Add application file

.gitattributes

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.gitignore

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+__marimo__/
+
+# Streamlit
+.streamlit/secrets.toml

README.md

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+---
+title: AIFaceShapeDetector
+emoji: ⚡
+colorFrom: blue
+colorTo: gray
+sdk: gradio
+sdk_version: 6.5.1
+app_file: app.py
+pinned: false
+license: cc-by-nc-4.0
+short_description: Smple Face Shape Detection using Mediapipe and ML
+---
+
+## AI Face Shape Detector (Hugging Face Space)
+
+Upload a front-facing photo (or use your webcam) and this Space predicts your **face shape** and shows a **MediaPipe face-mesh overlay** plus **confidence scores**.
+
+Full app available at [attractivenesstest.com/face_shape](https://attractivenesstest.com/face_shape).
+
+### Supported face shapes
+
+- **Oval**
+- **Round**
+- **Square**
+- **Heart**
+- **Oblong**
+
+### How it works (from `app.py`)
+
+- **Face landmark extraction**: Uses **MediaPipe Face Mesh** (up to **478 landmarks**) to detect a single face.
+- **Landmark normalization**: Centers landmarks using the eye/iris center, applies roll correction, and scales by inter-eye distance.
+- **Classification**: Flattens normalized landmark coordinates and runs a **pickled scikit-learn classifier** (loaded from `face_shape_model.pkl`) with a label encoder (`label_encoder_rf.pkl`).
+- **Outputs**:
+  - An image with a face mesh/contour overlay
+  - A formatted result card (shape + description + styling tip)
+  - Per-class confidence scores
+
+### Tips for best results
+
+- Use a **front-facing** photo with good lighting
+- Ensure your **entire face** is visible
+- Remove glasses if possible
+- Avoid tilting your head
+
+### Run locally
+
+```bash
+python -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
+python app.py
+```
+
+Then open the Gradio URL printed in your terminal.
+
+### Notes / limitations
+
+- This demo expects **one clear, visible face** in the image (`max_num_faces=1`).
+- If no face is detected, the app will return an error message asking for a clearer photo.
+- Uploaded images are processed in-memory by the app; the code does not intentionally save uploads to disk.
+
+---

app.py

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+"""
+Face Shape Detection - Hugging Face Space App
+Uses MediaPipe for face mesh extraction and a trained ML model for classification.
+"""
+
+import cv2
+import mediapipe as mp
+import numpy as np
+import pickle
+import gradio as gr
+from pathlib import Path
+from PIL import Image
+
+# Paths to model files
+PROJECT_DIR = Path(__file__).parent
+MODEL_FILE = PROJECT_DIR / 'face_shape_model.pkl'
+LABEL_ENCODER_FILE = PROJECT_DIR / 'label_encoder_rf.pkl'
+
+# Face shape descriptions for user-friendly output
+FACE_SHAPE_INFO = {
+    "oval": {
+        "emoji": "🥚",
+        "description": "Balanced proportions with a slightly narrower forehead and jaw. Often considered the most versatile face shape.",
+        "tips": "Most hairstyles and glasses work well with oval faces."
+    },
+    "round": {
+        "emoji": "🌕",
+        "description": "Equal width and length with soft, curved lines. Full cheeks and a rounded chin.",
+        "tips": "Angular frames and layered hairstyles can add definition."
+    },
+    "square": {
+        "emoji": "⬛",
+        "description": "Strong, angular jawline with forehead and jaw of similar width.",
+        "tips": "Round or oval glasses and soft, layered hairstyles complement this shape."
+    },
+    "heart": {
+        "emoji": "❤️",
+        "description": "Wider forehead tapering to a narrower chin, often with prominent cheekbones.",
+        "tips": "Bottom-heavy frames and chin-length hairstyles work great."
+    },
+    "oblong": {
+        "emoji": "📏",
+        "description": "Longer than wide with a straight cheek line and sometimes a longer nose.",
+        "tips": "Wide frames and voluminous hairstyles add width and balance."
+    }
+}
+
+
+def normalize_landmarks(keypoints, width, height):
+    """
+    Normalize keypoints to be centered, roll-corrected, and scaled.
+    Retains 3D coordinates (Z) but aligns to the 2D plane based on eyes.
+    """
+    if not keypoints:
+        return []
+
+    landmarks = np.array([[kp["x"], kp["y"], kp["z"]] for kp in keypoints])
+    
+    # Denormalize to pixel coordinates
+    landmarks[:, 0] *= width
+    landmarks[:, 1] *= height
+    landmarks[:, 2] *= width 
+
+    # Iris indices (refine_landmarks=True gives 478 points)
+    left_iris_idx = 468
+    right_iris_idx = 473
+
+    if len(landmarks) > right_iris_idx:
+        left_iris = landmarks[left_iris_idx]
+        right_iris = landmarks[right_iris_idx]
+    else:
+        # Fallback to eye corners
+        p1 = landmarks[33]
+        p2 = landmarks[133]
+        left_iris = (p1 + p2) / 2
+        p3 = landmarks[362]
+        p4 = landmarks[263]
+        right_iris = (p3 + p4) / 2
+
+    # 1. Centering
+    eye_center = (left_iris + right_iris) / 2.0
+    landmarks -= eye_center
+
+    # 2. Rotation (Roll Correction)
+    delta = left_iris - right_iris
+    dX, dY = delta[0], delta[1]
+    angle = np.arctan2(dY, dX)
+    c, s = np.cos(-angle), np.sin(-angle)
+    
+    R = np.array([
+        [c, -s, 0],
+        [s, c, 0],
+        [0, 0, 1]
+    ])
+    
+    landmarks = landmarks.dot(R.T)
+
+    # 3. Scaling
+    dist = np.sqrt(dX**2 + dY**2)
+    if dist > 0:
+        scale = 1.0 / dist
+        landmarks *= scale
+
+    return [(round(float(l[0]), 5), round(float(l[1]), 5), round(float(l[2]), 5)) 
+            for l in landmarks]
+
+
+def process_image_for_mesh(img_array):
+    """
+    Process image array to get face mesh data using MediaPipe.
+    Returns: keypoints, processed_img or None if failed
+    """
+    max_width_or_height = 512
+
+    mp_face_mesh = mp.solutions.face_mesh
+    
+    with mp_face_mesh.FaceMesh(
+            static_image_mode=True,
+            max_num_faces=1,
+            refine_landmarks=True,
+            min_detection_confidence=0.5) as face_mesh:
+        
+        # Convert PIL to numpy if needed
+        if isinstance(img_array, Image.Image):
+            img_array = np.array(img_array)
+        
+        # Handle RGBA images
+        if len(img_array.shape) == 3 and img_array.shape[2] == 4:
+            img_array = cv2.cvtColor(img_array, cv2.COLOR_RGBA2RGB)
+        
+        # Ensure RGB format
+        if len(img_array.shape) == 3 and img_array.shape[2] == 3:
+            img_rgb = img_array.copy()
+        else:
+            return None, None, "Invalid image format"
+
+        # Downscale large images
+        h, w = img_rgb.shape[:2]
+        longest = max(h, w)
+        if longest > max_width_or_height:
+            scale = max_width_or_height / float(longest)
+            new_w = max(1, int(round(w * scale)))
+            new_h = max(1, int(round(h * scale)))
+            img_rgb = cv2.resize(img_rgb, (new_w, new_h), interpolation=cv2.INTER_AREA)
+        
+        # Process the image
+        results = face_mesh.process(img_rgb)
+        
+        if not results.multi_face_landmarks:
+            return None, None, "No face detected in the image. Please upload a clear photo with a visible face."
+
+        keypoints = []
+        for landmark in results.multi_face_landmarks[0].landmark:
+            keypoints.append({
+                "x": round(landmark.x, 5),
+                "y": round(landmark.y, 5),
+                "z": round(landmark.z, 5)
+            })
+        
+        return keypoints, img_rgb, None
+
+
+def draw_face_mesh_overlay(img_rgb, keypoints):
+    """Draw face mesh overlay on the image for visualization."""
+    img_overlay = img_rgb.copy()
+    h, w = img_overlay.shape[:2]
+    
+    mp_face_mesh = mp.solutions.face_mesh
+    mp_drawing = mp.solutions.drawing_utils
+    mp_drawing_styles = mp.solutions.drawing_styles
+    
+    # Draw key landmark points
+    for i, kp in enumerate(keypoints):
+        x = int(kp["x"] * w)
+        y = int(kp["y"] * h)
+        # Draw small circles at landmark positions
+        cv2.circle(img_overlay, (x, y), 1, (0, 255, 200), -1)
+    
+    # Draw face contour (simplified)
+    contour_indices = [10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,
+                       397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,
+                       172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109, 10]
+    
+    for i in range(len(contour_indices) - 1):
+        idx1 = contour_indices[i]
+        idx2 = contour_indices[i + 1]
+        if idx1 < len(keypoints) and idx2 < len(keypoints):
+            pt1 = (int(keypoints[idx1]["x"] * w), int(keypoints[idx1]["y"] * h))
+            pt2 = (int(keypoints[idx2]["x"] * w), int(keypoints[idx2]["y"] * h))
+            cv2.line(img_overlay, pt1, pt2, (100, 255, 180), 2)
+    
+    return img_overlay
+
+
+# Load model at startup
+print("Loading face shape classification model...")
+try:
+    with open(MODEL_FILE, 'rb') as f:
+        model = pickle.load(f)
+    with open(LABEL_ENCODER_FILE, 'rb') as f:
+        label_encoder = pickle.load(f)
+    print("Model loaded successfully!")
+    MODEL_LOADED = True
+except Exception as e:
+    print(f"Error loading model: {e}")
+    MODEL_LOADED = False
+    model = None
+    label_encoder = None
+
+
+def predict_face_shape(image):
+    """
+    Main prediction function for Gradio interface.
+    """
+    if image is None:
+        return None, "Please upload an image.", ""
+    
+    if not MODEL_LOADED:
+        return None, "Model not loaded. Please check server logs.", ""
+    
+    # Process image and extract landmarks
+    keypoints, img_processed, error = process_image_for_mesh(image)
+    
+    if error:
+        return None, error, ""
+    
+    # Create visualization
+    img_overlay = draw_face_mesh_overlay(img_processed, keypoints)
+    
+    # Normalize landmarks
+    h, w = img_processed.shape[:2]
+    normalized_kpts = normalize_landmarks(keypoints, w, h)
+    
+    # Prepare features (flatten x, y only)
+    flattened_features = []
+    for kp in normalized_kpts:
+        flattened_features.extend([kp[0], kp[1]])
+    
+    features_array = np.array([flattened_features])
+    
+    # Predict
+    probas = model.predict_proba(features_array)[0]
+    prediction_idx = model.predict(features_array)[0]
+    predicted_label = label_encoder.inverse_transform([prediction_idx])[0]
+    
+    # Build results
+    info = FACE_SHAPE_INFO.get(predicted_label.lower(), {
+        "emoji": "✨",
+        "description": "A unique face shape.",
+        "tips": "Embrace your unique features!"
+    })
+    
+    # Format confidence scores
+    confidence_text = ""
+    class_indices = np.argsort(probas)[::-1]
+    for i in class_indices:
+        class_name = label_encoder.classes_[i]
+        score = probas[i]
+        bar = "█" * int(score * 20)
+        confidence_text += f"{class_name.capitalize():10} {bar} {score*100:.1f}%\n"
+    
+    # Main result
+    result_html = f"""
+    <div style="text-align: center; padding: 20px;">
+        <h1 style="font-size: 3em; margin-bottom: 10px;">{info['emoji']}</h1>
+        <h2 style="font-size: 2em; color: #1d4ed8; margin-bottom: 15px;">
+            {predicted_label.upper()}
+        </h2>
+        <p style="font-size: 1.1em; color: #4b5563; margin-bottom: 15px;">
+            {info['description']}
+        </p>
+        <div style="background: linear-gradient(135deg, #eff6ff 0%, #dbeafe 100%);
+                    padding: 15px; border-radius: 12px; margin-top: 15px;">
+            <strong>💡 Style Tips:</strong><br>
+            {info['tips']}
+        </div>
+    </div>
+    """
+    
+    return img_overlay, result_html, confidence_text
+
+
+# Custom CSS for beautiful UI
+custom_css = """
+.gradio-container {
+    font-family: 'Segoe UI', system-ui, sans-serif !important;
+}
+.gradio-container a,
+.gradio-container a:visited {
+    color: #1d4ed8;
+}
+.main-title {
+    text-align: center;
+    background: linear-gradient(135deg, #0ea5e9 0%, #2563eb 55%, #0f172a 100%);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    background-clip: text;
+    font-size: 2.5em !important;
+    font-weight: 700 !important;
+    margin-bottom: 0.5em !important;
+}
+.header-links {
+    display: flex;
+    justify-content: center;
+    gap: 12px;
+    flex-wrap: wrap;
+    margin: 0.25em 0 1.1em 0;
+}
+.header-link {
+    display: inline-flex;
+    align-items: center;
+    gap: 8px;
+    padding: 8px 12px;
+    border-radius: 999px;
+    border: 1px solid #cbd5e1;
+    background: #ffffff;
+    color: #0f172a !important;
+    text-decoration: none !important;
+    font-weight: 600;
+    font-size: 0.95em;
+    box-shadow: 0 1px 2px rgba(15, 23, 42, 0.06);
+}
+.header-link:hover {
+    border-color: #2563eb;
+    box-shadow: 0 6px 18px rgba(37, 99, 235, 0.15);
+    transform: translateY(-1px);
+}
+.header-link:focus-visible {
+    outline: 3px solid rgba(37, 99, 235, 0.35);
+    outline-offset: 2px;
+}
+.subtitle {
+    text-align: center;
+    color: #6b7280;
+    font-size: 1.1em;
+    margin-bottom: 1.5em;
+}
+footer {
+    visibility: hidden;
+}
+"""
+
+# Build Gradio Interface
+with gr.Blocks(
+    css=custom_css,
+    theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky", neutral_hue="slate"),
+) as demo:
+    gr.HTML("""
+        <h1 class="main-title">AI Face Shape Detector</h1>
+        <div class="header-links">
+            <a class="header-link" href="https://attractivenesstest.com/face_shape" target="_blank" rel="noopener noreferrer">
+                Face Shape Detection App
+            </a>
+            <a class="header-link" href="https://github.com/rs75/FaceShapeAI" target="_blank" rel="noopener noreferrer">
+                GitHub
+            </a>
+        </div>
+        <p class="subtitle">Upload a photo to discover your face shape using AI-powered analysis</p>
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_image = gr.Image(
+                label="📷 Upload Your Photo",
+                type="numpy",
+                sources=["upload", "webcam"],
+                height=400
+            )
+            analyze_btn = gr.Button("✨ Analyze Face Shape", variant="primary", size="lg")
+            
+            gr.Markdown("""
+            ### 📋 Tips for Best Results
+            - Use a **front-facing** photo with good lighting
+            - Ensure your **entire face** is visible
+            - Remove glasses if possible
+            - Avoid tilting your head
+            """)
+        
+        with gr.Column(scale=1):
+            output_image = gr.Image(
+                label="🎯 Face Mesh Analysis",
+                height=400
+            )
+            result_html = gr.HTML(label="Result")
+            
+            with gr.Accordion("📊 Confidence Scores", open=False):
+                confidence_output = gr.Textbox(
+                    label="",
+                    lines=6,
+                    interactive=False
+                )
+    
+    gr.HTML("""
+        <div style="text-align: center; margin-top: 30px; padding: 20px; 
+                    background: #f8fafc; border-radius: 12px;">
+            <p style="color: #6b7280; font-size: 0.9em;">
+                🔬 Powered by <strong>MediaPipe</strong> Face Mesh & Machine Learning<br>
+                📐 Analyzes 478 facial landmarks for accurate shape detection
+            </p>
+        </div>
+    """)
+    
+    # Event handlers
+    analyze_btn.click(
+        fn=predict_face_shape,
+        inputs=[input_image],
+        outputs=[output_image, result_html, confidence_output]
+    )
+    
+    input_image.change(
+        fn=predict_face_shape,
+        inputs=[input_image],
+        outputs=[output_image, result_html, confidence_output]
+    )
+
+
+if __name__ == "__main__":
+    demo.launch()

face_shape_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:790585e7c236f108d1131d4d295f28f68b2cbe2702f81ba500c5b8a9ec3294f2
+size 1308871

label_encoder_rf.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4ed219dcc5c0195ddd7f21aed8a86714ba9bada51c3684961a9331bf072af1d7
+size 283

requirements.txt

@@ -0,0 +1,6 @@
+gradio>=4.0.0
+mediapipe==0.10.21
+opencv-python-headless>=4.8.0
+numpy>=1.24.0
+scikit-learn>=1.3.0
+Pillow>=10.0.0