Update app.py

app.py

@@ -349,14 +349,14 @@ def completepreprocess(pdffile):
   return im2,myDataFrame
 
 
-title = "Interactive demo: Manifesto Information Extraction model"
-description = "Manifesto Information Extraction - We use Microsoft’s LayoutLMv3 trained on Manifesto Dataset through csv's to predict the labels.  To use it, simply upload a PDF or use the example PDF below and click ‘Submit’. Results will show up in a few seconds. If you want to make the output bigger, right-click on it and select ‘Open image in new tab’.Train =63 ,Test =15"
+title = "Interactive demo: Music Information Extraction model"
+description = "Music Information Extraction - We used Microsoft’s LayoutLMv3 trained on Our Music Dataset through csv's to predict the labels.  To use it, simply upload a PDF or use the example PDF below and click ‘Submit’. Results will show up in a few seconds. If you want to make the output bigger, right-click on it and select ‘Open image in new tab’.Train =16 ,Test =7"
 
 css = """.output_image, .input_image {height: 600px !important}"""
 #examples = [["461BHH69.PDF"],["AP-481-RF.PDF"],["DP-095-ML.PDF"],["DQ-231-LL.PDF"],["FK-941-ET.PDF"], ["FL-078-NH.PDF"]
 #              ,["14ZZ69.PDF"],["74BCA69.PDF"],["254BEG69.PDF"],["761BJQ69.PDF"],["AB-486-EH.PDF"],["AZ-211-ZA.PDF"], ["CY-073-YV.PDF"]]
 # ["744BJQ69.PDF"], ['tarros_2.jpg'],
-examples = [['3pages_messina.pdf'], ['messina2.jpg'], ['arkas1.jpg'], ['brointermed1.jpg'], ['brointermed2.pdf'], ['tarros_1.jpg'],  ['tarros_3.jpg'], ['tarros_4.jpg']]
+examples = [['test1.jpg'], ['doc1.pdf'], ['doc1.2.pdf']]
 
 iface = gr.Interface(fn=completepreprocess,
                      #inputs=gr.inputs.Image(type="pil",optional=True,label="upload file"),