Update app.py

app.py

@@ -1,31 +1,76 @@
 import gradio as gr
 import cv2
 import numpy as np
+import mediapipe as mp
 import uuid
 import os
+from sklearn.cluster import KMeans
 
-def capture_image(image):
+# Create reusable face mesh
+mp_face_mesh = mp.solutions.face_mesh
+face_mesh = mp_face_mesh.FaceMesh(static_image_mode=True)
+
+# Face Shape Detection Logic (Simplified based on face landmarks)
+def detect_face_shape(landmarks, image_shape):
+    # Grab required landmark points
+    left_cheek = landmarks[234]
+    right_cheek = landmarks[454]
+    chin = landmarks[152]
+    forehead = landmarks[10]
+
+    # Calculate distances
+    width = np.linalg.norm(np.array([left_cheek.x, left_cheek.y]) - np.array([right_cheek.x, right_cheek.y]))
+    height = np.linalg.norm(np.array([chin.x, chin.y]) - np.array([forehead.x, forehead.y]))
+
+    ratio = width / height
+    if ratio > 1.3:
+        return "Round"
+    elif ratio > 1.1:
+        return "Oval"
+    else:
+        return "Long"
+
+# Skin tone using KMeans
+def get_skin_tone(image):
+    h, w, _ = image.shape
+    face_crop = image[h//4:3*h//4, w//3:2*w//3]  # middle region
+
+    pixels = face_crop.reshape(-1, 3)
+    kmeans = KMeans(n_clusters=3, random_state=0).fit(pixels)
+    dominant = kmeans.cluster_centers_.astype(int)[0]
+    return tuple(dominant)
+
+def analyze_face(image):
     if image is None:
-        return None, "No image captured"
+        return None, "No image", "No skin tone"
     
-    # Create temp directory if not exists
-    os.makedirs("temp_images", exist_ok=True)
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    results = face_mesh.process(image_rgb)
 
-    # Generate unique filename
-    filename = f"temp_images/{uuid.uuid4().hex}.png"
-    cv2.imwrite(filename, cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
-    
-    return image, f"Image saved as {filename}"
+    if not results.multi_face_landmarks:
+        return image, "No face detected", "N/A"
+
+    landmarks = results.multi_face_landmarks[0].landmark
+    shape = detect_face_shape(landmarks, image.shape)
+
+    tone = get_skin_tone(image)
+    tone_str = f"RGB: {tone}"
+
+    return image, shape, tone_str
 
-# Gradio Interface
+# Gradio interface
 demo = gr.Interface(
-    fn=capture_image,
-    inputs=gr.Image(label="Capture from Webcam", type="numpy", image_mode="RGB"),
-    outputs=[gr.Image(label="Captured Image"), gr.Textbox(label="Status")],
-    live=True,
+    fn=analyze_face,
+    inputs=gr.Image(type="numpy", image_mode="BGR", label="Upload or Capture Face"),
+    outputs=[
+        gr.Image(label="Original Image"),
+        gr.Textbox(label="Detected Face Shape"),
+        gr.Textbox(label="Dominant Skin Tone")
+    ],
+    live=False,
     allow_flagging="never",
-    title="Step 1: Capture Webcam Image"
+    title="Step 2: Face Shape and Skin Tone Analyzer"
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()