Create app.py

app.py

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+import gradio as gr
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+import requests
+from PIL import Image
+import numpy as np
+import cv2
+processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
+model = VisionEncoderDecoderModel.from_pretrained("aico/TrOCR-MNIST")
+
+
+def process_image(image):
+
+    #print(np.shape(image))
+    #print(image)
+    #rint(image.astype('uint8'))
+    #cv2.imwrite("image.png",image.astype('uint8'),(28, 28))
+    img = Image.fromarray(image.astype('uint8')).convert("RGB")
+    #img = Image.open("image.png").convert("RGB")
+    print(img)
+    # prepare image
+    pixel_values = processor(img, return_tensors="pt").pixel_values
+
+    # generate (no beam search)
+    generated_ids = model.generate(pixel_values)
+
+    # decode
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+    return generated_text
+
+title = "Interactive demo: Single Digits MNIST"
+description = "Aico - University Utrecht"
+iface = gr.Interface(fn=process_image, 
+                     inputs="sketchpad", 
+                     outputs="label",
+                     title = title,
+                     description = description)
+iface.launch(debug=True)