📝 [mkdocs] Add mkdocs for documentation

Author: ishandutta0098

.github/workflows/docs.yml

@@ -1,36 +1,118 @@
-name: Build and Deploy Docs
+name: Deploy Documentation
+
 on:
   push:
     branches:
       - main
-      
+      - master
+  pull_request:
+    branches:
+      - main
+      - master
+
 permissions:
-  contents: write
+  contents: read
+  pages: write
+  id-token: write
+
+# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.
+# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.
+concurrency:
+  group: "pages"
+  cancel-in-progress: false
 
 jobs:
-  build-and-deploy:
+  # Build job
+  build:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v2
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
 
-      - name: Set up Python
-        uses: actions/setup-python@v2
+      - name: Setup Python
+        uses: actions/setup-python@v5
         with:
           python-version: '3.11'
-          
+      
+      - name: Cache dependencies
+        uses: actions/cache@v4
+        with:
+          path: ~/.cache/pip
+          key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
+          restore-keys: |
+            ${{ runner.os }}-pip-
+      
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install -e ".[dev]"  # This will install all dev dependencies including sphinx
-          
-      - name: Build docs
+          pip install mkdocs-material
+          pip install mkdocstrings[python]
+          pip install pymdown-extensions
+          # Install the package itself for API documentation
+          pip install -e .
+      
+      - name: Setup Pages
+        id: pages
+        uses: actions/configure-pages@v4
+      
+      - name: Build documentation
         run: |
-          sphinx-apidoc -o docs mukh
-          cd docs
-          make html
-          
+          mkdocs build --clean --strict
+      
+      - name: Upload artifact
+        uses: actions/upload-pages-artifact@v3
+        with:
+          path: ./site
+
+  # Deployment job
+  deploy:
+    if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/master'
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    runs-on: ubuntu-latest
+    needs: build
+    steps:
       - name: Deploy to GitHub Pages
-        uses: peaceiris/actions-gh-pages@v3
+        id: deployment
+        uses: actions/deploy-pages@v4
+
+  # Test documentation build on PRs
+  test-docs:
+    if: github.event_name == 'pull_request'
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+      
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install mkdocs-material
+          pip install mkdocstrings[python]
+          pip install pymdown-extensions
+          # Install the package itself for API documentation
+          pip install -e .
+      
+      - name: Test documentation build
+        run: |
+          mkdocs build --clean --strict
+      
+      - name: Comment PR
+        if: github.event_name == 'pull_request'
+        uses: actions/github-script@v7
         with:
-          github_token: ${{ secrets.GITHUB_TOKEN }}
-          publish_dir: ./docs/_build/html
+          script: |
+            github.rest.issues.createComment({
+              issue_number: context.issue.number,
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              body: '✅ Documentation build test passed!'
+            })

docs/api/core.md

@@ -0,0 +1,246 @@
+# Core Concepts
+
+This section covers the fundamental concepts, data structures, and patterns used throughout Mukh.
+
+## Detection Results
+
+All detection modules return structured data objects that provide consistent access to results.
+
+### Face Detection Results
+
+Face detection methods return lists of detection objects with bounding box information:
+
+```python
+from mukh.face_detection import FaceDetector
+
+detector = FaceDetector.create("mediapipe")
+detections = detector.detect("image.jpg")
+
+# Each detection contains bounding box and confidence
+for detection in detections:
+    print(f"Confidence: {detection.bbox.confidence}")
+    print(f"Position: ({detection.bbox.x1}, {detection.bbox.y1}) to ({detection.bbox.x2}, {detection.bbox.y2})")
+    print(f"Size: {detection.bbox.width} x {detection.bbox.height}")
+    
+    # Some models provide facial landmarks
+    if hasattr(detection, 'landmarks') and detection.landmarks is not None:
+        print(f"Landmarks: {len(detection.landmarks)} points")
+```
+
+### Deepfake Detection Results
+
+Deepfake detection returns classification results with confidence scores:
+
+```python
+from mukh.deepfake_detection import DeepfakeDetector
+
+detector = DeepfakeDetector("efficientnet")
+result = detector.detect("image.jpg")
+
+# Single image result
+print(f"Is deepfake: {result.is_deepfake}")
+print(f"Confidence: {result.confidence}")
+print(f"Model used: {result.model_name}")
+
+# Video results (list of frame results)
+video_results = detector.detect("video.mp4", num_frames=10)
+for frame_result in video_results:
+    print(f"Frame {frame_result.frame_number}: {frame_result.is_deepfake} (conf: {frame_result.confidence})")
+```
+
+## Common Patterns
+
+### Model Selection
+
+All modules use a factory pattern for model creation:
+
+```python
+# Face Detection - choose from blazeface, mediapipe, ultralight
+face_detector = FaceDetector.create("mediapipe")
+
+# Deepfake Detection - choose from resnet_inception, resnext, efficientnet  
+deepfake_detector = DeepfakeDetector("efficientnet")
+
+# Face Reenactment - currently supports tps
+reenactor = FaceReenactor.create("tps")
+```
+
+### File I/O Patterns
+
+All modules support flexible input/output options:
+
+```python
+# Basic detection with file paths
+detections = detector.detect(
+    image_path="input.jpg",
+    save_csv=True,
+    csv_path="results.csv",
+    save_annotated=True,
+    output_folder="output"
+)
+
+# Video processing with frame sampling
+results = detector.detect(
+    media_path="video.mp4",
+    num_frames=15,  # Sample 15 frames
+    output_folder="video_analysis"
+)
+```
+
+### Batch Processing
+
+Process multiple files efficiently:
+
+```python
+import os
+from mukh.face_detection import FaceDetector
+
+detector = FaceDetector.create("mediapipe")
+
+# Process all images in a directory
+image_dir = "input_images"
+for filename in os.listdir(image_dir):
+    if filename.lower().endswith(('.jpg', '.jpeg', '.png')):
+        image_path = os.path.join(image_dir, filename)
+        
+        detections = detector.detect(
+            image_path=image_path,
+            save_csv=True,
+            output_folder=f"output/{filename.split('.')[0]}"
+        )
+        print(f"{filename}: {len(detections)} detections")
+```
+
+## Configuration Patterns
+
+### Confidence Thresholds
+
+Control detection sensitivity across modules:
+
+```python
+# Face detection with custom confidence
+detector = FaceDetector.create("blazeface")
+# Note: confidence filtering happens in post-processing
+
+# Deepfake detection with threshold
+deepfake_detector = DeepfakeDetector("resnext", confidence_threshold=0.7)
+
+# Check if result meets threshold
+result = deepfake_detector.detect("image.jpg")
+if result.confidence >= 0.7:
+    print(f"High confidence detection: {result.is_deepfake}")
+```
+
+### Output Customization
+
+Control what gets saved and where:
+
+```python
+# Minimal output - just get results
+detections = detector.detect("image.jpg")
+
+# Save CSV results only
+detections = detector.detect(
+    "image.jpg",
+    save_csv=True,
+    csv_path="detections.csv"
+)
+
+# Save annotated images with custom folder
+detections = detector.detect(
+    "image.jpg", 
+    save_annotated=True,
+    output_folder="annotated_results"
+)
+
+# Full output - save everything
+detections = detector.detect(
+    "image.jpg",
+    save_csv=True,
+    csv_path="detections.csv", 
+    save_annotated=True,
+    output_folder="full_results"
+)
+```
+
+## Error Handling
+
+### Input Validation
+
+```python
+import os
+from mukh.face_detection import FaceDetector
+
+def safe_detection(image_path):
+    """Safely perform face detection with validation."""
+    # Validate file exists
+    if not os.path.exists(image_path):
+        raise FileNotFoundError(f"Image not found: {image_path}")
+    
+    # Validate file format
+    valid_formats = ('.jpg', '.jpeg', '.png', '.bmp')
+    if not image_path.lower().endswith(valid_formats):
+        raise ValueError(f"Unsupported format. Use: {valid_formats}")
+    
+    try:
+        detector = FaceDetector.create("mediapipe")
+        return detector.detect(image_path)
+    except Exception as e:
+        print(f"Detection failed: {e}")
+        return []
+```
+
+### Graceful Degradation
+
+```python
+def robust_analysis(media_path):
+    """Analyze media with fallback options."""
+    # Try multiple models in order of preference
+    models = ["efficientnet", "resnet_inception", "resnext"]
+    
+    for model_name in models:
+        try:
+            detector = DeepfakeDetector(model_name)
+            return detector.detect(media_path)
+        except Exception as e:
+            print(f"Model {model_name} failed: {e}")
+            continue
+    
+    raise RuntimeError("All models failed")
+```
+
+## Performance Considerations
+
+### Model Selection Guide
+
+| Use Case | Recommended Model | Trade-offs |
+|----------|------------------|------------|
+| **Face Detection** | | |
+| Real-time/Mobile | `blazeface` | Fast, lower accuracy |
+| General purpose | `mediapipe` | Balanced speed/accuracy |
+| CPU-only | `ultralight` | Optimized for CPU |
+| **Deepfake Detection** | | |
+| High accuracy | `resnext` | Slower, more memory |
+| Balanced | `resnet_inception` | Good all-around |
+| Speed optimized | `efficientnet` | Faster inference |
+
+### Memory Management
+
+```python
+# Process large batches efficiently
+import gc
+from mukh.face_detection import FaceDetector
+
+detector = FaceDetector.create("mediapipe")
+
+for batch_start in range(0, len(image_list), batch_size):
+    batch = image_list[batch_start:batch_start + batch_size]
+    
+    for image_path in batch:
+        detections = detector.detect(image_path)
+        # Process results...
+    
+    # Clear memory between batches
+    gc.collect()
+```
+```