back to final state

app.py

@@ -1,6 +1,5 @@
-import gradio as gr
-import csv
 import re
+import gradio as gr
 
 import torch
 from transformers import DonutProcessor, VisionEncoderDecoderModel
@@ -38,37 +37,20 @@ def process_document(image):
     sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
     sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()  # remove first task start token
     
-    with open('output.csv', 'a', newline='') as file:
-        writer = csv.writer(file)
-        writer.writerow(["image", sequence])
-    
     return processor.token2json(sequence)
 
-def download_csv():
-    return "output.csv"
-
 description = "To use it, simply upload your image 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'>Donut: OCR-free Document Understanding Transformer</a> | <a href='https://github.com/clovaai/donut' target='_blank'>Github Repo</a></p>"
 
-inputs = gr.inputs.Image()
-outputs = gr.outputs.Json()
-
-examples = [["example.png"], ["example_2.png"], ["example_3.png"]]
-
-download_button = gr.outputs.Button("Download CSV")
-
-interface = gr.Interface(
-    process_document, 
-    inputs=inputs, 
-    outputs=outputs, 
+demo = gr.Interface(
+    fn=process_document,
+    inputs="image",
+    outputs="json",
     title="Donut 🍩 for Document Parsing and Converting the Image to Database",
     description=description,
     article=article,
-    examples=examples,
     enable_queue=True,
-    cache_examples=False,
-    allow_download=True,
-    download_button=download_button
-)
+    examples=[["example.png"], ["example_2.png"], ["example_3.png"]],
+    cache_examples=False)
 
-interface.launch()
+demo.launch()