adjusted description; adjusted examples

app.py

@@ -1,3 +1,4 @@
+from errno import EHOSTDOWN
 import gradio
 import cv2
 from sklearn.naive_bayes import BernoulliNB 
@@ -7,7 +8,17 @@ import numpy as np
 # multiclass_model =  pickle.load(open('models/MulticlassModel_200x200', 'rb'))
 ensemble_model =  pickle.load(open('models/EnsembleModels_200x200', 'rb'))
 
-examples = ["images/Conso.jpg", "images/Incom.jpg"]
+examples = ["images/Conso.jpg", "images/Incom.jpg",'DBF.jpg'
+'images/DLC.jpg',
+'images/EHD.jpg',
+'images/IDR.jpg',
+'images/PPD.jpg',
+'images/PSLF.jpg',
+'images/REA.jpg',
+'images/SCD.jpg',
+'images/TDD.jpg',
+'images/TLF.jpg',
+'images/UD.jpg']
 
 def preprocess(img):
     img = cv2.resize(img, (200,200))
@@ -23,7 +34,7 @@ def predict(img):
     "Cons": 0, 
     "Publ": 4, 
     "Econ": 3,
-    "Reaf": 5}
+    "Rea": 5}
 
     proba = np.zeros((6))
     for key in categories.keys():
@@ -38,7 +49,7 @@ def generate_results(proba):
         "IDR", 
         "EHD",
         "PLSF", 
-        "REAF",
+        "REA",
         "UNKNOWN"] 
 
     scores = [0,0,0,0,0,0,0]
@@ -63,7 +74,7 @@ demo = gradio.Interface(
     outputs=gradio.Label(),
     title='Document Classification', 
     description='Loan Document Classification Using A Naive Bayes Classifier Ensemble',
-    article='This demo was built as part of demo for a student project.\n\n\n#BuiltAtMetis',    
+    article='The purpose of this demo was to provide a simple baseline for the classification of document images. View the complete write up here https://github.com/PatrickTyBrown/document_classification/blob/main/project_writeup.pdf\n\n\nLinkedin: https://www.linkedin.com/in/patrick-ty-brown/\nGithub: https://github.com/PatrickTyBrown/document_classification\nPortfolio: https://sites.google.com/view/patrick-brown/home',    
     examples=examples)  
 
 demo.launch()