Update app.py

app.py

@@ -1,30 +1,14 @@
 import gradio as gr
-import os
-import shutil
-import argparse
-import torch
-from PIL import Image
 from pdf2image import convert_from_path
-from donut import DonutModel
 
-def demo_process(doc):
-    global model, task_prompt, task_name
-    file_name = os.path.basename(doc)
-    new_file_location = os.path.join(os.getcwd(),file_name)
-    
-    shutil.copyfile(doc, new_file_location)
-    img = convert_from_path(new_file_location)[0]
-    output = model.inference(image=img, prompt=task_prompt)["predictions"][0]
-    return output
-    
-parser = argparse.ArgumentParser()
-parser.add_argument("--task", type=str, default="SGSInvoice")
-parser.add_argument("--pretrained_path", type=str, default="uartimcs/donut-invoice-extract")
-args, left_argv = parser.parse_known_args()
-task_name = args.task
-task_prompt = f"<s_{task_name}>"
+def pdf_to_jpg(pdf_path):
+    images = convert_from_path(pdf_path.name, first_page=1, last_page=1)
+    return images[0].save("first_page.jpg")
 
-model = DonutModel.from_pretrained("uartimcs/donut-invoice-extract")
-model.eval()
-demo = gr.Interface(fn=demo_process,inputs=gr.File(label="Upload PDF"),outputs="json", title=f"Donut 🍩 demonstration for `{task_name}` task",)
-demo.launch()
+interface = gr.Interface(
+    fn=pdf_to_jpg,
+    inputs=gr.inputs.File(label="Upload PDF"),
+    outputs="image"
+)
+
+interface.launch()