Create app.py

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+import requests
+from PIL import Image
+
+processor = TrOCRProcessor.from_pretrained("paran3xus/typress_ocr")
+model = VisionEncoderDecoderModel.from_pretrained('paran3xus/typress_ocr')
+
+# load image examples
+urls = ["https://huggingface.co/paran3xus/typress_ocr_space/raw/main/test_img/1.png", "https://huggingface.co/paran3xus/typress_ocr_space/raw/main/test_img/2.png", "https://huggingface.co/paran3xus/typress_ocr_space/raw/main/test_img/3.png"]
+for idx, url in enumerate(urls):
+  image = Image.open(requests.get(url, stream=True).raw)
+  image.save(f"image_{idx}.png")
+
+def process_image(image):
+    # prepare image
+    pixel_values = processor(image, 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: Typress OCR"
+description = "Demo for Typress OCR, an TrOCR model for Typst Mathematical Expressions Recognition. To use it, simply upload a (single-text line) image or use one of the example images below and click 'submit'. Results will show up in a few seconds."
+article = "<p style='text-align: center'><a href='https://github.com/ParaN3xus/typress'>Github Repo</a></p>"
+examples =[["image_0.png"], ["image_1.png"], ["image_2.png"]]
+
+iface = gr.Interface(fn=process_image, 
+                     inputs=gr.Image(type="pil"), 
+                     outputs=gr.Textbox(),
+                     title=title,
+                     description=description,
+                     article=article,
+                     examples=examples)
+iface.launch(debug=True)