Create app.py

app.py

@@ -0,0 +1,66 @@
+import argparse
+import gradio as gr
+import torch
+from PIL import Image
+import re
+from transformers import DonutProcessor, VisionEncoderDecoderModel
+from donut import DonutModel
+import json
+
+
+def demo_process(input_img, question=None):
+    global processor, model
+
+    input_img = Image.fromarray(input_img)
+    pixel_values = processor(input_img, return_tensors="pt").pixel_values.to(device)
+
+    if question:
+        task_prompt = f"<s_{question}>"
+        decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids.to(device)
+    else:
+        task_prompt = "<s_cord-v2>"
+        decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids.to(device)
+
+    with torch.no_grad():
+        outputs = model.generate(
+          pixel_values,
+          decoder_input_ids=decoder_input_ids,
+          max_length=1024,  # เปลี่ยนตามความต้องการ
+          early_stopping=True,
+          pad_token_id=processor.tokenizer.pad_token_id,
+          eos_token_id=processor.tokenizer.eos_token_id,
+          use_cache=True,
+          num_beams=1,
+          bad_words_ids=[[processor.tokenizer.unk_token_id]],
+          return_dict_in_generate=True,
+      )
+
+    seq = processor.batch_decode(outputs.sequences)[0]
+    seq = seq.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
+    seq = re.sub(r"<.*?>", "", seq, count=1).strip()
+    seq = processor.token2json(seq)
+    return seq
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--task", type=str, default="cord-v2")  # Add argument for task
+parser.add_argument("--pretrained_path", type=str, default="suthawadee/donut-demo_new")
+args, left_argv = parser.parse_known_args()
+
+processor = DonutProcessor.from_pretrained(args.pretrained_path)
+model = VisionEncoderDecoderModel.from_pretrained(args.pretrained_path)
+device = "cpu" if not torch.cuda.is_available() else "cuda"
+model.to(device)
+model.eval()
+
+inputs = ["image", "text"] if args.task == "docvqa" else "image"
+outputs = "json"
+
+demo = gr.Interface(
+    fn=demo_process,
+    inputs=inputs,
+    outputs=outputs,
+    title="🇹🇭🧾ThaiReceipt",
+    description="Upload an image."
+)
+demo.launch(debug=True)