Create app.py

app.py

@@ -0,0 +1,31 @@
+import cv2
+from tensorflow.keras.models import load_modelimport gradio as gr
+import tensorflow as tf
+import cv2
+import numpy as np
+from tensorflow.keras.models import load_model
+
+# Load the pre-trained model
+new_model = load_model('imageclassifier.h5')
+
+def classify_image(img):
+    # Resize the image
+    resize = tf.image.resize(img, (256, 256))
+    
+    # Preprocess the image and make prediction
+    yhat = new_model.predict(np.expand_dims(resize / 255, 0))
+
+    # Return the prediction result
+    return "Real" if yhat > 0.5 else "Fake"
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(),
+    outputs="text",
+    live=True,
+    capture_session=True  # Needed for TensorFlow/Keras models
+)
+
+# Launch the Gradio interface
+iface.launch()