbs.db

.streamlit/config.toml

@@ -0,0 +1,13 @@
+[server]
+maxUploadSize = 500
+headless = true
+port = 7860
+address = "0.0.0.0"
+enableCORS = false
+enableXsrfProtection = false
+
+[browser]
+gatherUsageStats = false
+
+[theme]
+base = "light"

app.py

@@ -0,0 +1,280 @@
+import streamlit as st
+import cv2
+import torch
+import numpy as np
+from PIL import Image
+import io
+import sys
+import os
+
+# Add segment-anything-2 to path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "segment-anything-2"))
+
+from sam2.build_sam import build_sam2
+from sam2.sam2_image_predictor import SAM2ImagePredictor
+
+# Page config
+st.set_page_config(
+    page_title="VREyeSAM - Non-frontal Iris Segmentation",
+    page_icon="👁️",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS
+st.markdown("""
+    <style>
+    .main {
+        padding: 2rem;
+    }
+    .stButton>button {
+        width: 100%;
+        background-color: #4CAF50;
+        color: white;
+        padding: 0.5rem;
+        font-size: 16px;
+    }
+    .result-box {
+        border: 2px solid #ddd;
+        border-radius: 10px;
+        padding: 1rem;
+        margin: 1rem 0;
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+@st.cache_resource
+def load_model():
+    """Load the VREyeSAM model"""
+    try:
+        # IMPORTANT: Hydra config system searches within sam2 package
+        # Use relative path without "segment-anything-2/" prefix
+        model_cfg = "configs/sam2/sam2_hiera_s.yaml"
+        sam2_checkpoint = "segment-anything-2/checkpoints/sam2_hiera_small.pt"
+        fine_tuned_weights = "segment-anything-2/checkpoints/VREyeSAM_uncertainity_best.torch"
+        
+        # Load model
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        
+        sam2_model = build_sam2(model_cfg, sam2_checkpoint, device=device)
+        predictor = SAM2ImagePredictor(sam2_model)
+        predictor.model.load_state_dict(torch.load(fine_tuned_weights, map_location=device))
+        
+        return predictor
+    except Exception as e:
+        st.error(f"Error loading model: {str(e)}")
+        return None
+
+def read_and_resize_image(image):
+    """Read and resize image for processing"""
+    img = np.array(image)
+    if len(img.shape) == 2:  # Grayscale
+        img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
+    elif img.shape[2] == 4:  # RGBA
+        img = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB)
+    
+    # Resize if needed
+    r = np.min([1024 / img.shape[1], 1024 / img.shape[0]])
+    if r < 1:
+        img = cv2.resize(img, (int(img.shape[1] * r), int(img.shape[0] * r)))
+    
+    return img
+
+def segment_iris(predictor, image):
+    """Perform iris segmentation"""
+    # Generate random points for inference
+    num_samples = 30
+    input_points = np.random.randint(0, min(image.shape[:2]), (num_samples, 1, 2))
+    
+    # Inference
+    with torch.no_grad():
+        predictor.set_image(image)
+        masks, scores, _ = predictor.predict(
+            point_coords=input_points,
+            point_labels=np.ones([input_points.shape[0], 1])
+        )
+    
+    # Convert to numpy
+    np_masks = np.array(masks[:, 0]).astype(np.float32)
+    np_scores = scores[:, 0]
+    
+    # Normalize scores
+    score_sum = np.sum(np_scores)
+    if score_sum > 0:
+        normalized_scores = np_scores / score_sum
+    else:
+        normalized_scores = np.ones_like(np_scores) / len(np_scores)
+    
+    # Generate probabilistic mask
+    prob_mask = np.sum(np_masks * normalized_scores[:, None, None], axis=0)
+    prob_mask = np.clip(prob_mask, 0, 1)
+    
+    # Threshold to get binary mask
+    binary_mask = (prob_mask > 0.2).astype(np.uint8)
+    
+    return binary_mask, prob_mask
+
+def overlay_mask_on_image(image, binary_mask, color=(0, 255, 0), alpha=0.5):
+    """Overlay binary mask on original image"""
+    overlay = image.copy()
+    mask_colored = np.zeros_like(image)
+    mask_colored[binary_mask > 0] = color
+    
+    # Blend
+    result = cv2.addWeighted(overlay, 1-alpha, mask_colored, alpha, 0)
+    
+    return result
+
+# Main App
+def main():
+    st.title("👁️ VREyeSAM: Non-Frontal Iris Segmentation")
+    st.markdown("""
+    Upload a non-frontal iris image captured in VR/AR environments, and VREyeSAM will segment the iris region 
+    using a fine-tuned SAM2 model with uncertainty-weighted loss.
+    """)
+    
+    # Sidebar
+    with st.sidebar:
+        st.header("About VREyeSAM")
+        st.markdown("""
+        **VREyeSAM** is a robust non-frontal iris segmentation framework designed for images captured under:
+        - Varying gaze directions
+        - Partial occlusions
+        - Inconsistent lighting conditions
+        
+        **Model Performance:**
+        - Recall: 0.870
+        - F1-Score: 0.806
+        """)
+        
+        st.header("Settings")
+        show_overlay = st.checkbox("Show Mask Overlay", value=True)
+        show_probabilistic = st.checkbox("Show Probabilistic Mask", value=False)
+    
+    # Load model
+    with st.spinner("Loading VREyeSAM model..."):
+        predictor = load_model()
+    
+    if predictor is None:
+        st.error("Failed to load model. Please check the setup.")
+        return
+    
+    st.success("✅ Model loaded successfully!")
+    
+    # File uploader with increased size limit
+    uploaded_file = st.file_uploader(
+        "Upload an iris image (JPG, PNG, JPEG)",
+        type=["jpg", "png", "jpeg"],
+        help="Upload a non-frontal iris image for segmentation"
+    )
+    
+    if uploaded_file is not None:
+        try:
+            # Display original image
+            image = Image.open(uploaded_file)
+            
+            col1, col2 = st.columns(2)
+            
+            with col1:
+                st.subheader("📷 Original Image")
+                st.image(image, use_container_width=True)
+            
+            # Process button
+            if st.button("🔍 Segment Iris", type="primary"):
+                with st.spinner("Segmenting iris..."):
+                    try:
+                        # Prepare image
+                        img_array = read_and_resize_image(image)
+                        
+                        # Perform segmentation
+                        binary_mask, prob_mask = segment_iris(predictor, img_array)
+                        
+                        with col2:
+                            st.subheader("🎯 Binary Mask")
+                            binary_mask_img = (binary_mask * 255).astype(np.uint8)
+                            st.image(binary_mask_img, use_container_width=True)
+                        
+                        # Additional results
+                        st.markdown("---")
+                        st.subheader("📊 Segmentation Results")
+                        
+                        result_cols = st.columns(2)
+                        
+                        with result_cols[0]:
+                            if show_overlay:
+                                st.markdown("**Overlay View**")
+                                overlay = overlay_mask_on_image(img_array, binary_mask)
+                                st.image(overlay, use_container_width=True)
+                        
+                        with result_cols[1]:
+                            if show_probabilistic:
+                                st.markdown("**Probabilistic Mask**")
+                                prob_mask_img = (prob_mask * 255).astype(np.uint8)
+                                st.image(prob_mask_img, use_container_width=True)
+                        
+                        # Download options
+                        st.markdown("---")
+                        st.subheader("💾 Download Results")
+                        
+                        download_cols = st.columns(2)
+                        
+                        with download_cols[0]:
+                            # Binary mask download
+                            binary_pil = Image.fromarray(binary_mask_img)
+                            buf = io.BytesIO()
+                            binary_pil.save(buf, format="PNG")
+                            st.download_button(
+                                label="Download Binary Mask",
+                                data=buf.getvalue(),
+                                file_name="binary_mask.png",
+                                mime="image/png"
+                            )
+                        
+                        with download_cols[1]:
+                            if show_overlay:
+                                # Overlay download
+                                overlay_pil = Image.fromarray(cv2.cvtColor(overlay, cv2.COLOR_BGR2RGB))
+                                buf = io.BytesIO()
+                                overlay_pil.save(buf, format="PNG")
+                                st.download_button(
+                                    label="Download Overlay",
+                                    data=buf.getvalue(),
+                                    file_name="overlay.png",
+                                    mime="image/png"
+                                )
+                        
+                        # Statistics
+                        st.markdown("---")
+                        st.subheader("📈 Segmentation Statistics")
+                        stats_cols = st.columns(3)
+                        
+                        mask_area = np.sum(binary_mask > 0)
+                        total_area = binary_mask.shape[0] * binary_mask.shape[1]
+                        coverage = (mask_area / total_area) * 100
+                        
+                        with stats_cols[0]:
+                            st.metric("Mask Coverage", f"{coverage:.2f}%")
+                        with stats_cols[1]:
+                            st.metric("Image Size", f"{img_array.shape[1]}x{img_array.shape[0]}")
+                        with stats_cols[2]:
+                            st.metric("Mask Area (pixels)", f"{mask_area:,}")
+                    
+                    except Exception as e:
+                        st.error(f"❌ Error during segmentation: {str(e)}")
+        
+        except Exception as e:
+            st.error(f"❌ Error loading image: {str(e)}")
+            st.info("Please try uploading a different image or reducing the file size.")
+
+    # Footer
+    st.markdown("---")
+    st.markdown("""
+    <div style='text-align: center'>
+        <p><strong>VREyeSAM</strong> - Virtual Reality Non-Frontal Iris Segmentation</p>
+        <p>🔗 <a href='https://github.com/GeetanjaliGTZ/VREyeSAM'>GitHub</a> | 
+        📧 <a href='mailto:geetanjalisharma546@gmail.com'>Contact</a></p>
+    </div>
+    """, unsafe_allow_html=True)
+
+if __name__ == "__main__":
+    main()

deploy.bat

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+@echo off
+REM VREyeSAM Deployment Script for Hugging Face Spaces (Windows)
+REM This script automates the deployment process
+
+echo ============================================================
+echo VREyeSAM Deployment to Hugging Face Spaces
+echo ============================================================
+echo.
+
+REM Step 1: Check prerequisites
+echo [1/8] Checking prerequisites...
+
+where git >nul 2>&1
+if errorlevel 1 (
+    echo [ERROR] Git is not installed
+    echo Install from: https://git-scm.com/
+    pause
+    exit /b 1
+)
+echo [OK] Git installed
+
+where git-lfs >nul 2>&1
+if errorlevel 1 (
+    echo [ERROR] Git LFS is not installed
+    echo Install from: https://git-lfs.github.com/
+    pause
+    exit /b 1
+)
+echo [OK] Git LFS installed
+echo.
+
+REM Step 2: Get Hugging Face credentials
+echo [2/8] Hugging Face Setup
+set /p HF_USERNAME="Enter your Hugging Face username: "
+set /p HF_SPACE_NAME="Enter your Space name: "
+
+set HF_SPACE_URL=https://huggingface.co/spaces/%HF_USERNAME%/%HF_SPACE_NAME%
+
+echo Space URL: %HF_SPACE_URL%
+set /p CONFIRM="Is this correct? (y/n): "
+
+if /i not "%CONFIRM%"=="y" (
+    echo Aborted.
+    pause
+    exit /b 0
+)
+echo.
+
+REM Step 3: Initialize Git repository
+echo [3/8] Initializing Git repository...
+
+if not exist ".git" (
+    git init
+    echo [OK] Git repository initialized
+) else (
+    echo [OK] Git repository already exists
+)
+echo.
+
+REM Step 4: Setup Git LFS
+echo [4/8] Setting up Git LFS...
+
+git lfs install
+git lfs track "*.pt"
+git lfs track "*.torch"
+git lfs track "*.pth"
+git lfs track "*.bin"
+
+echo [OK] Git LFS configured
+echo.
+
+REM Step 5: Verify required files
+echo [5/8] Verifying required files...
+
+set MISSING=0
+
+if exist "app.py" (
+    echo [OK] app.py
+) else (
+    echo [ERROR] app.py missing
+    set MISSING=1
+)
+
+if exist "requirements.txt" (
+    echo [OK] requirements.txt
+) else (
+    echo [ERROR] requirements.txt missing
+    set MISSING=1
+)
+
+if exist "README.md" (
+    echo [OK] README.md
+) else (
+    echo [ERROR] README.md missing
+    set MISSING=1
+)
+
+if exist ".gitattributes" (
+    echo [OK] .gitattributes
+) else (
+    echo [ERROR] .gitattributes missing
+    set MISSING=1
+)
+
+if %MISSING%==1 (
+    echo.
+    echo [ERROR] Missing required files. Please add them first.
+    pause
+    exit /b 1
+)
+echo.
+
+REM Check SAM2 files
+echo Checking SAM2 files...
+
+if not exist "segment-anything-2" (
+    echo [ERROR] segment-anything-2 directory not found
+    echo Please clone SAM2 repository first.
+    pause
+    exit /b 1
+)
+echo [OK] segment-anything-2 directory
+
+if not exist "segment-anything-2\checkpoints\sam2_hiera_small.pt" (
+    echo [ERROR] sam2_hiera_small.pt not found
+    pause
+    exit /b 1
+)
+echo [OK] sam2_hiera_small.pt
+
+if not exist "segment-anything-2\checkpoints\VREyeSAM_uncertainity_best.torch" (
+    echo [ERROR] VREyeSAM_uncertainity_best.torch not found
+    pause
+    exit /b 1
+)
+echo [OK] VREyeSAM_uncertainity_best.torch
+echo.
+
+REM Step 6: Add remote
+echo [6/8] Adding Hugging Face remote...
+
+git remote remove space >nul 2>&1
+git remote add space %HF_SPACE_URL%
+
+echo [OK] Remote added: %HF_SPACE_URL%
+echo.
+
+REM Step 7: Commit files
+echo [7/8] Committing files...
+
+git add .
+git commit -m "Deploy VREyeSAM to Hugging Face Spaces"
+
+echo [OK] Files committed
+echo.
+
+REM Step 8: Push to Hugging Face
+echo [8/8] Pushing to Hugging Face Spaces...
+echo This may take several minutes due to large model files...
+echo.
+
+set /p PUSH_CONFIRM="Ready to push? (y/n): "
+
+if /i not "%PUSH_CONFIRM%"=="y" (
+    echo Push cancelled. You can push manually later with:
+    echo   git push --set-upstream space main --force
+    pause
+    exit /b 0
+)
+
+echo Pushing to Hugging Face...
+git push --set-upstream space main --force
+
+if errorlevel 1 (
+    echo.
+    echo ============================================================
+    echo [ERROR] Deployment failed
+    echo ============================================================
+    echo.
+    echo Common issues:
+    echo 1. Authentication: Make sure you're logged in
+    echo 2. Space doesn't exist: Create it first on Hugging Face
+    echo 3. Large files: Ensure Git LFS is properly configured
+    echo.
+    pause
+    exit /b 1
+) else (
+    echo.
+    echo ============================================================
+    echo [SUCCESS] Deployment successful!
+    echo ============================================================
+    echo.
+    echo Your space is available at:
+    echo %HF_SPACE_URL%
+    echo.
+    echo It may take 10-15 minutes to build. Monitor progress at:
+    echo %HF_SPACE_URL%/logs
+    echo.
+    pause
+)

packages.txt

@@ -0,0 +1,2 @@
+libgl1
+libglib2.0-0

requirements_deploy.txt

@@ -0,0 +1,27 @@
+# Streamlit for web interface
+streamlit>=1.28.0
+
+# Core ML and Deep Learning
+torch>=2.0.0,<2.5.0
+torchvision>=0.15.0,<0.20.0
+numpy>=1.22.0,<2.0.0
+
+# Computer Vision
+opencv-python-headless>=4.5.0
+Pillow>=8.0.0
+
+# Data Processing
+pandas>=1.3.0
+scikit-learn>=1.0.0
+
+# Visualization
+matplotlib>=3.5.0
+
+# Utility
+tqdm>=4.62.0
+hydra-core>=1.1.0
+
+# For downloading model weights
+huggingface-hub>=0.19.0
+
+# Note: SAM2 will be installed from git in Dockerfile

segment-anything-2

@@ -0,0 +1 @@
+Subproject commit 2b90b9f5ceec907a1c18123530e92e794ad901a4

test_app_local.py

@@ -0,0 +1,205 @@
+#!/usr/bin/env python3
+"""
+Local Testing Script for VREyeSAM Streamlit App
+
+Run this script to test the app locally before deploying to Hugging Face Spaces.
+Usage: python test_app_local.py
+"""
+
+import subprocess
+import sys
+import os
+import time
+
+def check_dependencies():
+    """Check if all required dependencies are installed"""
+    print("🔍 Checking dependencies...")
+    
+    required_packages = [
+        'streamlit',
+        'torch',
+        'torchvision',
+        'opencv-python',
+        'numpy',
+        'PIL'
+    ]
+    
+    missing = []
+    for package in required_packages:
+        try:
+            __import__(package.replace('-', '_'))
+            print(f"   ✅ {package}")
+        except ImportError:
+            print(f"   ❌ {package}")
+            missing.append(package)
+    
+    if missing:
+        print(f"\n⚠️  Missing packages: {', '.join(missing)}")
+        print("Install them with: pip install -r requirements_deploy.txt")
+        return False
+    
+    print("✅ All dependencies installed\n")
+    return True
+
+def check_model_files():
+    """Check if model files exist"""
+    print("🔍 Checking model files...")
+    
+    files_to_check = [
+        "segment-anything-2/checkpoints/sam2_hiera_small.pt",
+        "segment-anything-2/checkpoints/VREyeSAM_uncertainity_best.torch"
+    ]
+    
+    all_exist = True
+    for file_path in files_to_check:
+        if os.path.exists(file_path):
+            size_mb = os.path.getsize(file_path) / (1024 * 1024)
+            print(f"   ✅ {file_path} ({size_mb:.1f} MB)")
+        else:
+            print(f"   ❌ {file_path} - NOT FOUND")
+            all_exist = False
+    
+    if not all_exist:
+        print("\n⚠️  Some model files are missing!")
+        print("Please run the setup instructions from README.md")
+        return False
+    
+    print("✅ All model files present\n")
+    return True
+
+def check_sam2_installation():
+    """Check if SAM2 is properly installed"""
+    print("🔍 Checking SAM2 installation...")
+    
+    try:
+        sys.path.insert(0, "segment-anything-2")
+        from sam2.build_sam import build_sam2
+        from sam2.sam2_image_predictor import SAM2ImagePredictor
+        print("   ✅ SAM2 modules can be imported")
+        print("✅ SAM2 properly installed\n")
+        return True
+    except ImportError as e:
+        print(f"   ❌ SAM2 import failed: {e}")
+        print("\n⚠️  SAM2 not properly installed!")
+        print("Install with:")
+        print("  git clone https://github.com/facebookresearch/segment-anything-2")
+        print("  cd segment-anything-2")
+        print("  pip install -e .")
+        return False
+
+def test_app_syntax():
+    """Check if app.py has syntax errors"""
+    print("🔍 Checking app.py syntax...")
+    
+    try:
+        with open('app.py', 'r', encoding='utf-8') as f:
+            code = f.read()
+            compile(code, 'app.py', 'exec')
+        print("   ✅ No syntax errors")
+        print("✅ app.py syntax valid\n")
+        return True
+    except SyntaxError as e:
+        print(f"   ❌ Syntax error in app.py: {e}")
+        return False
+    except UnicodeDecodeError as e:
+        print(f"   ⚠️  Unicode encoding issue: {e}")
+        print("   Trying with different encoding...")
+        try:
+            with open('app.py', 'r', encoding='latin-1') as f:
+                code = f.read()
+                compile(code, 'app.py', 'exec')
+            print("   ✅ No syntax errors (latin-1 encoding)")
+            print("✅ app.py syntax valid\n")
+            return True
+        except Exception as e2:
+            print(f"   ❌ Still failed: {e2}")
+            return False
+
+def run_streamlit_app():
+    """Launch the Streamlit app"""
+    print("🚀 Launching Streamlit app...")
+    print("=" * 60)
+    print("The app will open in your browser at http://localhost:8501")
+    print("Press Ctrl+C to stop the app")
+    print("=" * 60)
+    print()
+    
+    try:
+        subprocess.run(['streamlit', 'run', 'app.py'], check=True)
+    except KeyboardInterrupt:
+        print("\n\n✅ App stopped by user")
+    except subprocess.CalledProcessError as e:
+        print(f"\n❌ Error running app: {e}")
+        return False
+    
+    return True
+
+def create_test_image():
+    """Create a simple test image if none exists"""
+    print("🔍 Checking for test images...")
+    
+    test_dir = "test_images"
+    if not os.path.exists(test_dir):
+        os.makedirs(test_dir)
+        print(f"   📁 Created {test_dir} directory")
+    
+    # Check if there are any test images
+    image_files = [f for f in os.listdir(test_dir) if f.endswith(('.jpg', '.png', '.jpeg'))]
+    
+    if image_files:
+        print(f"   ✅ Found {len(image_files)} test image(s)")
+        print(f"   📂 Test images in: {test_dir}/")
+        for img in image_files:
+            print(f"      - {img}")
+    else:
+        print(f"   ℹ️  No test images found in {test_dir}/")
+        print(f"   💡 Add some iris images to {test_dir}/ for testing")
+    
+    print()
+
+def main():
+    """Main testing function"""
+    print("\n" + "=" * 60)
+    print("VREyeSAM Local Testing Suite")
+    print("=" * 60 + "\n")
+    
+    # Run all checks
+    checks = [
+        ("Dependencies", check_dependencies),
+        ("Model Files", check_model_files),
+        ("SAM2 Installation", check_sam2_installation),
+        ("App Syntax", test_app_syntax),
+    ]
+    
+    all_passed = True
+    for name, check_func in checks:
+        if not check_func():
+            all_passed = False
+            print(f"❌ {name} check failed\n")
+    
+    # Create test image directory
+    create_test_image()
+    
+    if not all_passed:
+        print("=" * 60)
+        print("⚠️  Some checks failed. Please fix the issues above.")
+        print("=" * 60)
+        sys.exit(1)
+    
+    print("=" * 60)
+    print("✅ All checks passed! Ready to run the app.")
+    print("=" * 60)
+    print()
+    
+    # Ask user if they want to run the app
+    response = input("Do you want to launch the app now? (y/n): ").strip().lower()
+    
+    if response == 'y':
+        run_streamlit_app()
+    else:
+        print("\n✅ Testing complete!")
+        print("To run the app manually, execute: streamlit run app.py")
+        print()
+
+if __name__ == "__main__":
+    main()

windows.bat

@@ -0,0 +1,91 @@
+@echo off
+REM VREyeSAM Setup Script for Windows
+REM This script sets up the environment and downloads required files
+
+echo ============================================================
+echo VREyeSAM Windows Setup Script
+echo ============================================================
+echo.
+
+REM Check if Python is installed
+python --version >nul 2>&1
+if errorlevel 1 (
+    echo [ERROR] Python is not installed or not in PATH
+    echo Please install Python 3.11 from https://www.python.org/
+    pause
+    exit /b 1
+)
+
+echo [1/6] Creating virtual environment...
+if exist vreyesam_env (
+    echo Virtual environment already exists, skipping...
+) else (
+    python -m venv vreyesam_env
+    echo Done!
+)
+echo.
+
+echo [2/6] Activating virtual environment...
+call vreyesam_env\Scripts\activate.bat
+echo Done!
+echo.
+
+echo [3/6] Installing dependencies...
+echo This may take a few minutes...
+python -m pip install --upgrade pip
+pip install streamlit
+pip install torch==2.3.0 torchvision==0.18.0 --index-url https://download.pytorch.org/whl/cu118
+pip install "numpy<2.0.0"
+pip install opencv-python-headless pillow pandas scikit-learn matplotlib tqdm hydra-core
+echo Done!
+echo.
+
+echo [4/6] Cloning SAM2 repository...
+if exist segment-anything-2 (
+    echo SAM2 repository already exists, skipping...
+) else (
+    git clone https://github.com/facebookresearch/segment-anything-2
+    echo Done!
+)
+echo.
+
+echo [5/6] Installing SAM2...
+cd segment-anything-2
+pip install -e .
+cd ..
+echo Done!
+echo.
+
+echo [6/6] Downloading model checkpoints...
+if not exist segment-anything-2\checkpoints mkdir segment-anything-2\checkpoints
+
+REM Download SAM2 base checkpoint
+if exist segment-anything-2\checkpoints\sam2_hiera_small.pt (
+    echo SAM2 checkpoint already exists, skipping...
+) else (
+    echo Downloading SAM2 checkpoint (this may take a few minutes)...
+    powershell -Command "Invoke-WebRequest -Uri 'https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_small.pt' -OutFile 'segment-anything-2\checkpoints\sam2_hiera_small.pt'"
+    echo Done!
+)
+
+REM Download VREyeSAM weights
+if exist segment-anything-2\checkpoints\VREyeSAM_uncertainity_best.torch (
+    echo VREyeSAM weights already exist, skipping...
+) else (
+    echo Downloading VREyeSAM weights...
+    pip install huggingface-hub
+    huggingface-cli download devnagaich/VREyeSAM VREyeSAM_uncertainity_best.torch --local-dir segment-anything-2\checkpoints\
+    echo Done!
+)
+echo.
+
+echo ============================================================
+echo Setup Complete!
+echo ============================================================
+echo.
+echo To run the app:
+echo   1. Activate the environment: vreyesam_env\Scripts\activate.bat
+echo   2. Run: streamlit run app.py
+echo.
+echo Press any key to exit...
+pause >nul