Update app.py

app.py

@@ -1,13 +1,13 @@
 from transformers import ViTFeatureExtractor, BertTokenizer, VisionEncoderDecoderModel, AutoTokenizer, AutoFeatureExtractor
 import gradio as gr
 
-title="Multilingual OCR (currently recognises: English and Chinese)"
-description="m_OCR(multilingual OCR) is a Vision-Encoder-Decoder model (based on the concept of TrOCR) which uses pre-trained facebook's vit-mae-large as the encoder and xlm-roberta-base as the decoder. \nIt has been trained on IAM, SROIE 2019, text_renderer Chinese (synthetic) and TRDG (synthetic) datasets (amounting to approx 1.4 Million samples) for English and Chinese document text-recognition."
-examples =[["demo_image/img1.png"], ["demo_image/img2.jpeg"], ["demo_image/img3.jpeg"], ["demo_image/img4.jpeg"], ["demo_image/img5.jpeg"], ["demo_image/img6.jpeg"]]
+title="Handwritten Phone Number OCR"
+description="handwritten_phone_number_OCR is a Vision-Encoder-Decoder model (based on the concept of TrOCR) which uses pre-trained facebook's vit-mae-large as the encoder and xlm-roberta-base as the decoder. \nIt has been trained on MNIST (2M synthetic images)."
+examples =[["demo_image/0313474611_lauf25.png"], ["demo_image/0466975865_nr4ywx.png"], ["demo_image/0473227403_7lnod1.png"], ["demo_image/0728880927_jr987p.png"], ["demo_image/0922853144_1o4ay5.png"]]
 
 
 
-model=VisionEncoderDecoderModel.from_pretrained("priyank-m/m_OCR")
+model=VisionEncoderDecoderModel.from_pretrained("dawars/handwritten_phone_number_OCR")
 tokenizer =  AutoTokenizer.from_pretrained("xlm-roberta-base")
 feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/vit-mae-large")