Update app.py

app.py

@@ -2,52 +2,94 @@ import gradio as gr
 import cv2
 import numpy as np
 import tempfile
+import os
 import mediapipe as mp
-from PIL import Image
+from sklearn.cluster import KMeans
 
-# Mediapipe face detection setup
-mp_face_detection = mp.solutions.face_detection
+# --- Face analysis setup ---
 mp_face_mesh = mp.solutions.face_mesh
-mp_drawing = mp.solutions.drawing_utils
-mp_drawing_styles = mp.solutions.drawing_styles
-
-def analyze_face_features(image):
-    # Convert to BGR for OpenCV compatibility
-    image_np = np.array(image)
-    image_np = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
-
-    # Face Mesh Detection
-    with mp_face_mesh.FaceMesh(static_image_mode=True, max_num_faces=1, refine_landmarks=True, min_detection_confidence=0.5) as face_mesh:
-        results = face_mesh.process(cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB))
-
-        if results.multi_face_landmarks:
-            for face_landmarks in results.multi_face_landmarks:
-                mp_drawing.draw_landmarks(
-                    image=image_np,
-                    landmark_list=face_landmarks,
-                    connections=mp_face_mesh.FACEMESH_TESSELATION,
-                    landmark_drawing_spec=None,
-                    connection_drawing_spec=mp_drawing_styles.get_default_face_mesh_tesselation_style())
-                mp_drawing.draw_landmarks(
-                    image=image_np,
-                    landmark_list=face_landmarks,
-                    connections=mp_face_mesh.FACEMESH_CONTOURS,
-                    landmark_drawing_spec=None,
-                    connection_drawing_spec=mp_drawing_styles.get_default_face_mesh_contours_style())
-        else:
-            print("No facial landmarks detected")
-
-    # Save processed image temporarily
-    temp_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
-    cv2.imwrite(temp_file.name, image_np)
-
-    return Image.fromarray(cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB))
-
-# Gradio Interface
+face_mesh = mp_face_mesh.FaceMesh(static_image_mode=True)
+
+# --- Face Shape Detection Logic ---
+def detect_face_shape(landmarks):
+    jaw_width = np.linalg.norm(np.array(landmarks[234]) - np.array(landmarks[454]))
+    face_height = np.linalg.norm(np.array(landmarks[10]) - np.array(landmarks[152]))
+
+    ratio = jaw_width / face_height
+    if ratio > 1.1:
+        return "Round"
+    elif 0.9 <= ratio <= 1.1:
+        return "Oval"
+    elif ratio < 0.9:
+        return "Heart"
+    return "Unknown"
+
+# --- Skin Tone Detection ---
+def detect_skin_tone(image):
+    rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    pixels = rgb_image.reshape(-1, 3)
+    kmeans = KMeans(n_clusters=3, random_state=0).fit(pixels)
+    dominant_color = np.uint8(kmeans.cluster_centers_[0])
+
+    avg_brightness = np.mean(dominant_color)
+    if avg_brightness > 200:
+        return "Fair"
+    elif avg_brightness > 100:
+        return "Medium"
+    else:
+        return "Dark"
+
+# --- Rule-Based Recommender ---
+def recommend_mask(face_shape, skin_tone):
+    recommendations = {
+        ("Round", "Fair"): "Floral",
+        ("Oval", "Medium"): "Metallic",
+        ("Square", "Dark"): "Classic",
+        ("Heart", "Fair"): "Glitter",
+        ("Heart", "Medium"): "Glitter",
+        ("Heart", "Dark"): "Glitter",
+        ("Diamond", "Fair"): "Lace",
+        ("Diamond", "Medium"): "Lace",
+        ("Diamond", "Dark"): "Lace",
+    }
+    return recommendations.get((face_shape, skin_tone), "Classic")
+
+# --- Main function ---
+def analyze_face(image):
+    if image is None:
+        return None, None, None
+
+    # Save to temp file
+    tmp_path = tempfile.mktemp(suffix=".png")
+    image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    cv2.imwrite(tmp_path, image_bgr)
+
+    # Face landmarks
+    results = face_mesh.process(image)
+    if not results.multi_face_landmarks:
+        return "No face detected", None, None
+
+    landmarks = results.multi_face_landmarks[0].landmark
+    h, w, _ = image.shape
+    landmark_points = [(int(p.x * w), int(p.y * h)) for p in landmarks]
+
+    face_shape = detect_face_shape(landmark_points)
+    skin_tone = detect_skin_tone(image_bgr)
+    mask_style = recommend_mask(face_shape, skin_tone)
+
+    return f"Face Shape: {face_shape}, Skin Tone: {skin_tone}", f"Recommended Mask Style: {mask_style}", image
+
+# --- Gradio UI ---
 iface = gr.Interface(
-    fn=analyze_face_features,
-    inputs=gr.Image(label="Upload or Capture a Face Image"),
-    outputs=gr.Image(label="Face Mesh + Features")
+    fn=analyze_face,
+    inputs=gr.Image(label="Capture Your Face", type="numpy"),
+    outputs=[
+        gr.Textbox(label="Detected Features"),
+        gr.Textbox(label="Recommended Mask Style"),
+        gr.Image(label="Your Photo")
+    ],
+    title="Party Mask Recommender 🎭",
+    description="Click a photo and get a party mask recommendation based on your face shape and skin tone!"
 )
 
 iface.launch()