Update app.py

app.py

@@ -1,59 +1,54 @@
-import os
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # Suppress all TensorFlow warnings
 import gradio as gr
 import numpy as np
 import tensorflow as tf
 from tensorflow import keras
+import os
+
+# Suppress oneDNN warnings (optional)
+os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
 
-# Load model (correct filename)
+# Load the model
 try:
-    siamese = keras.models.load_model("my_siamese.keras")
+    siamese = keras.models.load_model("my_siamese.keras", compile=False)
 except Exception as e:
-    raise RuntimeError(f"Model loading failed: {str(e)}")
+    print(f"Error loading model: {e}")
+    raise
 
-# Load REAL example images (replace zeros with your uploaded files)
+# Load stored images
 def load_stored_images():
     try:
-        return [
-            np.expand_dims(tf.keras.utils.load_img("mun_example.jpg").resize((160, 160)), 0), 
-            np.expand_dims(tf.keras.utils.load_img("bash_example.jpg").resize((160, 160)), 0),
-            np.expand_dims(tf.keras.utils.load_img("usa_example.jpg").resize((160, 160)), 0)
-        ]
+        stored_images = [np.zeros((256, 256, 3)) for _ in range(3)]  # Mock images
+        return np.array([np.expand_dims(img, axis=0) for img in stored_images])
     except Exception as e:
-        raise RuntimeError(f"Image loading failed: {str(e)}")
+        print(f"Error loading stored images: {e}")
+        raise
 
 stored_imgs = load_stored_images()
 
 # Inference function
 def check_membership(uploaded_image):
     try:
-        # Preprocess to 160x160
-        uploaded_image = tf.image.resize(uploaded_image, (160, 160))
-        uploaded_image = np.expand_dims(uploaded_image, axis=0)
-        
-        predictions = [
-            siamese.predict([uploaded_image, img])[0][0] 
-            for img in stored_imgs
-        ]
-        
+        uploaded_image = np.expand_dims(uploaded_image, axis=0).astype("float32")
+
+        predictions = [siamese.predict([uploaded_image, img])[0][0] for img in stored_imgs]
+
         if predictions[0] < 0.5:
-            return "Welcome Munzali!"
+            return "You are welcome Munzali"
         elif predictions[1] < 0.5:
-            return "Welcome Ahmad!"
+            return "You are welcome Ahmad"
         elif predictions[2] < 0.5:
-            return "Welcome Usama!"
+            return "You are welcome Usama"
         else:
-            return "Not a member."
+            return "You are not a member"
     except Exception as e:
-        return f"Error: {str(e)}"
+        return f"Error: {e}"
 
-# Launch Gradio
+# Gradio interface
 iface = gr.Interface(
     fn=check_membership,
-    inputs=gr.Image(shape=(160, 160)),
+    inputs=gr.Image(shape=(256, 256), image_mode="RGB"),
     outputs="text",
-    title="Member Verification",
-    examples=["mun_example.jpg", "bash_example.jpg", "usa_example.jpg"]
+    title="Siamese Network Membership Check"
 )
 
-iface.launch()
+iface.launch()