Merge remote-tracking branch 'refs/remotes/origin/main'

README.md

@@ -33,7 +33,7 @@ https://huggingface.co/spaces/Digital-Detectives/AI-vs-Real-Image-Detection
 
 ## UI
 
-https://github.com/user-attachments/assets/7d6a3c38-f7dc-4e74-8f0d-6d31653e7670
+<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/671fc13a4b2e5a664a9d3137/ZSo6v5V3QK-ip-PPHW_be.mp4"></video>
 
 ## Project Outline
 The goal of this project is to develop a deep learning model that can accurately distinguish between real images and AI-generated images. We will collect datasets of real images and fake images. The data will be preprocessed, normalized, and augmented to enhance training. Using TensorFlow and Keras, we will design a Convolutional Neural Network (CNN) for classification, and validating performance through a confusion matrix. Finally, the project will include documentation of the process, findings, and suggestions for future improvements.

app.py

@@ -74,28 +74,28 @@ def run_cnn(img_arr):
 
 # efficientnet model
 def run_effNet(img_arr):
-    try:
-        resolver = tf.distribute.cluster_resolver.TPUClusterResolver()
-        tf.config.experimental_connect_to_cluster(resolver)
-        tf.tpu.experimental.initialize_tpu_system(resolver)
-        strategy = tf.distribute.TPUStrategy(resolver)
-    except ValueError:
-        strategy = tf.distribute.get_strategy()  
-    with strategy.scope():
-            prediction = eff_net_model.predict(img_arr)
+    # try:
+    #     resolver = tf.distribute.cluster_resolver.TPUClusterResolver()
+    #     tf.config.experimental_connect_to_cluster(resolver)
+    #     tf.tpu.experimental.initialize_tpu_system(resolver)
+    #     strategy = tf.distribute.TPUStrategy(resolver)
+    # except ValueError:
+    #     strategy = tf.distribute.get_strategy()  
+    # with strategy.scope():
+    prediction = eff_net_model.predict(img_arr)
     return prediction
  
 # efficientnet art model
 def run_effNet_Art(img_arr):
-    try:
-        resolver = tf.distribute.cluster_resolver.TPUClusterResolver()
-        tf.config.experimental_connect_to_cluster(resolver)
-        tf.tpu.experimental.initialize_tpu_system(resolver)
-        strategy = tf.distribute.TPUStrategy(resolver)
-    except ValueError:
-        strategy = tf.distribute.get_strategy()  
-    with strategy.scope():
-            prediction = eff_net_art_model.predict(img_arr)
+    # try:
+    #     resolver = tf.distribute.cluster_resolver.TPUClusterResolver()
+    #     tf.config.experimental_connect_to_cluster(resolver)
+    #     tf.tpu.experimental.initialize_tpu_system(resolver)
+    #     strategy = tf.distribute.TPUStrategy(resolver)
+    # except ValueError:
+    #     strategy = tf.distribute.get_strategy()  
+    # with strategy.scope():
+    prediction = eff_net_art_model.predict(img_arr)
     return prediction
 
 # preprocess images for efficient net