Update sample code

app.py

@@ -1,7 +1,62 @@
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+import torch
+import re
+from transformers import DonutProcessor, VisionEncoderDecoderModel
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+def load_and_preprocess_image(image, processor):
+    """
+    Load an image and preprocess it for the model.
+    """
+    pixel_values = processor(image, return_tensors="pt").pixel_values
+    return pixel_values
+
+def generate_text_from_image(model, image, processor, device):
+    """
+    Generate text from an image using the trained model.
+    """
+    # Load and preprocess the image
+    pixel_values = load_and_preprocess_image(image, processor)
+    pixel_values = pixel_values.to(device)
+
+    # Generate output using model
+    model.eval()
+    with torch.no_grad():
+        task_prompt = "<s_receipt>" # <s_cord-v2> for v1
+        decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
+        decoder_input_ids = decoder_input_ids.to(device)
+        generated_outputs = model.generate(
+            pixel_values,
+            decoder_input_ids=decoder_input_ids,
+            max_length=model.decoder.config.max_position_embeddings, 
+            pad_token_id=processor.tokenizer.pad_token_id,
+            eos_token_id=processor.tokenizer.eos_token_id,
+            early_stopping=True,
+            bad_words_ids=[[processor.tokenizer.unk_token_id]],
+            return_dict_in_generate=True
+        )
+
+    # Decode generated output
+    decoded_text = processor.batch_decode(generated_outputs.sequences)[0]
+    decoded_text = decoded_text.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
+    decoded_text = re.sub(r"<.*?>", "", decoded_text, count=1).strip()  # remove first task start token
+    decoded_text = processor.token2json(decoded_text)
+    return decoded_text
+
+
+device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+processor = DonutProcessor.from_pretrained("AdamCodd/donut-receipts-extract")
+model = VisionEncoderDecoderModel.from_pretrained("AdamCodd/donut-receipts-extract")
+model.to(device)
+
+
+def process_image(image):
+    extracted_text = generate_text_from_image(model, image, processor, device)
+    return extracted_text
+
+
+image = gr.Image(type='pil')
+label = gr.Label()
+
+intf = gr.Interface(fn=process_image, inputs=image, outputs=label)
+intf.launch(inline=False)