Create app.py

app.py

@@ -0,0 +1,62 @@
+import argparse
+import gradio as gr
+import torch
+from PIL import Image
+
+from donut import DonutModel
+
+
+def demo_process_vqa(input_img, question):
+    global pretrained_model, task_prompt, task_name
+#    input_img = Image.fromarray(input_img)
+    user_prompt = task_prompt.replace("{user_input}", question)
+    output = pretrained_model.inference(input_img, prompt=user_prompt)["predictions"][0]
+    return output
+
+
+def demo_process(input_img):
+    global pretrained_model, task_prompt, task_name,security_layer
+    input_img = Image.fromarray(input_img)
+    sec = security_layer.inference(image=input_img,prompt="<s_rvlcdip>")['predictions'][0]
+    print(sec)
+    if sec['class']=="invoice":
+      output = pretrained_model.inference(image=input_img, prompt="<s_cord-v2>")["predictions"][0]
+      return output
+    return sec
+
+task_name="cord-v2"
+if "docvqa" == task_name:
+    task_prompt = "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
+else:  # rvlcdip, cord, ...
+    task_prompt = f"<s_{task_name}>"
+
+security_layer = DonutModel.from_pretrained("naver-clova-ix/donut-base-finetuned-rvlcdip")
+
+pretrained_model = DonutModel.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")
+
+
+if torch.cuda.is_available():
+    pretrained_model.half()
+    security_layer.half()
+    device = torch.device("cuda")
+    pretrained_model.to(device)
+    security_layer.to(device)
+else:
+  pretrained_model.encoder.to(torch.bfloat16)
+  security_layer.encoder.to(torch.bfloat16)
+
+pretrained_model.eval()
+security_layer.eval()
+    
+
+demo = gr.Interface(
+    fn=demo_process_vqa if task_name == "docvqa" else demo_process,
+    inputs=["image", "text"] if task_name == "docvqa" else "image",
+    outputs="json",
+    title=f"Donut 🍩 demonstration for `{task_name}` task",
+    concurrency_limit=10,
+    description="Get invoice details if invoice"
+)
+
+demo.queue(default_concurrency_limit=2,max_size=5)
+demo.launch(debug=True,share=True, inline=False)