Update app.py

app.py

@@ -1,27 +1,43 @@
 import gradio as gr
-import face_recognition
+import mediapipe as mp
 import numpy as np
 
+mp_face = mp.solutions.face_detection
+face_detection = mp_face.FaceDetection(model_selection=0, min_detection_confidence=0.5)
+
 def detect_faces(image):
-    image = np.array(image)
-    locations = face_recognition.face_locations(image)
-    encodings = face_recognition.face_encodings(image, locations)
+    image_np = np.array(image)
+    results = face_detection.process(image_np)
 
     faces = []
-    for (top, right, bottom, left), encoding in zip(locations, encodings):
-        cx = (left + right) / 2
-        cy = (top + bottom) / 2
-        faces.append({
-            "cx": cx,
-            "cy": cy,
-            "box": {
-                "top": top,
-                "right": right,
-                "bottom": bottom,
-                "left": left
-            },
-            "embedding": encoding.tolist()
-        })
+
+    if results.detections:
+        for detection in results.detections:
+            bbox = detection.location_data.relative_bounding_box
+
+            h, w, _ = image_np.shape
+
+            xmin = int(bbox.xmin * w)
+            ymin = int(bbox.ymin * h)
+            width = int(bbox.width * w)
+            height = int(bbox.height * h)
+
+            xmax = xmin + width
+            ymax = ymin + height
+
+            cx = (xmin + xmax) / 2
+            cy = (ymin + ymax) / 2
+
+            faces.append({
+                "cx": cx,
+                "cy": cy,
+                "box": {
+                    "xmin": xmin,
+                    "ymin": ymin,
+                    "xmax": xmax,
+                    "ymax": ymax
+                }
+            })
 
     return faces