Update

app.py

@@ -1,7 +1,51 @@
 import gradio as gr
+import face_alignment
+import numpy as np
+from PIL import Image
+import cv2
+import torch
+import spaces
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Initialize the FaceAlignment model
+fa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, device='cuda')
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+@spaces.GPU
+def detect_landmarks(image):
+    # Convert PIL Image to numpy array
+    img_array = np.array(image)
+    
+    # Detect landmarks
+    preds = fa.get_landmarks(img_array)
+    
+    if preds is None:
+        return image, "No face detected in the image."
+    
+    # Draw landmarks on the image
+    for facial_landmarks in preds:
+        for (x, y) in facial_landmarks:
+            cv2.circle(img_array, (int(x), int(y)), 2, (0, 255, 0), -1)
+    
+    return Image.fromarray(img_array), f"Detected {len(preds)} face(s) in the image."
+
+# Create Gradio Blocks
+with gr.Blocks() as demo:
+    gr.Markdown("# Face Alignment Demo")
+    gr.Markdown("Upload an image to detect facial landmarks.")
+    
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(type="pil", label="Input Image")
+            submit_btn = gr.Button("Detect Landmarks")
+        
+        with gr.Column():
+            output_image = gr.Image(type="pil", label="Output Image")
+            output_text = gr.Textbox(label="Detection Result")
+    
+    submit_btn.click(
+        fn=detect_landmarks,
+        inputs=input_image,
+        outputs=[output_image, output_text]
+    )
+
+# Launch the interface
+demo.launch()

requirements.txt

@@ -0,0 +1,6 @@
+     gradio
+     face_alignment
+     numpy
+     Pillow
+     opencv-python-headless
+     torch