Create app.py

app.py

@@ -0,0 +1,80 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from scipy.spatial.distance import cosine
+import cv2
+import os
+
+RECOGNITION_THRESHOLD = 0.3
+
+# Load the embedding model
+embedding_model = tf.keras.models.load_model('full_mode2.pth')
+
+# Database to store embeddings and user IDs
+user_embeddings = {}
+
+# Preprocess the image
+def preprocess_image(image):
+    image = cv2.resize(image, (375, 375))  # Resize image
+    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[user_id] = embedding
+        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)
+        min_distance = float('inf')
+        recognized_user_id = "Unknown"
+        
+        for user_id, embedding in user_embeddings.items():
+            distance = cosine(new_embedding, embedding)
+            print(f"Distance for {user_id}: {distance}")  # Debug: Print distances
+            if distance < min_distance:
+                min_distance = distance
+                recognized_user_id = user_id
+        
+        print(f"Min distance: {min_distance}")  # Debug: Print minimum distance
+        if min_distance > RECOGNITION_THRESHOLD:
+            return "User not recognized."
+        else:
+            return f"Recognized User: {recognized_user_id}"
+    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()