Add app.py

app.py

@@ -0,0 +1,40 @@
+from huggingface_hub import hf_hub_download
+import re
+from PIL import Image
+
+import gradio as gr
+
+from transformers import NougatProcessor, VisionEncoderDecoderModel
+from datasets import load_dataset
+import torch
+
+model_checkpoint = "facebook/nougat-base"
+processor = NougatProcessor.from_pretrained(model_checkpoint)
+model = VisionEncoderDecoderModel.from_pretrained(model_checkpoint)
+
+# Use GPU if possible
+device = "cuda" if torch.cuda_is_available() else "cpu"
+model.to(device)
+
+# prepare PDF image for the model
+def predict(img):
+    pixel_values = processor(img, return_tensors="pt").pixel_values
+
+    outputs = model.generate(
+            pixel_values.to(device)
+            min_length=1
+            max_new_tokens=30,
+            bad_words_ids=[[processor.tokenizer.unk_token_id]],
+    )
+
+    sequence = processor.batch_decode(outputs, skip_special_tokens=True)[0]
+    sequence = processor.post_process_generation(sequence, fix_markdown=False)
+    return sequence
+
+image = gr.Image()
+text = ["text"]
+examples = ['page_10.jpg']
+
+intf = gr.Interface(fn=predict, inputs=image, outpus=text, examples=examples)
+intf.launch(inline=False)
+