Create app.py

app.py

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+import mediapipe as mp
+import gradio as gr
+import cv2
+import torch
+
+
+# Images
+torch.hub.download_url_to_file('https://artbreeder.b-cdn.net/imgs/c789e54661bfb432c5522a36553f.jpeg', 'face1.jpg')
+torch.hub.download_url_to_file('https://artbreeder.b-cdn.net/imgs/c86622e8cb58d490e35b01cb9996.jpeg', 'face2.jpg')
+
+mp_face_mesh = mp.solutions.face_mesh
+
+# Prepare DrawingSpec for drawing the face landmarks later.
+mp_drawing = mp.solutions.drawing_utils 
+drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
+
+# Run MediaPipe Face Mesh.
+
+def inference(image):
+    with mp_face_mesh.FaceMesh(
+        static_image_mode=True,
+        max_num_faces=2,
+        min_detection_confidence=0.5) as face_mesh:
+        # Convert the BGR image to RGB and process it with MediaPipe Face Mesh.
+        results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+        annotated_image = image.copy()
+        for face_landmarks in results.multi_face_landmarks:
+            mp_drawing.draw_landmarks(
+                image=annotated_image,
+                landmark_list=face_landmarks,
+                connections=mp_face_mesh.FACE_CONNECTIONS,
+                landmark_drawing_spec=drawing_spec,
+                connection_drawing_spec=drawing_spec)
+            return annotated_image
+
+title = "Face Mesh"
+description = "demo for Face Mesh. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1907.06724'>Real-time Facial Surface Geometry from Monocular Video on Mobile GPUs</a> | <a href='https://github.com/google/mediapipe'>Github Repo</a></p>"
+
+gr.Interface(
+    inference, 
+    [gr.inputs.Image(label="Input")], 
+    gr.outputs.Image(type="pil", label="Output"),
+    title=title,
+    description=description,
+    article=article, 
+    examples=[
+              ["face1.jpg"],
+              ["face2.jpg"]
+    ]).launch(debug=True)