Update app.py

app.py

@@ -1,42 +1,53 @@
 import gradio as gr
-import face_recognition
+import cv2
+import mediapipe as mp
 import numpy as np
 from PIL import Image, ImageDraw
 
-def recognize_faces(image):
-    # Convert uploaded image to RGB
-    image_rgb = np.array(image.convert("RGB"))
+# Initialize MediaPipe Face Detection
+mp_face_detection = mp.solutions.face_detection
+mp_drawing = mp.solutions.drawing_utils
 
-    # Detect face locations and encodings
-    face_locations = face_recognition.face_locations(image_rgb)
-    face_encodings = face_recognition.face_encodings(image_rgb, face_locations)
+def detect_faces(image):
+    # Convert to OpenCV format
+    img = np.array(image.convert("RGB"))
+    img_rgb = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
 
-    # Draw bounding boxes
-    pil_image = Image.fromarray(image_rgb)
-    draw = ImageDraw.Draw(pil_image)
+    # Create a face detection object
+    with mp_face_detection.FaceDetection(model_selection=0, min_detection_confidence=0.5) as face_detection:
+        results = face_detection.process(img)
 
-    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
-        name = "Unknown"  # Placeholder — can later be replaced with real matching logic
+        # Draw results
+        if results.detections:
+            for detection in results.detections:
+                bboxC = detection.location_data.relative_bounding_box
+                h, w, _ = img.shape
+                x, y, width, height = (
+                    int(bboxC.xmin * w),
+                    int(bboxC.ymin * h),
+                    int(bboxC.width * w),
+                    int(bboxC.height * h),
+                )
 
-        # Draw box
-        draw.rectangle(((left, top), (right, bottom)), outline=(0, 255, 0), width=3)
+                # Draw bounding box
+                cv2.rectangle(img_rgb, (x, y), (x + width, y + height), (0, 255, 0), 3)
 
-        # Label below the face
-        text_width, text_height = draw.textsize(name)
-        draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=(0, 255, 0))
-        draw.text((left + 6, bottom - text_height - 5), name, fill=(0, 0, 0))
+                # Add label "Unknown"
+                cv2.rectangle(img_rgb, (x, y - 25), (x + 100, y), (0, 255, 0), -1)
+                cv2.putText(img_rgb, "Unknown", (x + 5, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 0), 2)
 
-    del draw
-    return pil_image
+    # Convert back to PIL for Gradio
+    result_image = Image.fromarray(cv2.cvtColor(img_rgb, cv2.COLOR_BGR2RGB))
+    return result_image
 
 
 # Gradio Interface
 demo = gr.Interface(
-    fn=recognize_faces,
+    fn=detect_faces,
     inputs=gr.Image(type="pil", label="Upload an image"),
     outputs=gr.Image(label="Detected Faces"),
-    title="Face Recognition (Future-Ready)",
-    description="Uploads an image, detects faces, and labels them as Unknown. Can be extended for identity recognition later.",
+    title="Simple Face Detection (Lightweight)",
+    description="Detects faces using MediaPipe and labels them as 'Unknown'. Future-ready for adding identity recognition.",
 )
 
 if __name__ == "__main__":