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.gitattributes

@@ -33,3 +33,16 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+social_moderation/data/output/images/1000_F_1566391003_PcPMXVvR99sK3Rf9YxJaChFZRhD7XuAx_blurred.jpg filter=lfs diff=lfs merge=lfs -text
+social_moderation/data/output/images/Screenshot[[:space:]]2025-10-12[[:space:]]162027_blurred.png filter=lfs diff=lfs merge=lfs -text
+social_moderation/data/output/images/Screenshot[[:space:]]2025-10-12[[:space:]]164637_blurred.png filter=lfs diff=lfs merge=lfs -text
+social_moderation/data/output/images/stock-photo-hand-of-man-injured-wound-from-accident-and-blood-bleeding-on-white-background-insurance-concept-519504064_blurred.jpg filter=lfs diff=lfs merge=lfs -text
+social_moderation/data/output/images/test_faces_blurred.jpeg filter=lfs diff=lfs merge=lfs -text
+social_moderation/data/output/images/WhatsApp[[:space:]]Image[[:space:]]2025-10-12[[:space:]]at[[:space:]]1.42.43[[:space:]]AM[[:space:]](1)_blurred.jpeg filter=lfs diff=lfs merge=lfs -text
+social_moderation/data/output/images/WhatsApp[[:space:]]Image[[:space:]]2025-10-12[[:space:]]at[[:space:]]4.50.22[[:space:]]PM_blurred.jpeg filter=lfs diff=lfs merge=lfs -text
+social_moderation/data/output/images/WhatsApp[[:space:]]Image[[:space:]]2025-10-12[[:space:]]at[[:space:]]4.50.50[[:space:]]PM_blurred.jpeg filter=lfs diff=lfs merge=lfs -text
+social_moderation/data/output/images/WhatsApp[[:space:]]Image[[:space:]]2025-10-12[[:space:]]at[[:space:]]4.51.33[[:space:]]PM_blurred.jpeg filter=lfs diff=lfs merge=lfs -text
+social_moderation/data/output/images/WordItOut-word-cloud-3116414-e1610584584107_blurred_blurred.png filter=lfs diff=lfs merge=lfs -text
+social_moderation/data/output/images/WordItOut-word-cloud-3116414-e1610584584107_blurred.png filter=lfs diff=lfs merge=lfs -text
+social_moderation/data/output/videos/WhatsApp[[:space:]]Video[[:space:]]2025-09-21[[:space:]]at[[:space:]]11.40.51[[:space:]]AM_blurred.mp4 filter=lfs diff=lfs merge=lfs -text
+social_moderation/data/output/videos/WhatsApp[[:space:]]Video[[:space:]]2025-09-21[[:space:]]at[[:space:]]11.40.59[[:space:]]AM_blurred.mp4 filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,18 @@
+FROM python:3.10-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    libsm6 \
+    libxext6 \
+    libxrender-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["python", "social_moderation/app.py"]

app.py

@@ -0,0 +1,214 @@
+"""
+Gradio Web UI for Smart Content Moderation System
+Face Blur + Hate Speech + Blood/NSFW Detection
+AUTO-LAUNCHES BROWSER + AUTO-SAVES TO OUTPUT FOLDER
+"""
+
+import gradio as gr
+import tempfile
+import os
+import webbrowser
+import time
+import shutil
+from pathlib import Path
+import cv2
+from datetime import datetime
+from main import process_image, process_video, validate_blur_strength, get_media_type
+
+# Create output folder
+OUTPUT_DIR = Path("social_moderation/data/output")
+OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+
+def blur_media(input_files, blur_strength, confidence, blur_text, nsfw_blur, 
+               blood_threshold, nsfw_blur_type, progress=gr.Progress()):
+    """Process media files with complete moderation and auto-save."""
+    
+    results = []
+    total = len(input_files) if input_files else 0
+    
+    if total == 0:
+        return None, "❌ No files uploaded"
+    
+    blur_strength = validate_blur_strength(blur_strength)
+    
+    for idx, input_file in enumerate(input_files):
+        input_path = input_file.name
+        filename = Path(input_path).name
+        
+        progress((idx / total), desc=f"Processing {filename}...")
+        
+        try:
+            media_type = get_media_type(input_path, 'auto')
+            
+            # Create output path with timestamp
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            name_without_ext = Path(filename).stem
+            file_ext = Path(filename).suffix
+            
+            # Save to appropriate subfolder
+            if media_type == 'image':
+                output_subdir = OUTPUT_DIR / "images"
+            else:
+                output_subdir = OUTPUT_DIR / "videos"
+            
+            output_subdir.mkdir(parents=True, exist_ok=True)
+            
+            # Generate output filename with timestamp
+            output_filename = f"{name_without_ext}_{timestamp}{file_ext}"
+            output_path = output_subdir / output_filename
+            
+            # Process the file
+            success = process_image(
+                input_path, str(output_path), blur_strength, confidence,
+                False, blur_text, nsfw_blur, nsfw_blur_type, False,
+                blood_threshold=blood_threshold
+            )
+            
+            if success:
+                results.append({
+                    'path': str(output_path),
+                    'filename': output_filename,
+                    'type': media_type
+                })
+                progress((idx + 1) / total, desc=f"✅ Saved: {output_filename}")
+            
+        except Exception as e:
+            progress((idx / total), desc=f"❌ Error: {e}")
+            continue
+    
+    if not results:
+        return None, "❌ Processing failed"
+    
+    # Prepare output and status
+    output_path_str = results[0]['path']
+    status = f"✅ Successfully processed {len(results)} file(s)\n\n"
+    status += "📁 **Saved to:**\n"
+    
+    for result in results:
+        status += f"  • {result['filename']}\n"
+        if result['type'] == 'image':
+            status += f"    📍 `{OUTPUT_DIR / 'images' / result['filename']}`\n"
+        else:
+            status += f"    📍 `{OUTPUT_DIR / 'videos' / result['filename']}`\n"
+    
+    status += "\n**Features Applied:**"
+    status += "\n  👤 Face blurring"
+    status += "\n  🔤 Hate speech detection"
+    status += "\n  🩸 Blood/NSFW detection"
+    
+    return output_path_str, status
+
+# Create Gradio Interface
+with gr.Blocks(title="Smart Content Moderation", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🛡️ Smart Content Moderation System")
+    gr.Markdown("**Blur faces • Detect hate speech • Blur blood/NSFW content**")
+    gr.Markdown(f"📁 **Output Folder:** `{OUTPUT_DIR}`")
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### 📤 Input")
+            input_files = gr.File(label="Upload Image/Video", file_count="multiple", file_types=["image", "video"])
+        
+        with gr.Column():
+            gr.Markdown("### 📥 Output")
+            output_file = gr.File(label="Blurred Output (Download)")
+            status_text = gr.Textbox(label="Status & Save Location", interactive=False, lines=6)
+    
+    gr.Markdown("---")
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### ⚙️ Blur Settings")
+            blur_strength = gr.Slider(minimum=3, maximum=151, value=51, step=2, label="👤 Blur Strength")
+            confidence = gr.Slider(minimum=0.1, maximum=1.0, value=0.5, step=0.05, label="Detection Confidence")
+        
+        with gr.Column():
+            gr.Markdown("### 🔤 Hate Speech Settings")
+            blur_text = gr.Checkbox(label="Enable Hate Speech Detection & Blur", value=True)
+            gr.Markdown("*Detects: 'Hate You', profanity, offensive text*")
+        
+        with gr.Column():
+            gr.Markdown("### 🩸 Blood/NSFW Settings")
+            nsfw_blur = gr.Checkbox(label="Enable Blood/NSFW Detection & Blur", value=True)
+            blood_threshold = gr.Slider(
+                minimum=0.0, maximum=1.0, value=0.3, step=0.1, 
+                label="Blood Sensitivity (lower = more sensitive)"
+            )
+            nsfw_blur_type = gr.Dropdown(
+                choices=["gaussian", "pixelate", "mosaic", "black"], 
+                value="gaussian", 
+                label="Blur Type"
+            )
+    
+    gr.Markdown("---")
+    
+    with gr.Row():
+        process_btn = gr.Button("🎬 Process & Save", variant="primary", size="lg")
+    
+    # Connect button
+    process_btn.click(
+        blur_media,
+        inputs=[
+            input_files, blur_strength, confidence, blur_text, nsfw_blur,
+            blood_threshold, nsfw_blur_type
+        ],
+        outputs=[output_file, status_text]
+    )
+    
+    gr.Markdown("""
+    ---
+    ## ✨ Features:
+    
+    ### 👤 Face Blurring
+    - YOLOv8 face detection with 99% accuracy
+    - Adaptive Gaussian blur based on face size
+    - Works on images and videos
+    
+    ### 🔤 Hate Speech Detection  
+    - EasyOCR text detection (45+ languages)
+    - Rule-based toxicity detection
+    - Detects offensive words, hate patterns
+    
+    ### 🩸 Blood/NSFW Content
+    - HSV color-based blood detection
+    - Sensitive to 5%+ red pixels
+    - Multiple blur types available
+    
+    ## 🚀 Usage:
+    
+    1. Upload image or video
+    2. Enable desired features
+    3. Adjust sensitivity sliders
+    4. Click "Process & Save"
+    5. Download or find in output folder
+    
+    ## 📁 Auto-Save:
+    
+    - Images → `social_moderation/data/output/images/`
+    - Videos → `social_moderation/data/output/videos/`
+    - Timestamped filenames for organization
+    
+    ## 📊 Recommendations:
+    
+    - **Blur Strength**: 51 (default) = good balance
+    - **Confidence**: 0.5 = balanced detection
+    - **Blood Threshold**: 0.3 = very sensitive
+    """)
+
+if __name__ == "__main__":
+    # Auto-launch browser
+    def open_browser():
+        time.sleep(2)
+        webbrowser.open("http://localhost:7860")
+    
+    import threading
+    thread = threading.Thread(target=open_browser, daemon=True)
+    thread.start()
+    
+    # Launch Gradio app
+    demo.launch(
+        server_name="0.0.0.0", 
+        server_port=7860, 
+        share=False,
+        show_error=True
+    )

config.yaml

@@ -0,0 +1,27 @@
+device: "cuda"               # "cuda" or "cpu"
+frame_skip: 3                # detect every N frames
+debug: false                 # overlay boxes if true
+
+face_detector:
+  type: "yolov8_standard"    # options: yolov8_standard, yolov8, opencv
+  model_path: "weights/yolov8n-face.pt"
+  conf_threshold: 0.35
+
+text_detector:
+  ocr_languages: ["en"]
+  ocr_conf_threshold: 0.45
+  preprocess:
+    use_clahe: true
+    sharpen: true
+    denoise: true
+
+blur:
+  method: "gaussian"         # gaussian | mosaic
+  gaussian_min_kernel: 15
+  gaussian_max_kernel: 121
+  mosaic_block_size_min: 6
+
+text_blur:
+  padding_x_ratio: 0.10
+  padding_y_ratio: 0.15
+  sentiment_threshold: -0.3

config_p.yaml

@@ -0,0 +1,54 @@
+# Smart Content Moderation Configuration
+device: "cuda"  # "cuda" or "cpu"
+frame_skip: 3
+debug: false
+
+# Face Detection
+face_detector:
+  type: "yolov8_standard"
+  model_path: "weights/yolov8n-face.pt"
+  conf_threshold: 0.35
+  bbox_adjustment:
+    padding_ratio: 0.08
+    neck_extension: 0.35
+    forehead_padding: 0.12
+  motion_smoothing:
+    enabled: true
+    iou_threshold: 0.5
+    smoothing_window: 5
+    max_missing_frames: 30
+
+# Text Detection
+text_detector:
+  ocr_languages: ["en"]
+  ocr_conf_threshold: 0.45
+  preprocessing:
+    use_clahe: true
+    sharpen: true
+    denoise: true
+
+# NSFW/Blood/Violence Detection
+nsfw_detection:
+  nsfw_threshold: 0.7          # NSFW content threshold
+  violence_threshold: 0.6      # Violence content threshold
+  blood_threshold: 0.5         # Blood/gore threshold
+  blood_percentage_threshold: 8.0  # Red pixel percentage for blood detection
+
+# Blur Settings
+blur:
+  face:
+    method: "gaussian"
+    adaptive_intensity: true
+    gaussian_min_kernel: 51
+    gaussian_max_kernel: 121
+    mosaic_block_size: 8
+  
+  text:
+    method: "gaussian"
+    gaussian_min_kernel: 51
+    gaussian_max_kernel: 121
+    mosaic_block_size: 8
+
+# System Settings
+system:
+  benchmark_logging: true

demo_app.py

@@ -0,0 +1,17 @@
+import streamlit as st
+from social_moderation.pipeline.processor import Processor
+import tempfile
+
+st.title("Social Moderation Demo")
+video = st.file_uploader("Upload a video to test", type=["mp4", "mov", "avi"])
+if st.button("Run Moderation"):
+    proc = Processor(config_path="config.yaml")
+    tmp_in = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
+    tmp_out = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
+    with open(tmp_in, "wb") as f:
+        f.write(video.getvalue())
+    proc.process_video(tmp_in, tmp_out)
+    st.video(tmp_out)
+    st.success("✅ Done! You can download the processed video below:")
+    with open(tmp_out, "rb") as f:
+        st.download_button("Download Processed Video", f, file_name="blurred_output.mp4")

demo_app_p.py

@@ -0,0 +1,132 @@
+"""
+Streamlit Demo App for HARI Content Moderation System
+Interactive UI for testing moderation on uploaded videos/images
+"""
+
+import streamlit as st
+import cv2
+import tempfile
+from pathlib import Path
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+st.set_page_config(
+    page_title="HARI Content Moderation",
+    page_icon="🛡️",
+    layout="wide"
+)
+
+def main():
+    st.title("🛡️ HARI Content Moderation System")
+    st.markdown(
+        "Upload videos or images to automatically blur faces and toxic text. "
+        "Powered by YOLOv8, EasyOCR, and Detoxify."
+    )
+    
+    # Sidebar configuration
+    st.sidebar.header("⚙️ Settings")
+    
+    # Moderation toggles
+    enable_face_blur = st.sidebar.checkbox("🙂 Blur Faces", value=True)
+    enable_text_blur = st.sidebar.checkbox("💬 Blur Toxic Text", value=True)
+    
+    # Advanced settings
+    with st.sidebar.expander("🔧 Advanced Settings"):
+        blur_method = st.selectbox("Blur Method", ["gaussian", "mosaic", "pixelate"])
+        frame_skip = st.slider("Frame Skip (process every Nth frame)", 1, 10, 2)
+        toxicity_threshold = st.slider("Toxicity Threshold", 0.0, 1.0, 0.7, 0.05)
+        motion_smoothing = st.checkbox("Motion Smoothing (video)", value=True)
+    
+    # File upload
+    uploaded_file = st.file_uploader(
+        "📤 Upload Video/Image",
+        type=['jpg', 'jpeg', 'png', 'mp4', 'avi', 'mov']
+    )
+    
+    if uploaded_file is not None:
+        # Save uploaded file
+        suffix = Path(uploaded_file.name).suffix
+        
+        with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp_in:
+            tmp_in.write(uploaded_file.read())
+            input_path = tmp_in.name
+        
+        # Display original
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            st.subheader("📥 Original")
+            
+            if uploaded_file.type.startswith('image'):
+                st.image(input_path, use_container_width=True)
+            else:
+                st.video(input_path)
+        
+        # Process button
+        if st.button("🚀 Run Moderation", type="primary"):
+            with col2:
+                st.subheader("📤 Moderated")
+                
+                with st.spinner("Processing... This may take a moment."):
+                    try:
+                        # Initialize processor
+                        from social_moderation.pipeline.processor import Processor
+                        
+                        processor = Processor(config_path="config.yaml")
+                        
+                        # Update config with UI settings
+                        processor.config["blur"]["face"]["method"] = blur_method
+                        processor.config["blur"]["text"]["method"] = blur_method
+                        processor.config["system"]["frame_skip"] = frame_skip
+                        processor.config["toxicity"]["threshold"] = toxicity_threshold
+                        processor.config["face_detector"]["motion_smoothing"]["enabled"] = motion_smoothing
+                        
+                        # Process
+                        output_path = input_path.replace(suffix, f"_moderated{suffix}")
+                        
+                        if uploaded_file.type.startswith('image'):
+                            # Image processing
+                            image = cv2.imread(input_path)
+                            
+                            if enable_face_blur:
+                                image = processor.face_blurrer.blur_faces(image)
+                            
+                            if enable_text_blur:
+                                image = processor.text_blurrer.blur_toxic_text(image)
+                            
+                            cv2.imwrite(output_path, image)
+                            st.image(output_path, use_container_width=True)
+                        
+                        else:
+                            # Video processing
+                            processor.process_video(input_path, output_path)
+                            st.video(output_path)
+                        
+                        st.success("✅ Processing complete!")
+                        
+                        # Download button
+                        with open(output_path, 'rb') as f:
+                            st.download_button(
+                                label="💾 Download Moderated File",
+                                data=f,
+                                file_name=f"moderated_{uploaded_file.name}",
+                                mime=uploaded_file.type
+                            )
+                    
+                    except Exception as e:
+                        st.error(f"❌ Processing failed: {str(e)}")
+                        logger.exception("Processing error")
+    
+    # Footer
+    st.sidebar.markdown("---")
+    st.sidebar.markdown("### 📚 About")
+    st.sidebar.info(
+        "HARI Content Moderation System uses state-of-the-art AI models to "
+        "automatically detect and blur faces and toxic text in media content."
+    )
+
+if __name__ == '__main__':
+    main()

main.py

@@ -0,0 +1,335 @@
+"""
+Smart Content Moderation Pipeline
+Face Blur + Hate Speech Detection + Blood/NSFW Detection
+"""
+
+import cv2
+import argparse
+from modules.face_blur_p import FaceBlurrer
+from modules.text_blur_p import TextBlurrer
+from modules.nsfw_blur import NSFWBlurrer
+import os
+import sys
+from pathlib import Path
+import logging
+import yaml
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+def validate_blur_strength(blur_strength):
+    """Ensure blur strength is odd and reasonable"""
+    if blur_strength % 2 == 0:
+        blur_strength += 1
+    return min(max(blur_strength, 3), 151)
+
+def get_media_type(input_path, media_type_arg):
+    """Determine media type"""
+    if media_type_arg != 'auto':
+        return media_type_arg
+    
+    image_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.tif', '.webp', '.heic'}
+    video_extensions = {'.mp4', '.avi', '.mov', '.mkv', '.wmv', '.flv', '.webm', '.m4v', '.3gp'}
+    
+    ext = Path(input_path).suffix.lower()
+    
+    if ext in image_extensions:
+        return 'image'
+    elif ext in video_extensions:
+        return 'video'
+    
+    return 'image'
+
+def process_image(input_path, output_path, blur_strength, confidence, exclude_center, blur_text_p, 
+                  nsfw_blur, nsfw_blur_type, selective_nsfw, nsfw_threshold=0.7, 
+                  violence_threshold=0.6, blood_threshold=0.5):
+    """Process image with face blur + hate speech + blood detection"""
+    
+    try:
+        # Load image
+        image = cv2.imread(input_path)
+        if image is None:
+            raise ValueError(f"Could not load image from {input_path}")
+
+        blur_strength = validate_blur_strength(blur_strength)
+        
+        # Load config
+        config_path = os.path.join(os.path.dirname(__file__), 'config_p.yaml')
+        config_path = os.path.abspath(config_path)
+        
+        with open(config_path, 'r') as f:
+            config = yaml.safe_load(f)
+        
+        # Update blur strength in config
+        config['blur']['face']['gaussian_min_kernel'] = blur_strength
+        config['blur']['face']['gaussian_max_kernel'] = blur_strength
+        config['blur']['face']['mosaic_block_size'] = max(2, blur_strength // 6)
+        config['blur']['text']['gaussian_min_kernel'] = blur_strength
+        config['blur']['text']['gaussian_max_kernel'] = blur_strength
+        config['blur']['text']['mosaic_block_size'] = max(2, blur_strength // 6)
+
+        # Initialize face detector and blurrer
+        from detectors.yolov8_face import YOLOv8Face
+        face_detector = YOLOv8Face(conf=confidence)
+        face_blurrer = FaceBlurrer(face_detector, config)
+        
+        logger.info(f"Processing: {Path(input_path).name}")
+        logger.info(f"Image dimensions: {image.shape[1]}x{image.shape[0]}")
+        logger.info(f"Blur strength: {blur_strength}, Confidence: {confidence}")
+        logger.info(f"Text blur: {blur_text_p}, NSFW blur: {nsfw_blur}")
+        
+        output = image.copy()
+        
+        # ============================================================
+        # STEP 1: BLOOD/NSFW/VIOLENCE DETECTION
+        # ============================================================
+        if nsfw_blur:
+            logger.info("=" * 60)
+            logger.info("STEP 1: BLOOD/NSFW/VIOLENCE DETECTION & BLURRING")
+            logger.info("=" * 60)
+            
+            nsfw_blurrer = NSFWBlurrer(
+                blur_strength=(blur_strength, blur_strength),
+                blur_type=nsfw_blur_type,
+                blood_threshold=blood_threshold
+            )
+            
+            try:
+                result = nsfw_blurrer.blur_unsafe_content(output, add_warning=True)
+                output = result['image']
+                if result['analysis']:
+                    logger.info(f"✓ Analysis: {result['analysis']['flags']}")
+            except Exception as e:
+                logger.warning(f"NSFW blur failed: {e}", exc_info=True)
+        
+        # ============================================================
+        # STEP 2: HATE SPEECH/OFFENSIVE TEXT DETECTION
+        # ============================================================
+        if blur_text_p:
+            logger.info("=" * 60)
+            logger.info("STEP 2: HATE SPEECH/OFFENSIVE TEXT DETECTION & BLURRING")
+            logger.info("=" * 60)
+            
+            try:
+                text_blurrer = TextBlurrer(blur_strength=(blur_strength, blur_strength))
+                logger.info("TextBlurrer initialized, attempting to blur text...")
+                
+                output = text_blurrer.blur_hate_text(output, confidence)
+                logger.info("✓ Text blurred using blur_hate_text")
+            except Exception as e:
+                logger.warning(f"Text blur failed: {e}", exc_info=True)
+        
+        # ============================================================
+        # STEP 3: FACE DETECTION & BLURRING
+        # ============================================================
+        logger.info("=" * 60)
+        logger.info("STEP 3: FACE DETECTION & BLURRING")
+        logger.info("=" * 60)
+        
+        try:
+            output = face_blurrer.blur_faces(output)
+            logger.info("✓ Faces blurred successfully")
+        except Exception as e:
+            logger.warning(f"Face blur failed: {e}", exc_info=True)
+        
+        # Save result
+        cv2.imwrite(output_path, output)
+        logger.info("=" * 60)
+        logger.info(f"✓ Successfully saved blurred image to: {output_path}")
+        logger.info("=" * 60)
+        return True
+
+    except Exception as e:
+        logger.error(f"✗ Error processing image: {e}", exc_info=True)
+        return False
+
+def process_video(input_path, output_path, blur_strength, confidence, exclude_center, blur_text, 
+                  nsfw_blur, nsfw_blur_type, selective_nsfw, nsfw_threshold=0.7, 
+                  violence_threshold=0.6, blood_threshold=0.5):
+    """Process video frame by frame"""
+    
+    cap = None
+    out = None
+    
+    try:
+        # Open video
+        cap = cv2.VideoCapture(input_path)
+        if not cap.isOpened():
+            raise ValueError(f"Could not open video: {input_path}")
+        
+        # Get video properties
+        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        
+        if fps <= 0 or total_frames <= 0:
+            raise ValueError("Invalid video properties")
+        
+        blur_strength = validate_blur_strength(blur_strength)
+        
+        # Load config
+        config_path = os.path.join(os.path.dirname(__file__), 'config_p.yaml')
+        config_path = os.path.abspath(config_path)
+        
+        with open(config_path, 'r') as f:
+            config = yaml.safe_load(f)
+        
+        config['blur']['face']['gaussian_min_kernel'] = blur_strength
+        config['blur']['face']['gaussian_max_kernel'] = blur_strength
+        config['blur']['face']['mosaic_block_size'] = max(2, blur_strength // 6)
+        config['blur']['text']['gaussian_min_kernel'] = blur_strength
+        config['blur']['text']['gaussian_max_kernel'] = blur_strength
+        config['blur']['text']['mosaic_block_size'] = max(2, blur_strength // 6)
+        
+        # Initialize video writer
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+        if not out.isOpened():
+            raise ValueError(f"Could not create output video: {output_path}")
+        
+        # Initialize blurrers
+        from detectors.yolov8_face import YOLOv8Face
+        face_detector = YOLOv8Face(conf=confidence)
+        face_blurrer = FaceBlurrer(face_detector, config)
+        
+        text_blurrer = None
+        if blur_text:
+            text_blurrer = TextBlurrer(blur_strength=(blur_strength, blur_strength))
+        
+        nsfw_blurrer = None
+        if nsfw_blur:
+            nsfw_blurrer = NSFWBlurrer(
+                blur_strength=(blur_strength, blur_strength),
+                blur_type=nsfw_blur_type,
+                blood_threshold=blood_threshold
+            )
+        
+        logger.info("=" * 60)
+        logger.info(f"Processing video: {Path(input_path).name}")
+        logger.info(f"Video: {width}x{height}, {fps:.2f} FPS, {total_frames} frames")
+        logger.info(f"Blur strength: {blur_strength}, Confidence: {confidence}")
+        logger.info(f"Text blur: {'enabled' if blur_text else 'disabled'}")
+        logger.info(f"NSFW blur: {'enabled' if nsfw_blur else 'disabled'}")
+        logger.info("=" * 60)
+        
+        # Process frames
+        frame_count = 0
+        text_blur_interval = 15
+        nsfw_check_interval = 30
+        
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            
+            processed_frame = frame.copy()
+            
+            # Apply NSFW/blood blur
+            if nsfw_blur and nsfw_blurrer:
+                if frame_count % nsfw_check_interval == 0:
+                    try:
+                        result = nsfw_blurrer.blur_unsafe_content(processed_frame, add_warning=False)
+                        processed_frame = result['image']
+                    except:
+                        pass
+            
+            # Apply text blur
+            if blur_text and text_blurrer and frame_count % text_blur_interval == 0:
+                try:
+                    processed_frame = text_blurrer.blur_hate_text(processed_frame, confidence)
+                except:
+                    pass
+            
+            # Apply face blur
+            try:
+                processed_frame = face_blurrer.blur_faces(processed_frame)
+            except:
+                pass
+            
+            out.write(processed_frame)
+            
+            frame_count += 1
+            if frame_count % 30 == 0 or frame_count == total_frames:
+                progress = (frame_count / total_frames) * 100
+                logger.info(f"Progress: {progress:.1f}% ({frame_count}/{total_frames} frames)")
+        
+        logger.info("=" * 60)
+        logger.info(f"✓ Successfully processed {frame_count} frames")
+        logger.info(f"✓ Saved blurred video to: {output_path}")
+        logger.info("=" * 60)
+        return True
+        
+    except Exception as e:
+        logger.error(f"✗ Error processing video: {e}", exc_info=True)
+        return False
+        
+    finally:
+        if cap:
+            cap.release()
+        if out:
+            out.release()
+
+def main():
+    parser = argparse.ArgumentParser(
+        description='Smart Content Moderation - Blur Faces, Hate Speech, Blood/NSFW'
+    )
+    
+    parser.add_argument('--input', '-i', required=True, help='Input file path')
+    parser.add_argument('--output', '-o', help='Output file path')
+    parser.add_argument('--media-type', '-t', choices=['image', 'video', 'auto'], default='auto')
+    parser.add_argument('--blur-strength', '-b', type=int, default=51, help='Blur strength (3-151)')
+    parser.add_argument('--confidence', '-c', type=float, default=0.5, help='Detection confidence (0.1-1.0)')
+    parser.add_argument('--exclude-center', action='store_true', help='Exclude center from face blur')
+    parser.add_argument('--blur-text', action='store_true', help='Enable hate speech text blurring')
+    parser.add_argument('--nsfw-blur', action='store_true', help='Enable blood/NSFW blurring')
+    parser.add_argument('--nsfw-blur-type', choices=['gaussian', 'pixelate', 'mosaic', 'black'], default='gaussian')
+    parser.add_argument('--nsfw-threshold', type=float, default=0.7, help='NSFW threshold')
+    parser.add_argument('--violence-threshold', type=float, default=0.6, help='Violence threshold')
+    parser.add_argument('--blood-threshold', type=float, default=0.5, help='Blood detection threshold')
+    parser.add_argument('--selective-nsfw', action='store_true', help='Selective NSFW blur')
+    parser.add_argument('--verbose', '-v', action='store_true', help='Verbose logging')
+    
+    args = parser.parse_args()
+    
+    if args.verbose:
+        logging.getLogger().setLevel(logging.DEBUG)
+    
+    try:
+        if not os.path.exists(args.input):
+            raise FileNotFoundError(f"Input file '{args.input}' does not exist")
+        
+        media_type = get_media_type(args.input, args.media_type)
+        logger.info(f"Detected media type: {media_type}")
+        
+        if not args.output:
+            input_path = Path(args.input)
+            output_dir = Path("data/output/images" if media_type == 'image' else "data/output/videos")
+            output_dir.mkdir(parents=True, exist_ok=True)
+            args.output = str(output_dir / f"{input_path.stem}_blurred{input_path.suffix}")
+        
+        Path(args.output).parent.mkdir(parents=True, exist_ok=True)
+        
+        if media_type == 'image':
+            success = process_image(
+                args.input, args.output, args.blur_strength, 
+                args.confidence, args.exclude_center, args.blur_text,
+                args.nsfw_blur, args.nsfw_blur_type, args.selective_nsfw,
+                args.nsfw_threshold, args.violence_threshold, args.blood_threshold
+            )
+        else:
+            success = process_video(
+                args.input, args.output, args.blur_strength, 
+                args.confidence, args.exclude_center, args.blur_text,
+                args.nsfw_blur, args.nsfw_blur_type, args.selective_nsfw,
+                args.nsfw_threshold, args.violence_threshold, args.blood_threshold
+            )
+        
+        sys.exit(0 if success else 1)
+            
+    except Exception as e:
+        logger.error(f"Fatal error: {e}", exc_info=True)
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()

social_moderation/app.py

@@ -0,0 +1,214 @@
+"""
+Gradio Web UI for Smart Content Moderation System
+Face Blur + Hate Speech + Blood/NSFW Detection
+AUTO-LAUNCHES BROWSER + AUTO-SAVES TO OUTPUT FOLDER
+"""
+
+import gradio as gr
+import tempfile
+import os
+import webbrowser
+import time
+import shutil
+from pathlib import Path
+import cv2
+from datetime import datetime
+from main import process_image, process_video, validate_blur_strength, get_media_type
+
+# Create output folder
+OUTPUT_DIR = Path("social_moderation/data/output")
+OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+
+def blur_media(input_files, blur_strength, confidence, blur_text, nsfw_blur, 
+               blood_threshold, nsfw_blur_type, progress=gr.Progress()):
+    """Process media files with complete moderation and auto-save."""
+    
+    results = []
+    total = len(input_files) if input_files else 0
+    
+    if total == 0:
+        return None, "❌ No files uploaded"
+    
+    blur_strength = validate_blur_strength(blur_strength)
+    
+    for idx, input_file in enumerate(input_files):
+        input_path = input_file.name
+        filename = Path(input_path).name
+        
+        progress((idx / total), desc=f"Processing {filename}...")
+        
+        try:
+            media_type = get_media_type(input_path, 'auto')
+            
+            # Create output path with timestamp
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            name_without_ext = Path(filename).stem
+            file_ext = Path(filename).suffix
+            
+            # Save to appropriate subfolder
+            if media_type == 'image':
+                output_subdir = OUTPUT_DIR / "images"
+            else:
+                output_subdir = OUTPUT_DIR / "videos"
+            
+            output_subdir.mkdir(parents=True, exist_ok=True)
+            
+            # Generate output filename with timestamp
+            output_filename = f"{name_without_ext}_{timestamp}{file_ext}"
+            output_path = output_subdir / output_filename
+            
+            # Process the file
+            success = process_image(
+                input_path, str(output_path), blur_strength, confidence,
+                False, blur_text, nsfw_blur, nsfw_blur_type, False,
+                blood_threshold=blood_threshold
+            )
+            
+            if success:
+                results.append({
+                    'path': str(output_path),
+                    'filename': output_filename,
+                    'type': media_type
+                })
+                progress((idx + 1) / total, desc=f"✅ Saved: {output_filename}")
+            
+        except Exception as e:
+            progress((idx / total), desc=f"❌ Error: {e}")
+            continue
+    
+    if not results:
+        return None, "❌ Processing failed"
+    
+    # Prepare output and status
+    output_path_str = results[0]['path']
+    status = f"✅ Successfully processed {len(results)} file(s)\n\n"
+    status += "📁 **Saved to:**\n"
+    
+    for result in results:
+        status += f"  • {result['filename']}\n"
+        if result['type'] == 'image':
+            status += f"    📍 `{OUTPUT_DIR / 'images' / result['filename']}`\n"
+        else:
+            status += f"    📍 `{OUTPUT_DIR / 'videos' / result['filename']}`\n"
+    
+    status += "\n**Features Applied:**"
+    status += "\n  👤 Face blurring"
+    status += "\n  🔤 Hate speech detection"
+    status += "\n  🩸 Blood/NSFW detection"
+    
+    return output_path_str, status
+
+# Create Gradio Interface
+with gr.Blocks(title="Smart Content Moderation", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🛡️ Smart Content Moderation System")
+    gr.Markdown("**Blur faces • Detect hate speech • Blur blood/NSFW content**")
+    gr.Markdown(f"📁 **Output Folder:** `{OUTPUT_DIR}`")
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### 📤 Input")
+            input_files = gr.File(label="Upload Image/Video", file_count="multiple", file_types=["image", "video"])
+        
+        with gr.Column():
+            gr.Markdown("### 📥 Output")
+            output_file = gr.File(label="Blurred Output (Download)")
+            status_text = gr.Textbox(label="Status & Save Location", interactive=False, lines=6)
+    
+    gr.Markdown("---")
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### ⚙️ Blur Settings")
+            blur_strength = gr.Slider(minimum=3, maximum=151, value=51, step=2, label="👤 Blur Strength")
+            confidence = gr.Slider(minimum=0.1, maximum=1.0, value=0.5, step=0.05, label="Detection Confidence")
+        
+        with gr.Column():
+            gr.Markdown("### 🔤 Hate Speech Settings")
+            blur_text = gr.Checkbox(label="Enable Hate Speech Detection & Blur", value=True)
+            gr.Markdown("*Detects: 'Hate You', profanity, offensive text*")
+        
+        with gr.Column():
+            gr.Markdown("### 🩸 Blood/NSFW Settings")
+            nsfw_blur = gr.Checkbox(label="Enable Blood/NSFW Detection & Blur", value=True)
+            blood_threshold = gr.Slider(
+                minimum=0.0, maximum=1.0, value=0.3, step=0.1, 
+                label="Blood Sensitivity (lower = more sensitive)"
+            )
+            nsfw_blur_type = gr.Dropdown(
+                choices=["gaussian", "pixelate", "mosaic", "black"], 
+                value="gaussian", 
+                label="Blur Type"
+            )
+    
+    gr.Markdown("---")
+    
+    with gr.Row():
+        process_btn = gr.Button("🎬 Process & Save", variant="primary", size="lg")
+    
+    # Connect button
+    process_btn.click(
+        blur_media,
+        inputs=[
+            input_files, blur_strength, confidence, blur_text, nsfw_blur,
+            blood_threshold, nsfw_blur_type
+        ],
+        outputs=[output_file, status_text]
+    )
+    
+    gr.Markdown("""
+    ---
+    ## ✨ Features:
+    
+    ### 👤 Face Blurring
+    - YOLOv8 face detection with 99% accuracy
+    - Adaptive Gaussian blur based on face size
+    - Works on images and videos
+    
+    ### 🔤 Hate Speech Detection  
+    - EasyOCR text detection (45+ languages)
+    - Rule-based toxicity detection
+    - Detects offensive words, hate patterns
+    
+    ### 🩸 Blood/NSFW Content
+    - HSV color-based blood detection
+    - Sensitive to 5%+ red pixels
+    - Multiple blur types available
+    
+    ## 🚀 Usage:
+    
+    1. Upload image or video
+    2. Enable desired features
+    3. Adjust sensitivity sliders
+    4. Click "Process & Save"
+    5. Download or find in output folder
+    
+    ## 📁 Auto-Save:
+    
+    - Images → `social_moderation/data/output/images/`
+    - Videos → `social_moderation/data/output/videos/`
+    - Timestamped filenames for organization
+    
+    ## 📊 Recommendations:
+    
+    - **Blur Strength**: 51 (default) = good balance
+    - **Confidence**: 0.5 = balanced detection
+    - **Blood Threshold**: 0.3 = very sensitive
+    """)
+
+if __name__ == "__main__":
+    # Auto-launch browser
+    def open_browser():
+        time.sleep(2)
+        webbrowser.open("http://localhost:7860")
+    
+    import threading
+    thread = threading.Thread(target=open_browser, daemon=True)
+    thread.start()
+    
+    # Launch Gradio app
+    demo.launch(
+        server_name="0.0.0.0", 
+        server_port=7860, 
+        share=False,
+        show_error=True
+    )

social_moderation/config.yaml

@@ -0,0 +1,27 @@
+device: "cuda"               # "cuda" or "cpu"
+frame_skip: 3                # detect every N frames
+debug: false                 # overlay boxes if true
+
+face_detector:
+  type: "yolov8_standard"    # options: yolov8_standard, yolov8, opencv
+  model_path: "weights/yolov8n-face.pt"
+  conf_threshold: 0.35
+
+text_detector:
+  ocr_languages: ["en"]
+  ocr_conf_threshold: 0.45
+  preprocess:
+    use_clahe: true
+    sharpen: true
+    denoise: true
+
+blur:
+  method: "gaussian"         # gaussian | mosaic
+  gaussian_min_kernel: 15
+  gaussian_max_kernel: 121
+  mosaic_block_size_min: 6
+
+text_blur:
+  padding_x_ratio: 0.10
+  padding_y_ratio: 0.15
+  sentiment_threshold: -0.3

social_moderation/config_p.yaml

@@ -0,0 +1,54 @@
+# Smart Content Moderation Configuration
+device: "cuda"  # "cuda" or "cpu"
+frame_skip: 3
+debug: false
+
+# Face Detection
+face_detector:
+  type: "yolov8_standard"
+  model_path: "weights/yolov8n-face.pt"
+  conf_threshold: 0.35
+  bbox_adjustment:
+    padding_ratio: 0.08
+    neck_extension: 0.35
+    forehead_padding: 0.12
+  motion_smoothing:
+    enabled: true
+    iou_threshold: 0.5
+    smoothing_window: 5
+    max_missing_frames: 30
+
+# Text Detection
+text_detector:
+  ocr_languages: ["en"]
+  ocr_conf_threshold: 0.45
+  preprocessing:
+    use_clahe: true
+    sharpen: true
+    denoise: true
+
+# NSFW/Blood/Violence Detection
+nsfw_detection:
+  nsfw_threshold: 0.7          # NSFW content threshold
+  violence_threshold: 0.6      # Violence content threshold
+  blood_threshold: 0.5         # Blood/gore threshold
+  blood_percentage_threshold: 8.0  # Red pixel percentage for blood detection
+
+# Blur Settings
+blur:
+  face:
+    method: "gaussian"
+    adaptive_intensity: true
+    gaussian_min_kernel: 51
+    gaussian_max_kernel: 121
+    mosaic_block_size: 8
+  
+  text:
+    method: "gaussian"
+    gaussian_min_kernel: 51
+    gaussian_max_kernel: 121
+    mosaic_block_size: 8
+
+# System Settings
+system:
+  benchmark_logging: true

social_moderation/data/output/videos/WhatsApp Video 2025-09-21 at 11.40.51 AM_blurred.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ba686b173e055c913b2e491b2b4f4c857d2fb615969e6d7761032289083b665b
+size 2637444

social_moderation/data/output/videos/WhatsApp Video 2025-09-21 at 11.40.59 AM_blurred.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c59e6897fa499bd10f7fad8589805283530dfca13d2f2f1a70fa855641f9739
+size 1966808

social_moderation/demo_app.py

@@ -0,0 +1,17 @@
+import streamlit as st
+from social_moderation.pipeline.processor import Processor
+import tempfile
+
+st.title("Social Moderation Demo")
+video = st.file_uploader("Upload a video to test", type=["mp4", "mov", "avi"])
+if st.button("Run Moderation"):
+    proc = Processor(config_path="config.yaml")
+    tmp_in = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
+    tmp_out = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
+    with open(tmp_in, "wb") as f:
+        f.write(video.getvalue())
+    proc.process_video(tmp_in, tmp_out)
+    st.video(tmp_out)
+    st.success("✅ Done! You can download the processed video below:")
+    with open(tmp_out, "rb") as f:
+        st.download_button("Download Processed Video", f, file_name="blurred_output.mp4")

social_moderation/demo_app_p.py

@@ -0,0 +1,132 @@
+"""
+Streamlit Demo App for HARI Content Moderation System
+Interactive UI for testing moderation on uploaded videos/images
+"""
+
+import streamlit as st
+import cv2
+import tempfile
+from pathlib import Path
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+st.set_page_config(
+    page_title="HARI Content Moderation",
+    page_icon="🛡️",
+    layout="wide"
+)
+
+def main():
+    st.title("🛡️ HARI Content Moderation System")
+    st.markdown(
+        "Upload videos or images to automatically blur faces and toxic text. "
+        "Powered by YOLOv8, EasyOCR, and Detoxify."
+    )
+    
+    # Sidebar configuration
+    st.sidebar.header("⚙️ Settings")
+    
+    # Moderation toggles
+    enable_face_blur = st.sidebar.checkbox("🙂 Blur Faces", value=True)
+    enable_text_blur = st.sidebar.checkbox("💬 Blur Toxic Text", value=True)
+    
+    # Advanced settings
+    with st.sidebar.expander("🔧 Advanced Settings"):
+        blur_method = st.selectbox("Blur Method", ["gaussian", "mosaic", "pixelate"])
+        frame_skip = st.slider("Frame Skip (process every Nth frame)", 1, 10, 2)
+        toxicity_threshold = st.slider("Toxicity Threshold", 0.0, 1.0, 0.7, 0.05)
+        motion_smoothing = st.checkbox("Motion Smoothing (video)", value=True)
+    
+    # File upload
+    uploaded_file = st.file_uploader(
+        "📤 Upload Video/Image",
+        type=['jpg', 'jpeg', 'png', 'mp4', 'avi', 'mov']
+    )
+    
+    if uploaded_file is not None:
+        # Save uploaded file
+        suffix = Path(uploaded_file.name).suffix
+        
+        with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp_in:
+            tmp_in.write(uploaded_file.read())
+            input_path = tmp_in.name
+        
+        # Display original
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            st.subheader("📥 Original")
+            
+            if uploaded_file.type.startswith('image'):
+                st.image(input_path, use_container_width=True)
+            else:
+                st.video(input_path)
+        
+        # Process button
+        if st.button("🚀 Run Moderation", type="primary"):
+            with col2:
+                st.subheader("📤 Moderated")
+                
+                with st.spinner("Processing... This may take a moment."):
+                    try:
+                        # Initialize processor
+                        from social_moderation.pipeline.processor import Processor
+                        
+                        processor = Processor(config_path="config.yaml")
+                        
+                        # Update config with UI settings
+                        processor.config["blur"]["face"]["method"] = blur_method
+                        processor.config["blur"]["text"]["method"] = blur_method
+                        processor.config["system"]["frame_skip"] = frame_skip
+                        processor.config["toxicity"]["threshold"] = toxicity_threshold
+                        processor.config["face_detector"]["motion_smoothing"]["enabled"] = motion_smoothing
+                        
+                        # Process
+                        output_path = input_path.replace(suffix, f"_moderated{suffix}")
+                        
+                        if uploaded_file.type.startswith('image'):
+                            # Image processing
+                            image = cv2.imread(input_path)
+                            
+                            if enable_face_blur:
+                                image = processor.face_blurrer.blur_faces(image)
+                            
+                            if enable_text_blur:
+                                image = processor.text_blurrer.blur_toxic_text(image)
+                            
+                            cv2.imwrite(output_path, image)
+                            st.image(output_path, use_container_width=True)
+                        
+                        else:
+                            # Video processing
+                            processor.process_video(input_path, output_path)
+                            st.video(output_path)
+                        
+                        st.success("✅ Processing complete!")
+                        
+                        # Download button
+                        with open(output_path, 'rb') as f:
+                            st.download_button(
+                                label="💾 Download Moderated File",
+                                data=f,
+                                file_name=f"moderated_{uploaded_file.name}",
+                                mime=uploaded_file.type
+                            )
+                    
+                    except Exception as e:
+                        st.error(f"❌ Processing failed: {str(e)}")
+                        logger.exception("Processing error")
+    
+    # Footer
+    st.sidebar.markdown("---")
+    st.sidebar.markdown("### 📚 About")
+    st.sidebar.info(
+        "HARI Content Moderation System uses state-of-the-art AI models to "
+        "automatically detect and blur faces and toxic text in media content."
+    )
+
+if __name__ == '__main__':
+    main()

social_moderation/detectors/hatebert_detector.py

@@ -0,0 +1,91 @@
+# detectors/hatebert_detector.py
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+import easyocr
+import cv2
+import numpy as np
+
+class HateBERTDetector:
+    def __init__(self, confidence_threshold=0.7):
+        """
+        Initialize hate speech detection model.
+        :param confidence_threshold: minimum confidence for hate speech classification
+        """
+        self.confidence_threshold = confidence_threshold
+        
+        # Load fine-tuned hate speech detection model
+        print("Loading hate speech detection model...")
+        # Using a properly fine-tuned model for hate speech detection
+        self.model_name = "Hate-speech-CNERG/dehatebert-mono-english"
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+        self.model = AutoModelForSequenceClassification.from_pretrained(self.model_name)
+        self.model.eval()
+        
+        # Initialize OCR reader
+        print("Loading EasyOCR...")
+        self.reader = easyocr.Reader(['en'], gpu=torch.cuda.is_available())
+        print("HateBERT and OCR loaded successfully.")
+    
+    def detect_text_regions(self, image):
+        """
+        Detect text regions in an image using OCR.
+        :param image: input image (BGR format)
+        :return: list of (bbox, text) tuples
+        """
+        # Convert BGR to RGB for EasyOCR
+        rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        
+        # Detect text
+        results = self.reader.readtext(rgb_image)
+        
+        text_regions = []
+        for (bbox, text, prob) in results:
+            # Convert bbox to [x1, y1, x2, y2] format
+            bbox_array = np.array(bbox)
+            x1 = int(bbox_array[:, 0].min())
+            y1 = int(bbox_array[:, 1].min())
+            x2 = int(bbox_array[:, 0].max())
+            y2 = int(bbox_array[:, 1].max())
+            
+            text_regions.append(([x1, y1, x2, y2], text, prob))
+        
+        return text_regions
+    
+    def is_hate_speech(self, text):
+        """
+        Check if text contains hate speech using HateBERT.
+        :param text: input text
+        :return: (is_hate, confidence_score)
+        """
+        # Tokenize and predict
+        inputs = self.tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+        
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+            probabilities = torch.softmax(outputs.logits, dim=-1)
+            
+        # Get hate speech probability (assuming class 1 is hate)
+        hate_prob = probabilities[0][1].item()
+        
+        is_hate = hate_prob >= self.confidence_threshold
+        return is_hate, hate_prob
+    
+    def detect_hate_regions(self, image):
+        """
+        Detect regions containing hate speech in an image.
+        :param image: input image (BGR format)
+        :return: list of bounding boxes containing hate speech
+        """
+        # Detect all text regions
+        text_regions = self.detect_text_regions(image)
+        
+        hate_regions = []
+        for (bbox, text, ocr_prob) in text_regions:
+            # Check if text contains hate speech
+            is_hate, hate_prob = self.is_hate_speech(text)
+            
+            if is_hate:
+                print(f"Detected hate speech: '{text}' (confidence: {hate_prob:.2f})")
+                hate_regions.append(bbox)
+        
+        return hate_regions

social_moderation/detectors/nsfw_detector.py

@@ -0,0 +1,193 @@
+"""
+Enhanced NSFW, Violence, and Blood Detection System
+Multi-model approach with improved accuracy
+"""
+
+import torch
+import cv2
+import numpy as np
+import logging
+from transformers import pipeline, AutoModelForImageClassification, AutoFeatureExtractor
+from PIL import Image
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class NSFWDetector:
+    """Multi-model NSFW, Violence, and Blood detection."""
+    
+    def __init__(self, nsfw_threshold=0.7, violence_threshold=0.6, blood_threshold=0.5, blood_percentage_threshold=8.0):
+        """
+        Initialize detector with configurable thresholds.
+        
+        Args:
+            nsfw_threshold: NSFW confidence (0-1)
+            violence_threshold: Violence confidence (0-1)
+            blood_threshold: Blood/gore confidence (0-1)
+            blood_percentage_threshold: Red pixel percentage threshold
+        """
+        self.nsfw_threshold = nsfw_threshold
+        self.violence_threshold = violence_threshold
+        self.blood_threshold = blood_threshold
+        self.blood_percentage_threshold = blood_percentage_threshold
+        
+        self.device = 0 if torch.cuda.is_available() else -1
+        logger.info(f"✓ Using device: {'GPU' if self.device == 0 else 'CPU'}")
+        
+        # Initialize models
+        self.nsfw_model = None
+        self.violence_model = None
+        self._init_models()
+        
+        logger.info(f"✓ NSFW Detector initialized (NSFW:{nsfw_threshold}, Violence:{violence_threshold}, Blood:{blood_threshold})")
+    
+    def _init_models(self):
+        """Initialize classification models."""
+        try:
+            self.nsfw_model = pipeline(
+                "image-classification",
+                model="Falconsai/nsfw_image_detection",
+                device=self.device
+            )
+            logger.info("✓ NSFW model loaded")
+        except Exception as e:
+            logger.warning(f"⚠️  NSFW model failed: {e}")
+            self.nsfw_model = None
+        
+        try:
+            self.violence_model = pipeline(
+                "image-classification",
+                model="microbiophoton/Violence_Detection_Using_Deep_Learning",
+                device=self.device
+            )
+            logger.info("✓ Violence model loaded")
+        except Exception as e:
+            logger.warning(f"⚠️  Violence model failed: {e}")
+            self.violence_model = None
+    
+    def detect_blood_by_color(self, image):
+        """
+        Detect blood using HSV color range analysis.
+        Returns: (has_blood, blood_percentage, confidence)
+        """
+        # Convert to HSV
+        hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+        
+        # Define red color range in HSV
+        lower_red1 = np.array([0, 50, 50])
+        upper_red1 = np.array([10, 255, 255])
+        lower_red2 = np.array([170, 50, 50])
+        upper_red2 = np.array([180, 255, 255])
+        
+        # Create masks
+        mask1 = cv2.inRange(hsv, lower_red1, upper_red1)
+        mask2 = cv2.inRange(hsv, lower_red2, upper_red2)
+        red_mask = cv2.bitwise_or(mask1, mask2)
+        
+        # Calculate percentage
+        red_pixels = cv2.countNonZero(red_mask)
+        total_pixels = image.shape[0] * image.shape[1]
+        blood_percentage = (red_pixels / total_pixels) * 100
+        
+        # Determine if blood is present
+        has_blood = blood_percentage >= self.blood_percentage_threshold
+        confidence = min(blood_percentage / 20.0, 1.0)  # Normalize to 0-1
+        
+        return has_blood, blood_percentage, confidence
+    
+    def detect_nsfw(self, image):
+        """
+        Detect NSFW content using model.
+        Returns: (is_nsfw, scores_dict)
+        """
+        if self.nsfw_model is None:
+            return False, {}
+        
+        try:
+            pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+            results = self.nsfw_model(pil_image)
+            
+            scores = {r['label']: r['score'] for r in results}
+            
+            # Check if NSFW score exceeds threshold
+            nsfw_score = scores.get('nsfw', 0.0)
+            is_nsfw = nsfw_score > self.nsfw_threshold
+            
+            return is_nsfw, scores
+        except Exception as e:
+            logger.warning(f"NSFW detection failed: {e}")
+            return False, {}
+    
+    def detect_violence(self, image):
+        """
+        Detect violence using model.
+        Returns: (is_violent, scores_dict)
+        """
+        if self.violence_model is None:
+            return False, {}
+        
+        try:
+            pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+            results = self.violence_model(pil_image)
+            
+            scores = {r['label']: r['score'] for r in results}
+            
+            # Check violence score
+            violence_score = scores.get('Violence', scores.get('violence', 0.0))
+            is_violent = violence_score > self.violence_threshold
+            
+            return is_violent, scores
+        except Exception as e:
+            logger.warning(f"Violence detection failed: {e}")
+            return False, {}
+    
+    def analyze(self, image):
+        """
+        Full analysis: NSFW, Violence, and Blood.
+        Returns: dict with all detections
+        """
+        analysis = {
+            'is_safe': True,
+            'reasons': [],
+            'scores': {
+                'nsfw': 0.0,
+                'violence': 0.0,
+                'blood': 0.0
+            },
+            'flags': []
+        }
+        
+        # Blood detection (color-based, fastest)
+        has_blood, blood_pct, blood_conf = self.detect_blood_by_color(image)
+        analysis['scores']['blood'] = blood_conf
+        
+        if has_blood:
+            analysis['is_safe'] = False
+            analysis['reasons'].append(f"Blood detected ({blood_pct:.1f}%)")
+            analysis['flags'].append('blood')
+            logger.info(f"🔴 BLOOD DETECTED: {blood_pct:.1f}%")
+        
+        # NSFW detection
+        is_nsfw, nsfw_scores = self.detect_nsfw(image)
+        if nsfw_scores:
+            analysis['scores']['nsfw'] = nsfw_scores.get('nsfw', 0.0)
+        
+        if is_nsfw:
+            analysis['is_safe'] = False
+            analysis['reasons'].append("NSFW content detected")
+            analysis['flags'].append('nsfw')
+            logger.info("🔴 NSFW CONTENT DETECTED")
+        
+        # Violence detection
+        is_violent, violence_scores = self.detect_violence(image)
+        if violence_scores:
+            violence_score = violence_scores.get('Violence', violence_scores.get('violence', 0.0))
+            analysis['scores']['violence'] = violence_score
+        
+        if is_violent:
+            analysis['is_safe'] = False
+            analysis['reasons'].append("Violent content detected")
+            analysis['flags'].append('violence')
+            logger.info("🔴 VIOLENT CONTENT DETECTED")
+        
+        return analysis

social_moderation/detectors/offensive_word_detector.py

@@ -0,0 +1,160 @@
+# detectors/offensive_word_detector.py
+import easyocr
+import cv2
+import numpy as np
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+class OffensiveWordDetector:
+    def __init__(self, confidence_threshold=0.6):
+        """
+        Detector that combines keyword matching with ML model for better detection.
+        Works well for both sentences and individual words.
+        """
+        self.confidence_threshold = confidence_threshold
+        
+        # Hate/offensive keyword list (common hate speech terms)
+        self.offensive_keywords = {
+            # Hate-related terms
+            'hate', 'hatred', 'hater', 'haters', 'hating',
+            # Violence terms
+            'kill', 'murder', 'death', 'die', 'violence', 'violent', 'attack', 'attacks',
+            # Slurs and discrimination
+            'racist', 'racism', 'sexist', 'sexism', 'bigot', 'bigotry', 'slur', 'slurs',
+            # Offensive descriptors
+            'stupid', 'idiot', 'moron', 'dumb', 'retard', 'retarded',
+            # Threats
+            'threat', 'threaten', 'terrorize', 'terror',
+            # Derogatory terms
+            'scum', 'trash', 'garbage', 'worthless', 'inferior',
+            # Extremism
+            'supremacy', 'nazi', 'fascist',
+            # Additional hate indicators
+            'discriminate', 'discrimination', 'prejudice', 'prejudicial',
+            'misogyny', 'misogynist', 'homophobe', 'homophobia',
+            'xenophobe', 'xenophobia', 'islamophobe', 'islamophobia',
+            'antisemite', 'antisemitism',
+            # Add common slurs (redacted versions)
+            # Note: You can expand this list based on your needs
+        }
+        
+        # Load ML model for phrase-level detection
+        print("Loading offensive content detection model...")
+        self.model_name = "Hate-speech-CNERG/dehatebert-mono-english"
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+        self.model = AutoModelForSequenceClassification.from_pretrained(self.model_name)
+        self.model.eval()
+        
+        # Initialize OCR
+        print("Loading EasyOCR...")
+        self.reader = easyocr.Reader(['en'], gpu=torch.cuda.is_available())
+        print("Model loaded successfully.")
+    
+    def detect_text_regions(self, image):
+        """
+        Detect text regions with improved handling for word clouds.
+        """
+        rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        
+        # Use EasyOCR with adjusted parameters for better word cloud detection
+        results = self.reader.readtext(
+            rgb_image,
+            paragraph=False,  # Don't group into paragraphs
+            min_size=10,      # Detect smaller text
+            text_threshold=0.6  # Lower threshold for varied fonts
+        )
+        
+        text_regions = []
+        for (bbox, text, prob) in results:
+            bbox_array = np.array(bbox)
+            x1 = int(bbox_array[:, 0].min())
+            y1 = int(bbox_array[:, 1].min())
+            x2 = int(bbox_array[:, 0].max())
+            y2 = int(bbox_array[:, 1].max())
+            
+            text_regions.append(([x1, y1, x2, y2], text, prob))
+        
+        return text_regions
+    
+    def is_offensive_keyword(self, text):
+        """
+        Check if text contains offensive keywords.
+        """
+        text_lower = text.lower().strip()
+        
+        # Check exact match
+        if text_lower in self.offensive_keywords:
+            return True, 1.0
+        
+        # Check if any keyword is contained in the text
+        for keyword in self.offensive_keywords:
+            if keyword in text_lower:
+                return True, 0.9
+        
+        return False, 0.0
+    
+    def is_offensive_ml(self, text):
+        """
+        Use ML model to detect offensive content in phrases.
+        """
+        if len(text.strip()) < 3:
+            return False, 0.0
+        
+        inputs = self.tokenizer(text, return_tensors="pt", truncation=True, 
+                               max_length=512, padding=True)
+        
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+            probabilities = torch.softmax(outputs.logits, dim=-1)
+        
+        offensive_prob = probabilities[0][1].item()
+        is_offensive = offensive_prob >= self.confidence_threshold
+        
+        return is_offensive, offensive_prob
+    
+    def is_offensive(self, text):
+        """
+        Combined detection: keyword matching + ML model.
+        Returns True if either method detects offensive content.
+        """
+        # First check keywords (fast)
+        is_keyword_match, keyword_score = self.is_offensive_keyword(text)
+        if is_keyword_match:
+            return True, keyword_score
+        
+        # Then use ML model for context-aware detection
+        is_ml_offensive, ml_score = self.is_offensive_ml(text)
+        if is_ml_offensive:
+            return True, ml_score
+        
+        return False, max(keyword_score, ml_score)
+    
+    def detect_offensive_regions(self, image, verbose=True):
+        """
+        Detect all regions containing offensive content.
+        """
+        text_regions = self.detect_text_regions(image)
+        
+        if verbose:
+            print(f"Detected {len(text_regions)} text region(s)")
+        
+        offensive_regions = []
+        
+        for (bbox, text, ocr_prob) in text_regions:
+            # Clean the text
+            text_cleaned = text.strip()
+            
+            if len(text_cleaned) < 2:
+                continue
+            
+            # Check if offensive
+            is_offensive, confidence = self.is_offensive(text_cleaned)
+            
+            if is_offensive:
+                if verbose:
+                    print(f"⚠️  Offensive: '{text_cleaned}' (confidence: {confidence:.2f})")
+                offensive_regions.append(bbox)
+            elif verbose and len(text_cleaned) > 2:
+                print(f"✓  Clean: '{text_cleaned}' (score: {confidence:.2f})")
+        
+        return offensive_regions

social_moderation/detectors/opencv_face.py

@@ -0,0 +1,41 @@
+# detectors/opencv_face.py
+import cv2
+import os
+
+class OpenCVFace:
+    def __init__(self, conf=0.5):
+        """
+        Face detection using OpenCV's built-in Haar cascades.
+        """
+        self.conf = conf
+        
+        # Load pre-trained Haar cascade for face detection
+        cascade_path = cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
+        if not os.path.exists(cascade_path):
+            # Download if needed (though it should be included with OpenCV)
+            print("Haar cascade not found. Please ensure OpenCV is properly installed.")
+            raise FileNotFoundError("Haar cascade not found")
+            
+        self.face_cascade = cv2.CascadeClassifier(cascade_path)
+
+    def detect_faces(self, image, confidence_threshold=None):
+        """
+        Detect faces using OpenCV's Haar cascades.
+        """
+        # Convert to grayscale for detection
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        
+        # Detect faces
+        faces = self.face_cascade.detectMultiScale(
+            gray,
+            scaleFactor=1.1,
+            minNeighbors=5,
+            minSize=(30, 30)
+        )
+        
+        # Convert to [x1, y1, x2, y2] format
+        boxes = []
+        for (x, y, w, h) in faces:
+            boxes.append([x, y, x + w, y + h])
+            
+        return boxes

social_moderation/detectors/opencv_face_p.py

@@ -0,0 +1,69 @@
+"""
+OpenCV Haar Cascade Face Detector (Fallback)
+Lightweight CPU-based face detection
+"""
+
+import cv2
+import logging
+import numpy as np
+from typing import List, Tuple
+
+logger = logging.getLogger(__name__)
+
+class OpenCVFace:
+    """OpenCV Haar Cascade face detector as lightweight fallback."""
+    
+    def __init__(self):
+        """Initialize Haar Cascade classifier."""
+        try:
+            cascade_path = cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
+            self.face_cascade = cv2.CascadeClassifier(cascade_path)
+            
+            if self.face_cascade.empty():
+                raise IOError("Failed to load Haar cascade")
+            
+            logger.info("✅ OpenCV Haar Cascade face detector loaded")
+            
+        except Exception as e:
+            logger.error(f"Failed to load OpenCV face detector: {e}")
+            raise
+    
+    def detect_faces(self, image: np.ndarray, 
+                    conf_threshold: float = 0.5) -> List[Tuple[int, int, int, int, float]]:
+        """
+        Detect faces using Haar Cascade.
+        
+        Args:
+            image: Input image (BGR)
+            conf_threshold: Unused (kept for API consistency)
+            
+        Returns:
+            List of (x1, y1, x2, y2, confidence)
+        """
+        if image is None or image.size == 0:
+            return []
+        
+        try:
+            # Convert to grayscale
+            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+            
+            # Detect faces
+            faces = self.face_cascade.detectMultiScale(
+                gray,
+                scaleFactor=1.1,
+                minNeighbors=5,
+                minSize=(30, 30),
+                flags=cv2.CASCADE_SCALE_IMAGE
+            )
+            
+            # Convert to (x1, y1, x2, y2, conf) format
+            detections = []
+            for (x, y, w, h) in faces:
+                detections.append((x, y, x + w, y + h, 0.85))  # Fixed confidence
+            
+            logger.debug(f"OpenCV detected {len(detections)} faces")
+            return detections
+            
+        except Exception as e:
+            logger.error(f"OpenCV face detection failed: {e}")
+            return []