app.py

app.py

@@ -73,7 +73,7 @@ def compute_loss(input_image):
         loss += coeff * tf.reduce_sum(tf.square(activation[:, 2:-2, 2:-2, :])) / scaling
     return loss
     
- def gradient_ascent_step(img, learning_rate):
+def gradient_ascent_step(img, learning_rate):
     with tf.GradientTape() as tape:
         tape.watch(img)
         loss = compute_loss(img)
@@ -128,11 +128,11 @@ iface = gr.Interface(classify_image,image,label,
 	#outputs=[
 	#        gr.outputs.Textbox(label="Engine issue"),
        	#	gr.outputs.Textbox(label="Engine issue score")], 
-	examples=["sky.jpg"],  title="Image classification on CIFAR-100",
-	description = "Model for classifying  images from the CIFAR dataset using a vision transformer trained with small data.",
+	examples=["sky.jpg"],  title="Deep dream",
+	description = "Model for applying Deep Dream to an image.",
         article = "Author: <a href=\"https://huggingface.co/joheras\">Jónathan Heras</a>"
 #	examples = ["sample.csv"],
 )
 
 
-iface.launch()
+iface.launch()