added all files

app.py

@@ -0,0 +1,42 @@
+import re
+import gradio as gr
+
+import torch
+from functools import partial
+from PIL import Image
+from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
+
+
+model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-docvqa-large")
+processor = Pix2StructProcessor.from_pretrained("google/pix2struct-docvqa-large")
+
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+def generate(img, questions):
+    global model, processor
+    inputs = processor(images=[img for _ in range(len(questions))], text=questions, return_tensors="pt").to(device)
+    predictions = model.generate(**inputs, max_new_tokens=256)
+    return zip(questions, processor.batch_decode(predictions, skip_special_tokens=True))
+  
+
+def process_document(image, question):
+    return generate(image, [question])
+
+
+description = "Gradio Demo for Pix2Struct, an instance of `VisionEncoderDecoderModel` fine-tuned on DocVQA (document visual question answering). To use it, simply upload your image and type a question and click 'submit', or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2111.15664' target='_blank'>Pix2Struct for DocVQA</a> | <a href='https://arxiv.org/pdf/2210.03347.pdf' target='_blank'>Paper link</a></p>"
+
+demo = gr.Interface(
+    fn=process_document,
+    inputs=["image", "text"],
+    outputs="json",
+    title="Demo: Pix2Struct for DocVQA",
+    description=description,
+    article=article,
+    enable_queue=True,
+    examples=[["example_1.png", "When is the coffee break?"], ["example_2.jpeg", "What's the population of Stoddard?"]],
+    cache_examples=False)
+
+demo.launch()

requirements.txt

@@ -0,0 +1,3 @@
+torch
+git+https://github.com/huggingface/transformers.git
+sentencepiece