attempted example transformer test

appTr.py

@@ -0,0 +1,27 @@
+import gradio as gr
+from PIL import Image
+#import tensorflow as tf
+#from tensorflow.keras.models import load_model
+#import numpy as np
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+pipe = pipeline("image-to-text", model="Flova/omr_transformer")
+
+# # Load model directly
+# from transformers import AutoTokenizer, AutoModel
+
+# tokenizer = AutoTokenizer.from_pretrained("Flova/omr_transformer")
+# model = AutoModel.from_pretrained("Flova/omr_transformer")
+
+# Using Flova/omr_transformer
+
+def notation_2_note(input_img):
+    prediction = pipe(Image.fromarray(input_img))
+    #print(type(prediction))
+    output_text = prediction[0]['generated_text']
+    return output_text
+
+demo = gr.Interface(notation_2_note, gr.Image(), "text")
+demo.launch()