Made the description more fun

app.py

@@ -1,24 +1,13 @@
 from transformers import pipeline
 import gradio as gr
-# import io
-# import IPython.display
-# from PIL import Image
-# import base64
 
 get_completion = pipeline("image-to-text",model="nlpconnect/vit-gpt2-image-captioning")
 
-def summarize(input):
-    output = get_completion(input)
-    return output[0]['generated_text']
-
-# def image_to_base64_str(pil_image):
-#     byte_arr = io.BytesIO()
-#     pil_image.save(byte_arr, format='PNG')
-#     byte_arr = byte_arr.getvalue()
-#     return str(base64.b64encode(byte_arr).decode('utf-8'))
+# def summarize(input):
+#     output = get_completion(input)
+#     return output[0]['generated_text']
 
 def captioner(image):
-    # base64_image = image_to_base64_str(image)
     result = get_completion(image)
     return result[0]['generated_text']
 
@@ -26,8 +15,8 @@ gr.close_all()
 demo = gr.Interface(fn=captioner,
                     inputs=[gr.Image(label="Upload image", type="pil")],
                     outputs=[gr.Textbox(label="Caption")],
-                    title="Image Captioning with BLIP",
-                    description="Caption any image using the BLIP model",
+                    title="Image Captioning!",
+                    description="I'll be back... with captions!",
                     allow_flagging="never")
 
 demo.launch()