Update app.py

app.py

@@ -1,61 +1,61 @@
-import gradio as gr
-import numpy as np
-import tensorflow as tf
-from PIL import Image
-import cv2
-
-# Load TensorFlow Lite model
-interpreter = tf.lite.Interpreter(model_path="facenet.tflite")
-interpreter.allocate_tensors()
-
-# Get input and output details
-input_details = interpreter.get_input_details()
-output_details = interpreter.get_output_details()
-
-def preprocess_image(image):
-    """
-    Preprocess the input image for the FaceNet model.
-    """
-    image = Image.fromarray(image)
-    image = image.resize((160, 160))  # Resize to the model's input size
-    image_array = np.asarray(image).astype(np.float32)
-    image_array = (image_array - 127.5) / 127.5  # Normalize to [-1, 1]
-    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
-    return image_array
-
-def create_face_embedding(image):
-    """
-    Generate a face embedding for the given image.
-    """
-    processed_image = preprocess_image(image)
-
-    # Run the model
-    interpreter.set_tensor(input_details[0]['index'], processed_image)
-    interpreter.invoke()
-
-    # Extract the embedding
-    embedding = interpreter.get_tensor(output_details[0]['index'])
-    return embedding.flatten()
-
-# Gradio interface
-def generate_embedding(image):
-    """
-    Gradio function to process the image and return full embeddings.
-    """
-    try:
-        embedding = create_face_embedding(image)
-        return embedding.tolist()  # Convert numpy array to list
-    except Exception as e:
-        return f"Error: {e}"
-
-# Gradio interface setup
-iface = gr.Interface(
-    fn=generate_embedding,
-    inputs=gr.Image(type="numpy", label="Upload Face Image"),
-    outputs=gr.JSON(label="Face Embedding"),
-    title="Face Embedding Generator",
-    description="Upload a face image to generate a 512-dimensional embedding using the FaceNet model."
-)
-
-if __name__ == "__main__":
-    iface.launch()
+import gradio as gr
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+import cv2
+
+# Load TensorFlow Lite model
+interpreter = tf.lite.Interpreter(model_path="facenet.tflite")
+interpreter.allocate_tensors()
+
+# Get input and output details
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+def preprocess_image(image):
+    """
+    Preprocess the input image for the FaceNet model.
+    """
+    image = Image.fromarray(image)
+    image = image.resize((160, 160))  # Resize to the model's input size
+    image_array = np.asarray(image).astype(np.float32)
+    image_array = (image_array - 127.5) / 127.5  # Normalize to [-1, 1]
+    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
+    return image_array
+
+def create_face_embedding(image):
+    """
+    Generate a face embedding for the given image.
+    """
+    processed_image = preprocess_image(image)
+
+    # Run the model
+    interpreter.set_tensor(input_details[0]['index'], processed_image)
+    interpreter.invoke()
+
+    # Extract the embedding
+    embedding = interpreter.get_tensor(output_details[0]['index'])
+    return embedding.flatten()
+
+# Gradio interface
+def generate_embedding(image):
+    """
+    Gradio function to process the image and return full embeddings.
+    """
+    try:
+        embedding = create_face_embedding(image)
+        return embedding.tolist()  # Convert numpy array to list
+    except Exception as e:
+        return f"Error: {e}"
+
+# Gradio interface setup
+iface = gr.Interface(
+    fn=generate_embedding,
+    inputs=gr.Image(type="numpy", label="Upload Face Image"),
+    outputs=gr.JSON(label="Face Embedding"),
+    title="Face Embedding Generator",
+    description="Upload a face image to generate a 512-dimensional embedding using the FaceNet model."
+)
+
+if __name__ == "__main__":
+    iface.launch(share=True)