Add file

app.py

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+import os
+import re
+import gradio as gr
+from transformers import DonutProcessor, VisionEncoderDecoderModel
+import torch
+import traceback
+
+# 1) Load pretrained Donut model and processor
+MODEL_NAME = "naver-clova-ix/donut-base-finetuned-cord-v2"
+processor = DonutProcessor.from_pretrained(MODEL_NAME)
+model = VisionEncoderDecoderModel.from_pretrained(MODEL_NAME)
+
+# 2) Set device and move model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+# 3) Inference function with debugging
+def ocr_donut(image):
+    try:
+        if image is None:
+            return {"error": "No image provided."}
+
+        # Prepare prompt and inputs
+        task_prompt = "<s_cord-v2>"
+        decoder_input_ids = processor.tokenizer(
+            task_prompt,
+            add_special_tokens=False,
+            return_tensors="pt"
+        ).input_ids.to(device)
+
+        # Convert to tensor
+        pixel_values = processor(image.convert("RGB"), return_tensors="pt").pixel_values.to(device)
+
+        # Generate outputs
+        outputs = model.generate(
+            pixel_values,
+            decoder_input_ids=decoder_input_ids,
+            max_length=model.config.decoder.max_position_embeddings,
+            pad_token_id=processor.tokenizer.pad_token_id,
+            eos_token_id=processor.tokenizer.eos_token_id,
+            use_cache=True,
+            bad_words_ids=[[processor.tokenizer.unk_token_id]],
+            return_dict_in_generate=True,
+        )
+
+        # Decode and clean up
+        sequence = processor.batch_decode(outputs.sequences)[0]
+        sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
+        sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()
+        json_output = processor.token2json(sequence)
+
+        return {"result": json_output}
+
+    except Exception:
+        tb = traceback.format_exc()
+        print(tb)
+        return {"error": tb}
+
+# 4) Build Gradio interface
+demo = gr.Interface(
+    fn=ocr_donut,
+    inputs=gr.Image(type="pil", label="Upload Document Image"),
+    outputs=gr.JSON(label="Output"),
+    title="Donut OCR Gradio App",
+    description="Upload a document image and get structured JSON output. Errors will be shown for debugging."
+)
+
+# 5) Launch for Spaces
+demo.launch(
+    server_name="0.0.0.0",
+    server_port=int(os.environ.get("PORT", 7860)),
+    debug=True
+)