Update app.py

app.py

@@ -348,8 +348,8 @@ def generate_caption(image):
     # Read the image from the disk
     sample_img = decode_and_resize(sample_img) 
     img = sample_img.numpy().clip(0, 255).astype(np.uint8)
-    plt.imshow(img)
-    plt.show()
+    #plt.imshow(img)
+    #plt.show()  
 
     # Pass the image to the CNN
     img = tf.expand_dims(sample_img, 0)
@@ -374,7 +374,7 @@ def generate_caption(image):
 
     decoded_caption = decoded_caption.replace("<start> ", "")
     decoded_caption = decoded_caption.replace(" <end>", "").strip()
-    print("Predicted Caption: ", decoded_caption)
+    print(decoded_caption)
 
 inputs = [
     gr.inputs.Image( label="Original Image")
@@ -385,7 +385,7 @@ outputs = [
 ]
 
 title = "Image Captioning using CNN and a transformer + "
-description = "Implementing an image cpationing model using a pretrained CNN model of Efficient Net and transformer to generate Image Caption for the uploaded image. Flickr8K Dataset was used for training." 
+description = "Implementing an image captioning model using a pretrained CNN model of Efficient Net and transformer to generate Image Caption for the uploaded image. Flickr8K Dataset was used for training." 
 article = " "
 examples = [["pic 1.jpg"], ["pic 2.jpg"], ["pic 3.jpg"], ["pic 4.jpg"]]