Update app.py

app.py

@@ -1,104 +1,23 @@
-import gradio as gr
 import tensorflow as tf
-import numpy as np
-import cv2
-import os
-from scipy.spatial.distance import cosine
 from tensorflow.keras.models import load_model
-from tensorflow.keras import layers, Model
 
-def create_embedding_model():
-    # Load the model architecture
-    facenet_model = load_model('facenet_keras.h5', compile=False)
-
-    # Load the weights from a separate file
-    facenet_model.load_weights('facenet_keras_weights.h5')
-
-    # Define the embedding model using FaceNet
-    embedding_model = Model(inputs=facenet_model.input, 
-                            outputs=facenet_model.layers[-2].output, 
-                            name="Embedding")
-    
-    # Here, you don't need to set layers as trainable since it's already done in your training script
-    return embedding_model
-
-# Load the embedding model
-embedding_model = create_embedding_model()
-embedding_model.load_weights('facenet_siamese_embedding.h5')
-
-# Database to store embeddings and user IDs
-user_embeddings = []
-user_ids = []
-
-# Threshold 
-RECOGNITION_THRESHOLD = 0.1 # Adjust as needed
-
-# Preprocess the image
-def preprocess_image(image):
-    image = cv2.resize(image, (160, 160))  # Resize image to match FaceNet input size
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # Convert to RGB (OpenCV loads images in BGR)
-    image = tf.convert_to_tensor(image)  # Convert to TensorFlow tensor
-    image = tf.image.convert_image_dtype(image, tf.float32)  # Normalize pixel values
-    return np.expand_dims(image, axis=0)  # Add batch dimension
-
-# 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):
-    try:
-        embedding = generate_embedding(image)
-        user_embeddings.append(embedding)
-        user_ids.append(user_id)
-        return f"User {user_id} registered successfully."
-    except Exception as e:
-        return f"Error during registration: {str(e)}"
-
-# Recognize user 
-def recognize_user(image):
-    try:
-        new_embedding = generate_embedding(image)
-        closest_user_id = None
-        closest_distance = float('inf')
-
-        for user_id, embedding in zip(user_ids, user_embeddings):
-            distance = cosine(new_embedding, embedding)
-            print(f"Distance for {user_id}: {distance}")  # Debug: Print distances for each user
-            if distance < closest_distance:
-                closest_distance = distance
-                closest_user_id = user_id
-
-        print(f"Min distance: {closest_distance}")  # Debug: Print minimum distance
-
-        if closest_distance <= RECOGNITION_THRESHOLD:
-            return f"Recognized User: {closest_user_id}"
-        else:
-            return f"User not recognized. Closest Distance: {closest_distance}"
-    except Exception as e:
-        return f"Error during recognition: {str(e)}"
-
-
-def main():
-    with gr.Blocks() as demo:
-        gr.Markdown("Facial Recognition System")
-        with gr.Tab("Register"):
-            with gr.Row():
-                img_register = gr.Image()
-                user_id = gr.Textbox(label="User ID")
-                register_button = gr.Button("Register")
-            register_output = gr.Textbox()
-            register_button.click(register_user, inputs=[img_register, user_id], outputs=register_output)
-
-        with gr.Tab("Recognize"):
-            with gr.Row():
-                img_recognize = gr.Image()
-                recognize_button = gr.Button("Recognize")
-            recognize_output = gr.Textbox()
-            recognize_button.click(recognize_user, inputs=[img_recognize], outputs=recognize_output)
-
-    demo.launch(share=True)
-
-if __name__ == "__main__":
-    main()
+def preprocess_image(filename, target_shape=(160, 160)):
+    image_string = tf.io.read_file(filename)
+    image = tf.image.decode_jpeg(image_string, channels=3)
+    image = tf.image.convert_image_dtype(image, tf.float32)
+    image = tf.image.resize(image, target_shape)
+    return image
+
+embedding_model_path = 'facenet_siamese_embedding.h5'  
+embedding_model = load_model(embedding_model_path)
+
+def generate_embedding(image_path, model):
+    preprocessed_image = preprocess_image(image_path)
+    preprocessed_image = tf.expand_dims(preprocessed_image, axis=0)  # Add batch dimension
+    embedding = model(preprocessed_image)
+    return embedding
+
+# Example usage
+image_path = 'iman.jpg'  # Update with your image's path
+image_embedding = generate_embedding(image_path, embedding_model)
+print("Generated Embedding:", image_embedding.numpy())