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app.py

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+import gradio as gr
+import os
+import cv2
+import numpy as np
+from utils import load_model, preprocess, verify_identity
+
+model = load_model("siamesemodel.h5")
+registration_dir = "registered_users"
+
+def register_user(name, image):
+    user_path = os.path.join(registration_dir, name)
+    os.makedirs(user_path, exist_ok=True)
+    image.save(os.path.join(user_path, f"{name}_1.jpg"))
+    return f"User {name} registered successfully."
+
+def verify(image):
+    result = verify_identity(model, image, registration_dir)
+    return result
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 🧠 Siamese Face Verification")
+    with gr.Tab("Register"):
+        name = gr.Textbox(label="Enter Your Name")
+        reg_img = gr.Image(source="webcam", label="Click Your Photo", shape=(105,105))
+        reg_btn = gr.Button("Register")
+        reg_output = gr.Textbox()
+        reg_btn.click(fn=register_user, inputs=[name, reg_img], outputs=reg_output)
+
+    with gr.Tab("Verify"):
+        ver_img = gr.Image(source="webcam", label="Click Your Photo", shape=(105,105))
+        ver_btn = gr.Button("Verify")
+        ver_output = gr.Textbox()
+        ver_btn.click(fn=verify, inputs=ver_img, outputs=ver_output)
+
+demo.launch()

requirements.txt

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+gradio
+tensorflow
+opencv-python
+numpy
+Pillow

siamese_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d7ca682c8cb287f75eb7dd9b687f61e600150dde688d88034e0a20dbc2ebfca0
+size 467626624

utils.py

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+import os
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+import cv2
+
+IMG_SIZE = (105, 105)  # same size you trained on
+
+# Load the trained Siamese model
+def load_model(model_path):
+    model = tf.keras.models.load_model(model_path, custom_objects={'L1Dist': L1Dist})
+    return model
+
+# Custom L1 distance layer (used in Siamese networks)
+class L1Dist(tf.keras.layers.Layer):
+    def __init__(self, **kwargs):
+        super().__init__()
+
+    def call(self, input_embedding, validation_embedding):
+        return tf.math.abs(input_embedding - validation_embedding)
+
+# Preprocess image for model input
+def preprocess(image):
+    # Convert to numpy array if it's PIL
+    if isinstance(image, Image.Image):
+        image = np.array(image)
+
+    image = cv2.resize(image, IMG_SIZE)
+    image = image / 255.0
+    image = np.expand_dims(image, axis=0)  # (1, 105, 105, 3)
+    return image
+
+# Verify identity by comparing input image to stored validation images
+def verify_identity(model, input_img, users_folder, detection_threshold=0.5):
+    input_processed = preprocess(input_img)
+
+    highest_score = 0
+    identity = "Unknown"
+
+    for user in os.listdir(users_folder):
+        user_path = os.path.join(users_folder, user)
+        if not os.path.isdir(user_path):
+            continue
+
+        for img_file in os.listdir(user_path):
+            img_path = os.path.join(user_path, img_file)
+
+            try:
+                val_img = Image.open(img_path).convert('RGB')
+            except:
+                continue
+
+            val_processed = preprocess(val_img)
+
+            # Predict
+            result = model.predict([input_processed, val_processed])[0][0]
+
+            if result > highest_score:
+                highest_score = result
+                identity = user
+
+    if highest_score < detection_threshold:
+        return "User not recognized."
+    else:
+        return f"Verified: {identity} (Confidence: {highest_score:.2f})"