Fix Error 403 in new config

.streamlit/config.toml

@@ -0,0 +1,10 @@
+[server]
+maxUploadSize = 500
+headless = true
+port = 7860
+address = "0.0.0.0"
+
+[browser]
+gatherUsageStats = false
+serverAddress = "0.0.0.0"
+serverPort = 7860

Dockerfile

@@ -17,7 +17,7 @@ RUN apt-get update && apt-get install -y \
 # Copy requirements first for better caching
 COPY requirements_deploy.txt .
 
-# Install Python dependencies with version constraints
+# Install Python dependencies
 RUN pip install --no-cache-dir --upgrade pip && \
     pip install --no-cache-dir -r requirements_deploy.txt
 
@@ -35,7 +35,7 @@ RUN wget --no-check-certificate \
     https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_small.pt \
     -O segment-anything-2/checkpoints/sam2_hiera_small.pt
 
-# Download VREyeSAM fine-tuned weights using Python
+# Download VREyeSAM weights using Python
 RUN pip install --no-cache-dir huggingface-hub && \
     python -c "from huggingface_hub import hf_hub_download; \
     hf_hub_download(repo_id='devnagaich/VREyeSAM', \
@@ -44,10 +44,13 @@ RUN pip install --no-cache-dir huggingface-hub && \
     local_dir_use_symlinks=False)"
 
 # Verify files were downloaded
-RUN ls -lh segment-anything-2/checkpoints/ && \
-    test -f segment-anything-2/checkpoints/sam2_hiera_small.pt && \
-    test -f segment-anything-2/checkpoints/VREyeSAM_uncertainity_best.torch && \
-    echo "All checkpoints downloaded successfully!"
+RUN ls -lh segment-anything-2/checkpoints/
+
+# Create Streamlit config directory
+RUN mkdir -p /root/.streamlit
+
+# Copy Streamlit config to increase upload size
+COPY .streamlit/config.toml /root/.streamlit/config.toml
 
 # Copy application files
 COPY app.py .
@@ -60,9 +63,10 @@ ENV STREAMLIT_SERVER_PORT=7860
 ENV STREAMLIT_SERVER_ADDRESS=0.0.0.0
 ENV STREAMLIT_SERVER_HEADLESS=true
 ENV STREAMLIT_BROWSER_GATHER_USAGE_STATS=false
+ENV STREAMLIT_SERVER_MAX_UPLOAD_SIZE=500
 
 # Health check
 HEALTHCHECK CMD curl --fail http://localhost:7860/_stcore/health || exit 1
 
 # Run the application
-CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0", "--server.maxUploadSize=500"]

app.py

@@ -6,7 +6,6 @@ from PIL import Image
 import io
 import sys
 import os
-import traceback
 
 # Add segment-anything-2 to path
 sys.path.insert(0, os.path.join(os.path.dirname(__file__), "segment-anything-2"))
@@ -18,7 +17,8 @@ from sam2.sam2_image_predictor import SAM2ImagePredictor
 st.set_page_config(
     page_title="VREyeSAM - Non-frontal Iris Segmentation",
     page_icon="👁️",
-    layout="wide"
+    layout="wide",
+    initial_sidebar_state="expanded"
 )
 
 # Custom CSS
@@ -47,31 +47,13 @@ st.markdown("""
 def load_model():
     """Load the VREyeSAM model"""
     try:
-        # Path handling for both local and Docker environments
+        # Correct paths as specified
         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"
         
-        # Verify files exist
-        if not os.path.exists(sam2_checkpoint):
-            st.error(f"❌ SAM2 checkpoint not found at: {sam2_checkpoint}")
-            st.info("Current directory: " + os.getcwd())
-            st.info("Directory contents: " + str(os.listdir(".")))
-            return None
-        
-        if not os.path.exists(fine_tuned_weights):
-            st.error(f"❌ VREyeSAM weights not found at: {fine_tuned_weights}")
-            return None
-        
-        # Check file sizes
-        sam2_size = os.path.getsize(sam2_checkpoint) / (1024 * 1024)
-        vresam_size = os.path.getsize(fine_tuned_weights) / (1024 * 1024)
-        st.info(f"📦 SAM2 checkpoint: {sam2_size:.1f} MB")
-        st.info(f"📦 VREyeSAM weights: {vresam_size:.1f} MB")
-        
         # Load model
         device = "cuda" if torch.cuda.is_available() else "cpu"
-        st.info(f"🖥️ Loading model on: {device.upper()}")
         
         sam2_model = build_sam2(model_cfg, sam2_checkpoint, device=device)
         predictor = SAM2ImagePredictor(sam2_model)
@@ -79,9 +61,7 @@ def load_model():
         
         return predictor
     except Exception as e:
-        st.error(f"❌ Error loading model: {str(e)}")
-        st.error("Full traceback:")
-        st.code(traceback.format_exc())
+        st.error(f"Error loading model: {str(e)}")
         return None
 
 def read_and_resize_image(image):
@@ -193,10 +173,8 @@ def main():
         - Inconsistent lighting conditions
         
         **Model Performance:**
-        - Precision: 0.751
         - Recall: 0.870
         - F1-Score: 0.806
-        - Mean IoU: 0.647
         """)
         
         st.header("Settings")
@@ -214,7 +192,7 @@ def main():
     
     st.success("✅ Model loaded successfully!")
     
-    # File uploader
+    # File uploader with increased size limit
     uploaded_file = st.file_uploader(
         "Upload an iris image (JPG, PNG, JPEG)",
         type=["jpg", "png", "jpeg"],
@@ -222,132 +200,134 @@ def main():
     )
     
     if uploaded_file is not None:
-        # 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)
-                    
-                    # Extract iris strip
-                    iris_strip = extract_iris_strip(img_array, binary_mask) if show_iris_strip else None
-                    
-                    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(3)
-                    
-                    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)
-                    
-                    with result_cols[2]:
-                        if show_iris_strip and iris_strip is not None:
-                            st.markdown("**Extracted Iris Strip**")
-                            st.image(iris_strip, use_container_width=True)
-                        elif show_iris_strip:
-                            st.warning("No iris region detected")
-                    
-                    # Download options
-                    st.markdown("---")
-                    st.subheader("💾 Download Results")
-                    
-                    download_cols = st.columns(3)
-                    
-                    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))
+        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)
+                        
+                        # Extract iris strip
+                        iris_strip = extract_iris_strip(img_array, binary_mask) if show_iris_strip else None
+                        
+                        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(3)
+                        
+                        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)
+                        
+                        with result_cols[2]:
+                            if show_iris_strip and iris_strip is not None:
+                                st.markdown("**Extracted Iris Strip**")
+                                st.image(iris_strip, use_container_width=True)
+                            elif show_iris_strip:
+                                st.warning("No iris region detected")
+                        
+                        # Download options
+                        st.markdown("---")
+                        st.subheader("💾 Download Results")
+                        
+                        download_cols = st.columns(3)
+                        
+                        with download_cols[0]:
+                            # Binary mask download
+                            binary_pil = Image.fromarray(binary_mask_img)
                             buf = io.BytesIO()
-                            overlay_pil.save(buf, format="PNG")
+                            binary_pil.save(buf, format="PNG")
                             st.download_button(
-                                label="Download Overlay",
+                                label="Download Binary Mask",
                                 data=buf.getvalue(),
-                                file_name="overlay.png",
+                                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"
+                                )
+                        
+                        with download_cols[2]:
+                            if iris_strip is not None:
+                                # Iris strip download
+                                strip_pil = Image.fromarray(cv2.cvtColor(iris_strip, cv2.COLOR_BGR2RGB))
+                                buf = io.BytesIO()
+                                strip_pil.save(buf, format="PNG")
+                                st.download_button(
+                                    label="Download Iris Strip",
+                                    data=buf.getvalue(),
+                                    file_name="iris_strip.png",
+                                    mime="image/png"
+                                )
+                        
+                        # Statistics
+                        st.markdown("---")
+                        st.subheader("📈 Segmentation Statistics")
+                        stats_cols = st.columns(4)
+                        
+                        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:,}")
+                        with stats_cols[3]:
+                            if iris_strip is not None:
+                                st.metric("Strip Size", f"{iris_strip.shape[1]}x{iris_strip.shape[0]}")
                     
-                    with download_cols[2]:
-                        if iris_strip is not None:
-                            # Iris strip download
-                            strip_pil = Image.fromarray(cv2.cvtColor(iris_strip, cv2.COLOR_BGR2RGB))
-                            buf = io.BytesIO()
-                            strip_pil.save(buf, format="PNG")
-                            st.download_button(
-                                label="Download Iris Strip",
-                                data=buf.getvalue(),
-                                file_name="iris_strip.png",
-                                mime="image/png"
-                            )
-                    
-                    # Statistics
-                    st.markdown("---")
-                    st.subheader("📈 Segmentation Statistics")
-                    stats_cols = st.columns(4)
-                    
-                    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:,}")
-                    with stats_cols[3]:
-                        if iris_strip is not None:
-                            st.metric("Strip Size", f"{iris_strip.shape[1]}x{iris_strip.shape[0]}")
-                
-                except Exception as e:
-                    st.error(f"❌ Error during segmentation: {str(e)}")
-                    st.error("Full traceback:")
-                    st.code(traceback.format_exc())
+                    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>Sharma et al., IJCB 2025</p>
         <p>🔗 <a href='https://github.com/GeetanjaliGTZ/VREyeSAM'>GitHub</a> | 
         📧 <a href='mailto:geetanjalisharma546@gmail.com'>Contact</a></p>
     </div>

requirements_deploy.txt

@@ -1,39 +1,27 @@
-# VREyeSAM Requirements - Fixed Version Constraints
-# Compatible with Python 3.11+
-# This version resolves NumPy conflicts with gensim and numba
+# Streamlit for web interface
+streamlit>=1.28.0
 
-# Web Interface
-streamlit>=1.28.0,<2.0.0
-
-# Core ML and Deep Learning - COMPATIBLE VERSIONS
-torch==2.3.0
-torchvision==0.18.0
+# 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,<5.0.0
-Pillow>=8.0.0,<11.0.0
+opencv-python-headless>=4.5.0
+Pillow>=8.0.0
 
-# Data Processing and ML
-pandas>=1.3.0,<3.0.0
-scikit-learn>=1.0.0,<2.0.0
+# Data Processing
+pandas>=1.3.0
+scikit-learn>=1.0.0
 
 # Visualization
-matplotlib>=3.5.0,<4.0.0
+matplotlib>=3.5.0
 
 # Utility
-tqdm>=4.62.0,<5.0.0
-hydra-core>=1.1.0,<2.0.0
-omegaconf>=2.1.0,<3.0.0
+tqdm>=4.62.0
+hydra-core>=1.1.0
 
 # For downloading model weights
-huggingface-hub>=0.19.0,<1.0.0
-
-# Note: Install PyTorch with CUDA support separately if needed:
-# For CUDA 11.8: pip install torch==2.3.0 torchvision==0.18.0 --index-url https://download.pytorch.org/whl/cu118
-# For CUDA 12.1: pip install torch==2.3.0 torchvision==0.18.0 --index-url https://download.pytorch.org/whl/cu121
-# For CPU only: pip install torch==2.3.0 torchvision==0.18.0 --index-url https://download.pytorch.org/whl/cpu
+huggingface-hub>=0.19.0
 
-# SAM2 will be installed separately from git:
-# git clone https://github.com/facebookresearch/segment-anything-2
-# cd segment-anything-2 && pip install -e . && cd ..
+# Note: SAM2 will be installed from git in Dockerfile