Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from scipy.spatial.distance import cosine
+import cv2
+import os
+
+# Load the embedding model
+embedding_model = tf.keras.models.load_model('embedding_model.h5')
+
+# Database to store embeddings and user IDs
+user_embeddings = {}
+
+# Preprocess the image
+def preprocess_image(image):
+    image = cv2.resize(image, (200, 200))  # Assuming your model expects 200x200 input
+    image = tf.keras.applications.resnet50.preprocess_input(image)
+    return np.expand_dims(image, axis=0)
+
+# Generate embedding
+def generate_embedding(image):
+    preprocessed_image = preprocess_image(image)
+    return embedding_model.predict(preprocessed_image)[0]
+
+# Register new user
+def register_user(image, user_id):
+    embedding = generate_embedding(image)
+    user_embeddings[user_id] = embedding
+    return f"User {user_id} registered successfully."
+
+# Recognize user
+def recognize_user(image):
+    new_embedding = generate_embedding(image)
+    min_distance = float('inf')
+    recognized_user_id = "Unknown"
+    
+    for user_id, embedding in user_embeddings.items():
+        distance = cosine(new_embedding, embedding)
+        if distance < min_distance:
+            min_distance = distance
+            recognized_user_id = user_id
+
+    return f"Recognized User: {recognized_user_id}"
+
+# Gradio interface for registering users
+register_interface = gr.Interface(
+    fn=register_user,
+    inputs=[gr.inputs.Image(shape=(200, 200)), gr.inputs.Textbox(label="User ID")],
+    outputs="text",
+    live=True
+)
+
+# Gradio interface for recognizing users
+recognize_interface = gr.Interface(
+    fn=recognize_user,
+    inputs=gr.inputs.Image(shape=(200, 200)),
+    outputs="text",
+    live=True
+)
+
+if __name__ == "__main__":
+    register_interface.launch(share=True)
+    recognize_interface.launch(share=True)