First commit

README.md

@@ -1,8 +1,8 @@
 ---
-title: Unstructured Invoices
-emoji: 🐢
-colorFrom: pink
-colorTo: green
+title: Invoices Parser
+emoji: ⚡
+colorFrom: purple
+colorTo: red
 sdk: streamlit
 sdk_version: 1.17.0
 app_file: app.py

app.py

@@ -0,0 +1,83 @@
+import torch
+import streamlit as st
+import os
+
+from PIL import Image
+from io import BytesIO
+from transformers import VisionEncoderDecoderModel, VisionEncoderDecoderConfig , DonutProcessor
+
+task_prompt = "<s_unstructured-invoices>"
+
+def run_prediction(sample):
+    global pretrained_model, processor, task_prompt
+    if isinstance(sample, dict):
+        # prepare inputs
+        pixel_values = torch.tensor(sample["pixel_values"]).unsqueeze(0)
+    else:  # sample is an image
+        # prepare encoder inputs
+        pixel_values = processor(sample, return_tensors="pt").pixel_values
+    
+    decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
+
+    outputs = pretrained_model.generate(
+        pixel_values.to(device),
+        decoder_input_ids=decoder_input_ids.to(device)
+    )
+
+    # process output
+    prediction = processor.token2json(processor.batch_decode(outputs)[0])
+    
+    # load reference target
+    if isinstance(sample, dict):
+        target = processor.token2json(sample["target_sequence"])
+    else:
+        target = "<not_provided>"
+    
+    return prediction, target
+    
+
+logo = Image.open("./img/rsz_unstructured_logo.png")
+st.image(logo)
+
+st.markdown('''
+### Invoice Parser
+This is an OCR-free Document Understanding Transformer. It was fine-tuned with 1000 invoice images -> RVL-CDIP dataset.
+The original implementation can be found on [here](https://github.com/clovaai/donut).
+
+At [Unstructured.io](https://github.com/Unstructured-IO/unstructured) we are on a mission to build custom preprocessing pipelines for labeling, training, or production ML-ready pipelines. 
+Come and join us in our public repos and contribute! Each of your contributions and feedback holds great value and is very significant to the community.
+''')
+
+image_upload = None
+photo = None
+with st.sidebar:
+    # file upload
+    uploaded_file = st.file_uploader("Upload an invoice")
+    if uploaded_file is not None:
+        # To read file as bytes:
+        image_bytes_data = uploaded_file.getvalue()
+        image_upload = Image.open(BytesIO(image_bytes_data))  #.frombytes('RGBA', (128,128), image_bytes_data, 'raw')
+        # st.write(bytes_data)
+
+col1, col2 = st.columns(2)
+
+if image_upload:
+    image = image_upload
+else:
+    image = Image.open(f"./img/4fabfaab-1299.png")
+
+with col1:
+    st.image(image, caption='Your target invoice')
+
+with st.spinner(f'baking the invoice ...'):
+        processor = DonutProcessor.from_pretrained("unstructuredio/donut-invoices", max_length=1200, use_auth_token=os.environ['TOKEN'])
+        pretrained_model = VisionEncoderDecoderModel.from_pretrained("unstructuredio/donut-invoices", max_length=1200, use_auth_token=os.environ['TOKEN'])
+        
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        pretrained_model.to(device)
+
+with col2:
+    st.info(f'Parsing invoice')
+    parsed_info, _ = run_prediction(image.convert("RGB"))
+    st.text(f'\nInvoice Summary:')
+    st.json(parsed_info)

requirements.txt

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+opencv-python==4.7.0.68
+streamlit
+torch==1.13.1
+transformers==4.26.0