first commit

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
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+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
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+
+# Unit test / coverage reports
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+.coverage.*
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+.hypothesis/
+.pytest_cache/
+
+# Translations
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+
+# Django stuff:
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+# Temporary files
+*.tmp
+*.temp

README.md

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+# POS Face Recognition
+
+## Overview
+
+This Odoo module extends the Point of Sale (POS) system with AI-powered face recognition capabilities. It enables automatic customer identification through facial recognition, providing staff with instant access to customer information, purchase history, and personalized recommendations.
+
+## Features
+
+### 🎯 Core Functionality
+
+- **Real-time Face Recognition**: Automatically identifies customers using webcam in the POS interface
+- **Customer Training**: Capture and store up to 3 face images per customer for accurate recognition
+- **Automatic Sync**: Face images are automatically synchronized with the AI server
+- **Confidence Scoring**: Displays match probability for each recognized customer
+
+### 📊 Customer Insights
+
+When a customer is recognized, the sidebar displays:
+
+- **Customer Information**: Name and contact details
+- **Last 2 Orders**: Complete order history including:
+  - Order number and date
+  - Total amount
+  - Order status (with color-coded badges)
+  - Complete product list with quantities and prices
+- **Top 3 Products**: Most frequently purchased items by the customer
+
+### 🎨 User Interface
+
+- **Sidebar Integration**: Non-intrusive sidebar in the POS product screen
+- **Live Camera Feed**: Real-time video preview for face recognition
+- **Match List**: Shows all potential customer matches with confidence scores
+- **Detailed Order Cards**: Beautifully styled order history with hover effects
+- **Responsive Design**: Optimized for POS touchscreen interfaces
+
+## Requirements
+
+### Odoo Dependencies
+
+- `point_of_sale`
+- `contacts`
+
+### External Dependencies
+
+- **AI Face Recognition Server**: A separate Python Flask server that handles face recognition
+  - Repository: `ai_face_recognition_server` (included in workspace)
+  - Required Python packages: `flask`, `face_recognition`, `numpy`, `opencv-python`
+
+### Hardware Requirements
+
+- Webcam or camera device
+- Sufficient lighting for face recognition
+
+## Installation
+
+### 1. Install the Odoo Module
+
+```bash
+# Copy the module to your Odoo addons directory
+cp -r pos_face_recognition /path/to/odoo/addons/
+
+# Update the addons list in Odoo
+# Navigate to Apps > Update Apps List
+
+# Search for "POS Face Recognition" and install
+```
+
+### 2. Setup the AI Face Recognition Server
+
+```bash
+# Navigate to the AI server directory
+cd ai_face_recognition_server
+
+# Install Python dependencies
+pip install -r requirements.txt
+
+# Start the server
+python app.py
+```
+
+The server will run on `http://localhost:5000` by default.
+
+### 3. Configure the Module
+
+1. Go to **Point of Sale > Configuration > Point of Sale**
+2. Select your POS configuration
+3. In the **Face Recognition** section, set the **Face Recognition Server URL**:
+   ```
+   http://localhost:5000
+   ```
+4. Save the configuration
+
+## Usage
+
+### Training Customer Faces
+
+1. Navigate to **Contacts**
+2. Open a customer record
+3. In the **Face Recognition** tab, you'll find three image fields:
+   - **Face Image 1**
+   - **Face Image 2**
+   - **Face Image 3**
+4. Upload clear, front-facing photos of the customer
+5. Save the record - images are automatically synced to the AI server
+
+**Best Practices for Training Images:**
+- Use well-lit, clear photos
+- Capture different angles and expressions
+- Ensure the face is clearly visible
+- Avoid sunglasses or face coverings
+- Use recent photos
+
+### Using Face Recognition in POS
+
+1. Open the POS session
+2. The face recognition sidebar appears on the right side
+3. The camera automatically starts and begins scanning for faces
+4. When a customer is recognized:
+   - Their name appears in the match list with a confidence score
+   - Click on the match to select the customer
+   - View their order history and top products
+   - The customer is automatically set for the current order
+
+### Understanding the Display
+
+**Match List:**
+- Shows all potential matches with confidence percentage
+- Higher percentage = more confident match
+- Click any match to select that customer
+
+**Order History:**
+- Displays the last 2 orders with complete details
+- Color-coded status badges:
+  - 🟢 Green: Paid/Done
+  - 🔵 Blue: Invoiced
+- Shows all products ordered with quantities and prices
+
+**Top Products:**
+- Highlights the 3 most frequently purchased items
+- Helps staff make personalized recommendations
+
+## Configuration
+
+### POS Configuration
+
+**Settings > Point of Sale > Configuration > Point of Sale**
+
+- `pos_face_rec_server_url`: URL of the AI Face Recognition Server
+
+### Partner Configuration
+
+**Contacts > Customer**
+
+- `image_face_1`: First training image
+- `image_face_2`: Second training image
+- `image_face_3`: Third training image
+
+## Technical Details
+
+### Module Structure
+
+```
+pos_face_recognition/
+├── __init__.py
+├── __manifest__.py
+├── README.md
+├── models/
+│   ├── __init__.py
+│   ├── pos_config.py          # POS configuration extension
+│   ├── res_partner.py          # Customer model with face images
+│   └── res_config_settings.py # Settings configuration
+├── views/
+│   ├── pos_config_views.xml
+│   ├── res_partner_views.xml
+│   └── res_config_settings_views.xml
+└── static/
+    └── src/
+        ├── css/
+        │   └── face_recognition.css
+        ├── js/
+        │   ├── face_recognition_sidebar.js
+        │   ├── models.js
+        │   └── partner_details_edit.js
+        └── xml/
+            ├── face_recognition_screens.xml
+            └── partner_details_edit.xml
+```
+
+### API Endpoints
+
+The module communicates with the AI server using these endpoints:
+
+**POST /train**
+- Trains the AI model with customer face images
+- Payload: `{ partner_id, name, images: [base64_image1, base64_image2, base64_image3] }`
+
+**POST /recognize**
+- Recognizes faces in the provided image
+- Payload: `{ image: base64_image }`
+- Response: `{ matches: [{ id, name, probability }] }`
+
+### Data Flow
+
+1. **Training Phase:**
+   - Customer face images uploaded in Odoo
+   - Images automatically synced to AI server via `/train` endpoint
+   - AI server trains face recognition model
+
+2. **Recognition Phase:**
+   - POS camera captures frames every 5 seconds
+   - Frame sent to AI server via `/recognize` endpoint
+   - Server returns matching customers with confidence scores
+   - POS displays matches and fetches order history
+
+### Database Schema
+
+**res.partner (extended)**
+```python
+image_face_1 = fields.Binary("Face Image 1", attachment=True)
+image_face_2 = fields.Binary("Face Image 2", attachment=True)
+image_face_3 = fields.Binary("Face Image 3", attachment=True)
+```
+
+**pos.config (extended)**
+```python
+pos_face_rec_server_url = fields.Char("Face Recognition Server URL")
+```
+
+## Troubleshooting
+
+### Camera Not Working
+
+- **Check browser permissions**: Ensure the browser has camera access
+- **HTTPS requirement**: Some browsers require HTTPS for camera access
+- **Check console**: Open browser developer tools for error messages
+
+### No Matches Found
+
+- **Verify server connection**: Check if the AI server is running
+- **Check server URL**: Ensure the URL in POS config is correct
+- **Training data**: Verify customer has face images uploaded
+- **Lighting**: Ensure adequate lighting for face detection
+
+### Images Not Syncing
+
+- **Check server URL**: Verify the Face Recognition Server URL is configured
+- **Server logs**: Check AI server logs for errors
+- **Network connectivity**: Ensure Odoo can reach the AI server
+- **Image format**: Ensure images are in supported formats (JPEG, PNG)
+
+### Performance Issues
+
+- **Recognition interval**: Default is 5 seconds, can be adjusted in code
+- **Image quality**: Lower resolution images process faster
+- **Server resources**: Ensure AI server has adequate CPU/RAM
+
+## Development
+
+### Extending the Module
+
+**Add more customer insights:**
+```python
+# In res_partner.py
+def get_customer_stats(self):
+    self.ensure_one()
+    # Add custom logic
+    return {...}
+```
+
+**Customize recognition interval:**
+```javascript
+// In face_recognition_sidebar.js
+this.recognitionInterval = setInterval(() => {
+    this.captureAndSendFrame();
+}, 3000); // Change from 5000 to 3000 for 3 seconds
+```
+
+**Add more order history:**
+```javascript
+// In face_recognition_sidebar.js
+const orders = await this.orm.searchRead("pos.order",
+    [['partner_id', '=', partnerId]],
+    ['name', 'date_order', 'amount_total', 'state', 'lines'],
+    { limit: 5, order: "date_order desc" } // Change from 2 to 5
+);
+```
+
+## Security & Privacy
+
+- Face images are stored securely in Odoo's attachment system
+- Communication with AI server should use HTTPS in production
+- Comply with local privacy laws (GDPR, CCPA, etc.)
+- Obtain customer consent before capturing face images
+- Implement data retention policies
+- Provide customers with opt-out options
+
+## License
+
+LGPL-3
+
+## Support
+
+For issues, questions, or contributions:
+- Check the troubleshooting section
+- Review server logs for detailed error messages
+- Ensure all dependencies are properly installed
+
+## Changelog
+
+### Version 17.0.1.0.0
+- Initial release
+- Real-time face recognition in POS
+- Customer training with 3 images
+- Last 2 orders display with detailed information
+- Top 3 products recommendation
+- Automatic image synchronization
+- Confidence scoring for matches
+- Responsive sidebar UI with enhanced styling
+
+## Credits
+
+Developed for Odoo 17.0

__init__.py

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+from . import models

__manifest__.py

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+{
+    'name': 'POS Face Recognition',
+    'version': '17.0.1.0.0',
+    'category': 'Point of Sale',
+    'summary': 'Face Recognition for POS',
+    'description': """
+        This module extends the Point of Sale to include Face Recognition capabilities.
+        - Capture customer images for training.
+        - Recognize customers in POS using AI.
+    """,
+    'depends': ['point_of_sale', 'contacts'],
+    'data': [
+        'views/res_partner_views.xml',
+        'views/pos_config_views.xml',
+        'views/res_config_settings_views.xml',
+    ],
+    'assets': {
+        'point_of_sale.assets_prod': [
+            'pos_face_recognition/static/src/xml/face_recognition_screens.xml',
+            'pos_face_recognition/static/src/xml/partner_details_edit.xml',
+            'pos_face_recognition/static/src/js/models.js',
+            'pos_face_recognition/static/src/js/face_recognition_sidebar.js',
+            'pos_face_recognition/static/src/js/partner_details_edit.js',
+            'pos_face_recognition/static/src/css/face_recognition.css',
+        ],
+    },
+    'installable': True,
+    'application': False,
+    'license': 'LGPL-3',
+}

ai_face_recognition_server/app.py

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+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import face_recognition
+import numpy as np
+import base64
+import cv2
+import os
+
+app = Flask(__name__)
+CORS(app)  # Enable CORS for all routes
+
+# In-memory storage for known face encodings and names
+known_face_encodings = []
+known_face_names = []
+known_face_ids = []
+
+def load_known_faces():
+    """
+    Load known faces from a directory or database.
+    For this MVP, we'll assume images are stored in a 'faces' directory 
+    with filenames like 'partner_id_name.jpg'.
+    In a real production scenario, you'd fetch these from Odoo or a shared storage.
+    """
+    global known_face_encodings, known_face_names, known_face_ids
+    
+    faces_dir = "faces"
+    if not os.path.exists(faces_dir):
+        os.makedirs(faces_dir)
+        print(f"Created {faces_dir} directory. Please add images there.")
+        return
+
+    for filename in os.listdir(faces_dir):
+        if filename.endswith((".jpg", ".png", ".jpeg")):
+            try:
+                # Filename format: ID_Name.jpg
+                name_part = os.path.splitext(filename)[0]
+                parts = name_part.split('_', 1)
+                if len(parts) == 2:
+                    p_id = int(parts[0])
+                    name = parts[1]
+                else:
+                    p_id = 0
+                    name = name_part
+
+                image_path = os.path.join(faces_dir, filename)
+                image = face_recognition.load_image_file(image_path)
+                encodings = face_recognition.face_encodings(image)
+
+                if encodings:
+                    known_face_encodings.append(encodings[0])
+                    known_face_names.append(name)
+                    known_face_ids.append(p_id)
+                    print(f"Loaded face for: {name} (ID: {p_id})")
+            except Exception as e:
+                print(f"Error loading {filename}: {e}")
+
+@app.route('/recognize', methods=['POST'])
+def recognize_face():
+    data = request.json
+    if 'image' not in data:
+        return jsonify({'error': 'No image provided'}), 400
+
+    try:
+        # Decode base64 image
+        image_data = base64.b64decode(data['image'])
+        nparr = np.frombuffer(image_data, np.uint8)
+        frame = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+        
+        if frame is None:
+            return jsonify({'error': 'Invalid image data'}), 400
+        
+        # Convert BGR (OpenCV) to RGB (face_recognition)
+        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        
+        # Ensure the image is in the correct format
+        rgb_frame = np.ascontiguousarray(rgb_frame, dtype=np.uint8)
+
+        # Find all faces in the current frame
+        face_locations = face_recognition.face_locations(rgb_frame)
+        face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
+
+        matches_list = []
+
+        for face_encoding in face_encodings:
+            # See if the face is a match for the known face(s)
+            matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
+            name = "Unknown"
+            p_id = 0
+            probability = 0.0
+
+            # Or instead, use the known face with the smallest distance to the new face
+            face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
+            if len(face_distances) > 0:
+                best_match_index = np.argmin(face_distances)
+                if matches[best_match_index]:
+                    name = known_face_names[best_match_index]
+                    p_id = known_face_ids[best_match_index]
+                    # Simple probability estimation based on distance (lower distance = higher prob)
+                    # Distance 0.0 -> 100%, Distance 0.6 -> ~0% (threshold)
+                    distance = face_distances[best_match_index]
+                    probability = max(0, 1.0 - distance)
+
+                    matches_list.append({
+                        'id': p_id,
+                        'name': name,
+                        'probability': probability
+                    })
+
+        # Sort by probability
+        matches_list.sort(key=lambda x: x['probability'], reverse=True)
+
+        return jsonify({'matches': matches_list})
+
+    except Exception as e:
+        print(f"Error processing image: {e}")
+        return jsonify({'error': str(e)}), 500
+
+@app.route('/train', methods=['POST'])
+def train_face():
+    data = request.json
+    if 'partner_id' not in data or 'images' not in data:
+        return jsonify({'error': 'Missing partner_id or images'}), 400
+
+    partner_id = data['partner_id']
+    name = data.get('name', 'Unknown')
+    images = data['images'] # List of base64 strings
+
+    faces_dir = "faces"
+    if not os.path.exists(faces_dir):
+        os.makedirs(faces_dir)
+
+    processed_count = 0
+    
+    # Clear existing images for this partner to avoid duplicates/stale data?
+    # For simplicity, we might just overwrite or append. Let's append with index.
+    # Or better, delete old ones first if we want to keep it clean.
+    # Let's just save new ones for now.
+    
+    for idx, img_data in enumerate(images):
+        if not img_data:
+            continue
+            
+        try:
+            # Decode base64
+            image_bytes = base64.b64decode(img_data)
+            filename = f"{partner_id}_{name.replace(' ', '_')}_{idx}.jpg"
+            filepath = os.path.join(faces_dir, filename)
+            
+            with open(filepath, "wb") as f:
+                f.write(image_bytes)
+            
+            # Update in-memory knowledge
+            image = face_recognition.load_image_file(filepath)
+            encodings = face_recognition.face_encodings(image)
+            
+            if encodings:
+                known_face_encodings.append(encodings[0])
+                known_face_names.append(name)
+                known_face_ids.append(partner_id)
+                processed_count += 1
+                print(f"Trained face for: {name} (ID: {partner_id}) - Image {idx}")
+
+        except Exception as e:
+            print(f"Error saving/training image {idx} for {name}: {e}")
+
+    return jsonify({'message': f'Processed {processed_count} images for {name}'})
+
+if __name__ == '__main__':
+    load_known_faces()
+    app.run(host='0.0.0.0', port=5000)

ai_face_recognition_server/requirements.txt

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+flask
+flask-cors
+face_recognition
+numpy
+opencv-python

models/__init__.py

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+from . import res_partner
+from . import res_config_settings
+from . import pos_config
+

models/pos_config.py

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+from odoo import models, fields
+
+
+class PosConfig(models.Model):
+    _inherit = 'pos.config'
+
+    pos_face_rec_server_url = fields.Char(
+        string="Face Recognition Server URL",
+        help="URL of the AI Face Recognition Server (e.g., http://localhost:5000)"
+    )

models/res_config_settings.py

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+from odoo import models, fields
+
+
+class ResConfigSettings(models.TransientModel):
+    _inherit = 'res.config.settings'
+
+    pos_face_rec_server_url = fields.Char(
+        string="AI Face Recognition Server URL",
+        related='pos_config_id.pos_face_rec_server_url',
+        readonly=False,
+        help="URL of the AI Face Recognition Server (e.g., http://localhost:5000)"
+    )

models/res_partner.py

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+from odoo import models, fields, api
+import requests
+import logging
+
+_logger = logging.getLogger(__name__)
+
+class ResPartner(models.Model):
+    _inherit = 'res.partner'
+
+    image_face_1 = fields.Binary("Face Image 1", attachment=True)
+    image_face_2 = fields.Binary("Face Image 2", attachment=True)
+    image_face_3 = fields.Binary("Face Image 3", attachment=True)
+
+    def _sync_face_images(self):
+        # Get the server URL from any POS config that has it configured
+        pos_config = self.env['pos.config'].search([('pos_face_rec_server_url', '!=', False)], limit=1)
+        if not pos_config or not pos_config.pos_face_rec_server_url:
+            _logger.warning("Face Recognition Server URL is not configured in any POS configuration")
+            return
+        
+        server_url = pos_config.pos_face_rec_server_url
+
+        for partner in self:
+            # Only sync if at least one image is present
+            if not any([partner.image_face_1, partner.image_face_2, partner.image_face_3]):
+                continue
+
+            images = [
+                partner.image_face_1.decode('utf-8') if isinstance(partner.image_face_1, bytes) else partner.image_face_1,
+                partner.image_face_2.decode('utf-8') if isinstance(partner.image_face_2, bytes) else partner.image_face_2,
+                partner.image_face_3.decode('utf-8') if isinstance(partner.image_face_3, bytes) else partner.image_face_3
+            ]
+            
+            payload = {
+                'partner_id': partner.id,
+                'name': partner.name,
+                'images': images
+            }
+            
+            try:
+                # Remove trailing slash if present
+                base_url = server_url.rstrip('/')
+                requests.post(f"{base_url}/train", json=payload, timeout=5)
+            except Exception as e:
+                _logger.error(f"Failed to sync face images for partner {partner.id}: {e}")
+
+    @api.model_create_multi
+    def create(self, vals_list):
+        partners = super().create(vals_list)
+        partners._sync_face_images()
+        return partners
+
+    def write(self, vals):
+        res = super().write(vals)
+        if any(f in vals for f in ['image_face_1', 'image_face_2', 'image_face_3', 'name']):
+            self._sync_face_images()
+        return res
+
+    def get_top_products(self):
+        self.ensure_one()
+        # Simple implementation: get top 3 products by quantity from past orders
+        query = """
+            SELECT pt.name, sum(pol.qty) as qty
+            FROM pos_order_line pol
+            JOIN pos_order po ON pol.order_id = po.id
+            JOIN product_product pp ON pol.product_id = pp.id
+            JOIN product_template pt ON pp.product_tmpl_id = pt.id
+            WHERE po.partner_id = %s
+            GROUP BY pt.name
+            ORDER BY qty DESC
+            LIMIT 3
+        """
+        self.env.cr.execute(query, (self.id,))
+        return [{'name': row[0], 'qty': row[1]} for row in self.env.cr.fetchall()]

static/src/css/face_recognition.css

@@ -0,0 +1,255 @@
+.face-recognition-sidebar {
+    position: absolute;
+    top: 65px;
+    right: 0;
+    bottom: 0;
+    width: 300px;
+    background: white;
+    border-left: 1px solid #ccc;
+    z-index: 100;
+    display: flex;
+    flex-direction: column;
+    box-shadow: -2px 0 5px rgba(0, 0, 0, 0.1);
+}
+
+.sidebar-header {
+    padding: 10px;
+    background: #f0f0f0;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    border-bottom: 1px solid #ccc;
+}
+
+.sidebar-content {
+    flex: 1;
+    overflow-y: auto;
+    padding: 10px;
+}
+
+.camera-container video {
+    background: #000;
+    border-radius: 4px;
+}
+
+.match-list {
+    list-style: none;
+    padding: 0;
+}
+
+.match-item {
+    padding: 8px;
+    border-bottom: 1px solid #eee;
+    cursor: pointer;
+}
+
+.match-item:hover {
+    background: #f9f9f9;
+}
+
+.match-info {
+    display: flex;
+    justify-content: space-between;
+}
+
+.customer-details {
+    margin-top: 20px;
+    border-top: 2px solid #eee;
+    padding-top: 10px;
+}
+
+.close-btn {
+    background: none;
+    border: none;
+    font-size: 1.2em;
+    cursor: pointer;
+}
+
+.face-recognition-sidebar .control-button {
+    cursor: pointer;
+}
+
+.face-recognition-sidebar.hidden {
+    display: none;
+}
+
+/* Add padding to ProductScreen to avoid sidebar overlap */
+.pos .product-screen {
+    padding-right: 305px;
+}
+/* Order 
+Details Styling */
+.last-orders {
+    margin-top: 15px;
+}
+
+.last-orders h5 {
+    margin-bottom: 10px;
+    color: #333;
+    font-size: 14px;
+    font-weight: 600;
+}
+
+.orders-list {
+    display: flex;
+    flex-direction: column;
+    gap: 12px;
+}
+
+.order-card {
+    background: #f8f9fa;
+    border: 1px solid #e0e0e0;
+    border-radius: 6px;
+    padding: 10px;
+    transition: box-shadow 0.2s;
+}
+
+.order-card:hover {
+    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
+}
+
+.order-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: 8px;
+    padding-bottom: 6px;
+    border-bottom: 1px solid #d0d0d0;
+}
+
+.order-number {
+    font-weight: 600;
+    color: #2c3e50;
+    font-size: 13px;
+}
+
+.order-date {
+    font-size: 11px;
+    color: #7f8c8d;
+}
+
+.order-details {
+    margin-bottom: 8px;
+}
+
+.order-row {
+    display: flex;
+    justify-content: space-between;
+    padding: 3px 0;
+    font-size: 12px;
+}
+
+.order-row .label {
+    color: #666;
+    font-weight: 500;
+}
+
+.order-row .value {
+    color: #333;
+    font-weight: 600;
+}
+
+.order-row .value.status {
+    text-transform: capitalize;
+    padding: 2px 8px;
+    border-radius: 3px;
+    font-size: 11px;
+}
+
+.order-row .value.status.paid {
+    background: #d4edda;
+    color: #155724;
+}
+
+.order-row .value.status.done {
+    background: #d4edda;
+    color: #155724;
+}
+
+.order-row .value.status.invoiced {
+    background: #cce5ff;
+    color: #004085;
+}
+
+.order-products {
+    margin-top: 8px;
+    padding-top: 8px;
+    border-top: 1px dashed #d0d0d0;
+}
+
+.products-label {
+    font-size: 11px;
+    color: #666;
+    font-weight: 600;
+    margin-bottom: 5px;
+}
+
+.product-list {
+    list-style: none;
+    padding: 0;
+    margin: 0;
+}
+
+.product-item {
+    display: flex;
+    align-items: center;
+    padding: 4px 0;
+    font-size: 11px;
+    gap: 6px;
+}
+
+.product-qty {
+    background: #007bff;
+    color: white;
+    padding: 2px 6px;
+    border-radius: 3px;
+    font-weight: 600;
+    min-width: 30px;
+    text-align: center;
+}
+
+.product-name {
+    flex: 1;
+    color: #333;
+}
+
+.product-price {
+    color: #28a745;
+    font-weight: 600;
+}
+
+.no-orders {
+    color: #999;
+    font-style: italic;
+    font-size: 12px;
+    text-align: center;
+    padding: 20px 0;
+}
+
+/* Top Products Styling */
+.top-products {
+    margin-top: 15px;
+    padding-top: 15px;
+    border-top: 2px solid #e0e0e0;
+}
+
+.top-products h5 {
+    margin-bottom: 10px;
+    color: #333;
+    font-size: 14px;
+    font-weight: 600;
+}
+
+.top-products ul {
+    list-style: none;
+    padding: 0;
+}
+
+.top-products li {
+    padding: 6px 8px;
+    background: #fff3cd;
+    border-left: 3px solid #ffc107;
+    margin-bottom: 6px;
+    font-size: 12px;
+    border-radius: 3px;
+}

static/src/js/face_recognition_sidebar.js

@@ -0,0 +1,184 @@
+/** @odoo-module */
+
+import { Component, useState, onMounted, onWillUnmount } from "@odoo/owl";
+import { ProductScreen } from "@point_of_sale/app/screens/product_screen/product_screen";
+import { useService } from "@web/core/utils/hooks";
+import { registry } from "@web/core/registry";
+
+export class FaceRecognitionSidebar extends Component {
+    static template = "pos_face_recognition.FaceRecognitionSidebar";
+
+    setup() {
+        this.pos = useService("pos");
+        this.orm = useService("orm");
+        console.log("FaceRecognitionSidebar setup");
+        this.state = useState({
+            matches: [],
+            selectedCustomer: null,
+            lastOrders: [],
+            topProducts: [],
+        });
+        this.recognitionInterval = null;
+        this.stream = null;
+
+        onMounted(() => {
+            this.startCamera();
+        });
+
+        // Cleanup interval and camera when component is destroyed
+        onWillUnmount(() => {
+            if (this.recognitionInterval) {
+                clearInterval(this.recognitionInterval);
+            }
+            if (this.stream) {
+                this.stream.getTracks().forEach(track => track.stop());
+            }
+        });
+    }
+
+    async startCamera() {
+        console.log("startCamera called");
+        try {
+            const video = document.getElementById('face-rec-video');
+            console.log("Video element found:", video);
+            if (video) {
+                this.stream = await navigator.mediaDevices.getUserMedia({ video: true });
+                console.log("Camera stream obtained:", this.stream);
+                video.srcObject = this.stream;
+
+                // Start real-time recognition loop once camera is ready
+                this.startRealTimeRecognition();
+            } else {
+                console.error("Video element not found!");
+            }
+        } catch (err) {
+            console.error("Error accessing camera:", err);
+        }
+    }
+
+    startRealTimeRecognition() {
+        console.log("startRealTimeRecognition called");
+        console.log("Server URL:", this.pos.config.pos_face_rec_server_url);
+        
+        if (!this.pos.config.pos_face_rec_server_url) {
+            console.error("Face Recognition Server URL is not configured. Please configure it in POS Settings.");
+            return;
+        }
+        
+        // Capture frame every 5 seconds
+        this.recognitionInterval = setInterval(() => {
+            console.log("Recognition interval tick");
+            this.captureAndSendFrame();
+        }, 5000);
+    }
+
+    async captureAndSendFrame() {
+        const video = document.getElementById('face-rec-video');
+        if (!video) {
+            console.error("Video element not found");
+            return;
+        }
+        
+        if (!this.pos.config.pos_face_rec_server_url) {
+            console.error("Face Recognition Server URL is not configured");
+            return;
+        }
+
+        const canvas = document.createElement('canvas');
+        canvas.width = video.videoWidth;
+        canvas.height = video.videoHeight;
+        canvas.getContext('2d').drawImage(video, 0, 0);
+        // Convert to base64, remove header
+        const imageData = canvas.toDataURL('image/jpeg').split(',')[1];
+
+        try {
+            const response = await fetch(this.pos.config.pos_face_rec_server_url + '/recognize', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                },
+                body: JSON.stringify({ image: imageData }),
+            });
+
+            if (response.ok) {
+                const result = await response.json();
+                if (result.matches && result.matches.length > 0) {
+                    // Enrich matches with partner phone numbers
+                    this.state.matches = result.matches.map(match => {
+                        const partner = this.pos.db.get_partner_by_id(match.id);
+                        return {
+                            ...match,
+                            phone: partner ? partner.phone || partner.mobile : null
+                        };
+                    });
+                }
+            }
+        } catch (err) {
+            console.error("Error sending frame to AI server:", err);
+        }
+    }
+
+    async selectCustomer(match) {
+        const partner = this.pos.db.get_partner_by_id(match.id);
+        if (partner) {
+            this.state.selectedCustomer = partner;
+            this.pos.get_order().set_partner(partner);
+
+            // Fetch details
+            await this.fetchCustomerDetails(partner.id);
+        }
+    }
+
+    async fetchCustomerDetails(partnerId) {
+        // Fetch last 2 orders with detailed information
+        const orders = await this.orm.searchRead("pos.order",
+            [['partner_id', '=', partnerId]],
+            ['name', 'date_order', 'amount_total', 'state', 'lines'],
+            { limit: 2, order: "date_order desc" }
+        );
+        
+        // Fetch order lines for each order
+        for (let order of orders) {
+            if (order.lines && order.lines.length > 0) {
+                const lines = await this.orm.searchRead("pos.order.line",
+                    [['id', 'in', order.lines]],
+                    ['product_id', 'qty', 'price_subtotal_incl', 'full_product_name'],
+                    {}
+                );
+                // Add product names to lines
+                order.lines = lines.map(line => ({
+                    ...line,
+                    product_name: line.full_product_name || (line.product_id && line.product_id[1]) || 'Unknown Product'
+                }));
+            } else {
+                order.lines = [];
+            }
+        }
+        
+        this.state.lastOrders = orders;
+
+        // Fetch top 3 products
+        try {
+            const topProducts = await this.orm.call("res.partner", "get_top_products", [partnerId]);
+            console.log("Top products received:", topProducts);
+            console.log("Type:", typeof topProducts);
+            console.log("First product:", topProducts && topProducts[0]);
+            
+            // Ensure it's an array
+            if (Array.isArray(topProducts)) {
+                this.state.topProducts = topProducts;
+            } else {
+                console.error("topProducts is not an array:", topProducts);
+                this.state.topProducts = [];
+            }
+        } catch (error) {
+            console.error("Error fetching top products:", error);
+            this.state.topProducts = [];
+        }
+    }
+}
+
+ProductScreen.components = {
+    ...ProductScreen.components,
+    FaceRecognitionSidebar,
+};

static/src/js/models.js

@@ -0,0 +1,20 @@
+/** @odoo-module */
+
+import { PosStore } from "@point_of_sale/app/store/pos_store";
+import { patch } from "@web/core/utils/patch";
+
+patch(PosStore.prototype, {
+    async _processData(loadedData) {
+        await super._processData(...arguments);
+        // We don't necessarily need to load the full image data for all partners on load
+        // because it would be heavy. We only need to send it back when saving.
+        // However, if we want to show existing images in the edit screen, we might need them.
+        // But standard Odoo doesn't load full images for partners in POS to save bandwidth.
+        // It usually loads small avatars.
+        // For now, let's NOT load them by default to avoid performance issues.
+        // The edit screen will show empty if not modified in current session, 
+        // or we could fetch them on demand (like `partnerImageUrl` does).
+        // BUT, `saveChanges` in `partner_editor.js` sends `processedChanges`.
+        // If we want to support editing, we need to handle the fields.
+    }
+});

static/src/js/partner_details_edit.js

@@ -0,0 +1,107 @@
+/** @odoo-module */
+
+import { PartnerDetailsEdit } from "@point_of_sale/app/screens/partner_list/partner_editor/partner_editor";
+import { patch } from "@web/core/utils/patch";
+import { getDataURLFromFile } from "@web/core/utils/urls";
+import { ErrorPopup } from "@point_of_sale/app/errors/popups/error_popup";
+import { _t } from "@web/core/l10n/translation";
+import { loadImage } from "@point_of_sale/utils";
+import { useRef, useState, onWillUnmount } from "@odoo/owl";
+
+patch(PartnerDetailsEdit.prototype, {
+    setup() {
+        super.setup(...arguments);
+        this.changes.image_face_1 = this.changes.image_face_1 || false;
+        this.changes.image_face_2 = this.changes.image_face_2 || false;
+        this.changes.image_face_3 = this.changes.image_face_3 || false;
+
+        this.camera = useState({
+            activeField: null,
+        });
+        this.videoRef = useRef("video");
+
+        onWillUnmount(() => {
+            this.stopCamera();
+        });
+    },
+
+    async startCamera(fieldName) {
+        if (this.camera.activeField) {
+            this.stopCamera();
+        }
+        try {
+            const stream = await navigator.mediaDevices.getUserMedia({ video: true });
+            this.camera.activeField = fieldName;
+            this.stream = stream;
+            // Wait for the video element to be rendered
+            await Promise.resolve();
+            // We might need a small delay or rely on the ref being attached on render
+            // Since we set state, a render will happen. The ref should be available after render.
+            // However, in Owl, refs are usually available after patch.
+            // Let's try setting it in a next tick or just relying on the template to use the ref.
+            // Actually, we need to set srcObject.
+            setTimeout(() => {
+                if (this.videoRef.el) {
+                    this.videoRef.el.srcObject = stream;
+                }
+            }, 100);
+        } catch (err) {
+            console.error("Camera error", err);
+            this.popup.add(ErrorPopup, { title: _t("Camera Error"), body: _t("Could not access camera.") });
+        }
+    },
+
+    stopCamera() {
+        if (this.stream) {
+            this.stream.getTracks().forEach(track => track.stop());
+            this.stream = null;
+        }
+        this.camera.activeField = null;
+    },
+
+    capturePhoto() {
+        if (!this.videoRef.el) return;
+        const video = this.videoRef.el;
+        const canvas = document.createElement("canvas");
+        canvas.width = video.videoWidth;
+        canvas.height = video.videoHeight;
+        const ctx = canvas.getContext("2d");
+        ctx.drawImage(video, 0, 0);
+
+        const dataUrl = canvas.toDataURL('image/jpeg');
+        // Remove data:image/jpeg;base64, prefix
+        this.changes[this.camera.activeField] = dataUrl.split(',')[1];
+        this.stopCamera();
+    },
+
+    async uploadFaceImage(event, fieldName) {
+        const file = event.target.files[0];
+        if (!file) return;
+
+        if (!file.type.match(/image.*/)) {
+            await this.popup.add(ErrorPopup, {
+                title: _t("Unsupported File Format"),
+                body: _t("Only web-compatible Image formats such as .png or .jpeg are supported."),
+            });
+            return;
+        }
+
+        const imageUrl = await getDataURLFromFile(file);
+        const loadedImage = await loadImage(imageUrl, {
+            onError: () => {
+                this.popup.add(ErrorPopup, {
+                    title: _t("Loading Image Error"),
+                    body: _t("Encountered error when loading image. Please try again."),
+                });
+            }
+        });
+
+        if (loadedImage) {
+            // Resize to reasonable size for face recognition (e.g. 800x600 is usually enough)
+            const resizedImage = await this._resizeImage(loadedImage, 800, 600);
+            const dataUrl = resizedImage.toDataURL();
+            // Remove data:image/png;base64, prefix
+            this.changes[fieldName] = dataUrl.split(',')[1];
+        }
+    }
+});

static/src/xml/face_recognition_screens.xml

@@ -0,0 +1,104 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<templates id="template" xml:space="preserve">
+
+    <t t-name="pos_face_recognition.FaceRecognitionButton" owl="1">
+        <div class="control-button" t-on-click="onClick">
+            <i class="fa fa-camera" role="img" aria-label="Face Recognition" title="Face Recognition"/>
+            <span>Face Rec.</span>
+        </div>
+    </t>
+
+    <t t-name="pos_face_recognition.FaceRecognitionSidebar" owl="1">
+        <div class="face-recognition-sidebar">
+            <div class="sidebar-header">
+                <h3>Face Recognition</h3>
+            </div>
+            <div class="sidebar-content">
+                <div class="camera-container">
+                    <video id="face-rec-video" autoplay="true" playsinline="true" width="100%"></video>
+                </div>
+                
+                <div class="results-container" t-if="state.matches.length > 0">
+                    <h4>Matches:</h4>
+                    <ul class="match-list">
+                        <t t-foreach="state.matches" t-as="match" t-key="match.id">
+                            <li t-on-click="() => this.selectCustomer(match)" class="match-item">
+                                <div class="match-info">
+                                    <div>
+                                        <span class="match-name"><t t-esc="match.name"/></span>
+                                        <span class="match-phone" t-if="match.phone"><br/><t t-esc="match.phone"/></span>
+                                    </div>
+                                    <span class="match-prob"><t t-esc="(match.probability * 100).toFixed(1)"/>%</span>
+                                </div>
+                            </li>
+                        </t>
+                    </ul>
+                </div>
+
+                <div class="customer-details" t-if="state.selectedCustomer">
+                    <h4>Selected: <t t-esc="state.selectedCustomer.name"/></h4>
+                    
+                    <div class="last-orders">
+                        <h5>Last 2 Orders:</h5>
+                        <div t-if="state.lastOrders and state.lastOrders.length > 0" class="orders-list">
+                            <t t-foreach="state.lastOrders" t-as="order" t-key="order.id">
+                                <div class="order-card">
+                                    <div class="order-header">
+                                        <span class="order-number">Order #<t t-esc="order.name || order.id"/></span>
+                                        <span class="order-date"><t t-esc="order.date_order"/></span>
+                                    </div>
+                                    <div class="order-details">
+                                        <div class="order-row">
+                                            <span class="label">Total:</span>
+                                            <span class="value"><t t-esc="env.utils.formatCurrency(order.amount_total)"/></span>
+                                        </div>
+                                        <div class="order-row" t-if="order.lines and order.lines.length > 0">
+                                            <span class="label">Items:</span>
+                                            <span class="value"><t t-esc="order.lines.length"/> product(s)</span>
+                                        </div>
+                                        <div class="order-row" t-if="order.state">
+                                            <span class="label">Status:</span>
+                                            <span class="value status" t-att-class="order.state"><t t-esc="order.state"/></span>
+                                        </div>
+                                    </div>
+                                    <div class="order-products" t-if="order.lines and order.lines.length > 0">
+                                        <div class="products-label">Products:</div>
+                                        <ul class="product-list">
+                                            <t t-foreach="order.lines" t-as="line" t-key="line.id">
+                                                <li class="product-item">
+                                                    <span class="product-qty"><t t-esc="line.qty"/>x</span>
+                                                    <span class="product-name"><t t-esc="line.product_name"/></span>
+                                                    <span class="product-price"><t t-esc="env.utils.formatCurrency(line.price_subtotal_incl)"/></span>
+                                                </li>
+                                            </t>
+                                        </ul>
+                                    </div>
+                                </div>
+                            </t>
+                        </div>
+                        <p t-else="" class="no-orders">No previous orders found</p>
+                    </div>
+                    <br/>
+                    <div class="top-products">
+                        <h5>Top 3 Products:</h5>
+                        <ul t-if="state.topProducts and state.topProducts.length > 0">
+                            <t t-foreach="state.topProducts" t-as="prod" t-key="prod_index">
+                                <li>
+                                    <t t-out="prod['name']['en_US']"/> (Qty: <t t-out="Math.round(prod['qty'])"/>)
+                                </li>
+                            </t>
+                        </ul>
+                        <p t-else="">No previous orders found</p>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </t>
+
+    <t t-name="pos_face_recognition.ProductScreen" t-inherit="point_of_sale.ProductScreen" t-inherit-mode="extension">
+        <xpath expr="//div[hasclass('product-screen')]" position="inside">
+            <FaceRecognitionSidebar />
+        </xpath>
+    </t>
+
+</templates>

static/src/xml/partner_details_edit.xml

@@ -0,0 +1,79 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<templates id="template" xml:space="preserve">
+    <t t-name="pos_face_recognition.PartnerDetailsEdit" t-inherit="point_of_sale.PartnerDetailsEdit" t-inherit-mode="extension">
+        <xpath expr="//div[hasclass('partner-details-box')]" position="inside">
+            <div class="partner-detail col">
+                <label class="form-label label">Face Image 1</label>
+                <div class="d-flex flex-column gap-2">
+                    <t t-if="camera.activeField === 'image_face_1'">
+                        <div class="position-relative text-center">
+                            <video t-ref="video" autoplay="autoplay" playsinline="playsinline" style="width: 100%; max-width: 300px; background: #000;" />
+                            <div class="d-flex gap-2 mt-1 justify-content-center">
+                                <button class="btn btn-primary" t-on-click="capturePhoto">Capture</button>
+                                <button class="btn btn-secondary" t-on-click="stopCamera">Cancel</button>
+                            </div>
+                        </div>
+                    </t>
+                    <t t-else="">
+                        <div class="d-flex align-items-center gap-2">
+                            <t t-if="changes.image_face_1">
+                                <img t-att-src="'data:image/jpeg;base64,' + changes.image_face_1" style="width: 64px; height: 64px; object-fit: cover;" class="rounded"/>
+                            </t>
+                            <button class="btn btn-secondary" t-on-click="() => this.startCamera('image_face_1')">
+                                <i class="fa fa-camera"/> Take Photo
+                            </button>
+                        </div>
+                    </t>
+                </div>
+            </div>
+            <div class="partner-detail col">
+                <label class="form-label label">Face Image 2</label>
+                <div class="d-flex flex-column gap-2">
+                    <t t-if="camera.activeField === 'image_face_2'">
+                        <div class="position-relative text-center">
+                            <video t-ref="video" autoplay="autoplay" playsinline="playsinline" style="width: 100%; max-width: 300px; background: #000;" />
+                            <div class="d-flex gap-2 mt-1 justify-content-center">
+                                <button class="btn btn-primary" t-on-click="capturePhoto">Capture</button>
+                                <button class="btn btn-secondary" t-on-click="stopCamera">Cancel</button>
+                            </div>
+                        </div>
+                    </t>
+                    <t t-else="">
+                        <div class="d-flex align-items-center gap-2">
+                            <t t-if="changes.image_face_2">
+                                <img t-att-src="'data:image/jpeg;base64,' + changes.image_face_2" style="width: 64px; height: 64px; object-fit: cover;" class="rounded"/>
+                            </t>
+                            <button class="btn btn-secondary" t-on-click="() => this.startCamera('image_face_2')">
+                                <i class="fa fa-camera"/> Take Photo
+                            </button>
+                        </div>
+                    </t>
+                </div>
+            </div>
+            <div class="partner-detail col">
+                <label class="form-label label">Face Image 3</label>
+                <div class="d-flex flex-column gap-2">
+                    <t t-if="camera.activeField === 'image_face_3'">
+                        <div class="position-relative text-center">
+                            <video t-ref="video" autoplay="autoplay" playsinline="playsinline" style="width: 100%; max-width: 300px; background: #000;" />
+                            <div class="d-flex gap-2 mt-1 justify-content-center">
+                                <button class="btn btn-primary" t-on-click="capturePhoto">Capture</button>
+                                <button class="btn btn-secondary" t-on-click="stopCamera">Cancel</button>
+                            </div>
+                        </div>
+                    </t>
+                    <t t-else="">
+                        <div class="d-flex align-items-center gap-2">
+                            <t t-if="changes.image_face_3">
+                                <img t-att-src="'data:image/jpeg;base64,' + changes.image_face_3" style="width: 64px; height: 64px; object-fit: cover;" class="rounded"/>
+                            </t>
+                            <button class="btn btn-secondary" t-on-click="() => this.startCamera('image_face_3')">
+                                <i class="fa fa-camera"/> Take Photo
+                            </button>
+                        </div>
+                    </t>
+                </div>
+            </div>
+        </xpath>
+    </t>
+</templates>

views/pos_config_views.xml

@@ -0,0 +1,15 @@
+<?xml version="1.0" encoding="utf-8"?>
+<odoo>
+    <record id="pos_config_view_form_inherit_face_recognition" model="ir.ui.view">
+        <field name="name">pos.config.view.form.inherit.face.recognition</field>
+        <field name="model">pos.config</field>
+        <field name="inherit_id" ref="point_of_sale.pos_config_view_form"/>
+        <field name="arch" type="xml">
+            <xpath expr="//div[@class='row mt16 o_settings_container']/div[@groups='base.group_system']" position="before">
+                <setting string="Face Recognition" help="Configure the AI server for face recognition.">
+                    <field name="pos_face_rec_server_url" placeholder="http://localhost:5000"/>
+                </setting>
+            </xpath>
+        </field>
+    </record>
+</odoo>

views/res_config_settings_views.xml

@@ -0,0 +1,15 @@
+<?xml version="1.0" encoding="utf-8"?>
+<odoo>
+    <record id="res_config_settings_view_form" model="ir.ui.view">
+        <field name="name">res.config.settings.view.form.inherit.pos.face.recognition</field>
+        <field name="model">res.config.settings</field>
+        <field name="inherit_id" ref="point_of_sale.res_config_settings_view_form"/>
+        <field name="arch" type="xml">
+            <xpath expr="//block[@id='pos_interface_section']" position="inside">
+                <setting string="Face Recognition" help="Configure the AI server for face recognition.">
+                    <field name="pos_face_rec_server_url" placeholder="http://localhost:5000"/>
+                </setting>
+            </xpath>
+        </field>
+    </record>
+</odoo>

views/res_partner_views.xml

@@ -0,0 +1,21 @@
+<?xml version="1.0" encoding="utf-8"?>
+<odoo>
+    <record id="view_partner_form_face_recognition" model="ir.ui.view">
+        <field name="name">res.partner.form.face.recognition</field>
+        <field name="model">res.partner</field>
+        <field name="inherit_id" ref="base.view_partner_form"/>
+        <field name="arch" type="xml">
+            <xpath expr="//notebook" position="inside">
+                <page string="Face Recognition" name="face_recognition">
+                    <group>
+                        <group>
+                            <field name="image_face_1" widget="image" class="oe_avatar"/>
+                            <field name="image_face_2" widget="image" class="oe_avatar"/>
+                            <field name="image_face_3" widget="image" class="oe_avatar"/>
+                        </group>
+                    </group>
+                </page>
+            </xpath>
+        </field>
+    </record>
+</odoo>