Delete old_app.py

old_app.py

@@ -1,126 +0,0 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-from scipy.spatial.distance import cosine
-import cv2
-import os
-
-from tensorflow.keras.applications import resnet
-from tensorflow.keras import layers, Model
-
-def create_embedding_model():
-    base_cnn = resnet.ResNet50(weights="imagenet", input_shape=(200, 200, 3), include_top=False)
-
-    flatten = layers.Flatten()(base_cnn.output)
-    dense1 = layers.Dense(512, activation="relu")(flatten)
-    dense1 = layers.BatchNormalization()(dense1)
-    dense2 = layers.Dense(256, activation="relu")(dense1)
-    dense2 = layers.BatchNormalization()(dense2)
-    output = layers.Dense(256)(dense2)
-
-    embedding_model = Model(base_cnn.input, output, name="Embedding")
-
-    trainable = False
-    for layer in base_cnn.layers:
-        if layer.name == "conv5_block1_out":
-            trainable = True
-        layer.trainable = trainable
-
-    return embedding_model
-
-# K-mean Clustering
-from sklearn.cluster import KMeans
-import matplotlib.pyplot as plt
-
-# Threshold 
-RECOGNITION_THRESHOLD = 0.1  # Adjust as needed
-n_clusters = 5  # You can adjust this based on your data
-kmeans = KMeans(n_clusters=n_clusters)
-
-# Load the embedding model
-# embedding_model = tf.keras.models.load_model('base_128.h5')
-embedding_model = create_embedding_model()
-embedding_model.load_weights('base_128.h5')
-
-# Database to store embeddings and user IDs
-user_embeddings = []
-user_ids = []
-
-# Preprocess the image
-def preprocess_image(image):
-    image = cv2.resize(image, (200, 200))  # Resize image to 200x200
-    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):
-    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)
-        
-        if len(user_embeddings) < n_clusters:
-            # Handle the case where there are not enough users for K-means
-            # For example, you could use nearest neighbor search among existing embeddings
-            # Here, I'm just returning a message for simplicity
-            return "Not enough registered users for recognition."
-
-        # Update the KMeans model
-        kmeans.fit(user_embeddings)
-        cluster_label = kmeans.predict([new_embedding])[0]
-        distances = kmeans.transform([new_embedding])[0]
-        min_distance = np.min(distances)
-
-        if min_distance > RECOGNITION_THRESHOLD:
-            return "User not recognized."
-
-        # Find the user ID(s) in the closest cluster
-        recognized_user_ids = [user_ids[i] for i, label in enumerate(kmeans.labels_) if label == cluster_label]
-        return f"Recognized User(s): {', '.join(recognized_user_ids)}"
-    except Exception as e:
-        return f"Error during recognition: {str(e)}"
-
-def plot_clusters():
-    # Assuming embeddings are 2-dimensional
-    plt.figure(figsize=(8, 6))
-    plt.scatter(*zip(*user_embeddings), c=kmeans.labels_)
-    plt.title('User Embeddings Clustered by K-Means')
-    plt.xlabel('Embedding Dimension 1')
-    plt.ylabel('Embedding Dimension 2')
-    plt.show()
-        
-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()