Start app v.1

app.py

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+
+import io
+import pandas as pd
+import plotly_express as px
+import streamlit as st
+import torch
+import torch.nn.functional as F
+import numpy as np
+from easyocr import Reader
+from PIL import Image
+from transformers import (
+    LayoutLMv3ImageProcessor,
+    LayoutLMv3ForSequenceClassification,
+    LayoutLMv3Processor,
+    LayoutLMv3TokenizerFast,
+)
+
+
+DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"
+MICROSOFT_MODEL_NAME = "microsoft/layoutlmv3-base"
+MODEL_NAME = "curiousily/layoutlmv3-financial-document-classification"
+
+def create_bounding_box(bbox_data, width_scale: float, height_scale: float):
+    xs = []
+    ys = []
+    for x, y in bbox_data:
+        xs.append(x)
+        ys.append(y)
+        
+    left = int(min(xs) * width_scale)
+    top = int(min(ys) * height_scale)
+    right = int(max(xs) * width_scale)
+    bottom = int(max(ys) * height_scale)
+    
+    return [left, top, right, bottom]
+
+@st.cache_resource
+def create_ocr_reader():
+    return Reader(["en"])
+
+@st.cache_resource
+def create_processor():
+    feature_extractor = LayoutLMv3ImageProcessor(apply_ocr = False)
+    tokenizer = LayoutLMv3TokenizerFast.from_pretrained(MICROSOFT_MODEL_NAME)
+    return LayoutLMv3Processor(feature_extractor, tokenizer)
+
+@st.cache_resource
+def create_model():
+    model = LayoutLMv3ForSequenceClassification.from_pretrained(MODEL_NAME)
+    return model.eval().to(DEVICE)
+
+def predict(image: Image, reader: Reader, processor: LayoutLMv3Processor, model: LayoutLMv3ForSequenceClassification):
+    ocr_result = reader.readtext(image)
+    
+    width, height = image.size
+    width_scale = 1000 / width
+    height_scale = 1000 / height
+    
+    words = []
+    boxes = []
+    
+    for bbox, word, confidence in ocr_result:
+        words.append(word)
+        boxes.append(create_bounding_box(bbox, width_scale, height_scale))
+        
+    encoding = processor(image, words, boxes = boxes, max_length=512, padding = "max_length", truncation = True, return_tensors = "pt")
+        
+    with torch.inference_mode():
+        output = model(input_ids = encoding["input_ids"].to(DEVICE),
+        attention_mask = encoding["attention_mask"].to(DEVICE),
+        bbox = encoding["bbox"].to(DEVICE),
+        pixel_values = encoding["pixel_values"].to(DEVICE))
+        
+    logits = output.logits
+    predicted_class = logits.argmax()
+    probabilities = F.softmax(logits, dim=-1).flatten().tolist()
+    
+    return predicted_class.cpu().item(), probabilities
+
+reader = create_ocr_reader()
+processor = create_processor()
+model = create_model()
+
+upload_file = st.file_uploader("Upload Document Image", ["jpg", "png"])
+if upload_file is not None:
+    bytes_data = io.BytesIO(upload_file.getvalue())
+    image = Image.open(bytes_data)
+    st.image(image, "Your Document Image")
+    
+    predicted_class, probabilities = predict(image, reader, processor, model)
+    print("Predicted class:",predicted_class)
+    print("Probabilities:",probabilities)
+    # print(predict(image, reader, processor, model))
+    
+    predicted_label = model.config.id2label[predicted_class]
+    
+    st.markdown(f"Predicted document type: **{predicted_label}**")
+    
+    # make chart
+    df_predictions = pd.DataFrame({
+        "Document" : list(model.config.id2label.values()),
+        "confidence" : probabilities
+    })
+    
+    fig = px.bar(df_predictions, x = "Document", y = "confidence", title = "Document Type Confidence")
+    st.plotly_chart(fig, use_container_width=True)

requirements.txt

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+easyocr==1.6.2
+pandas==1.5.3
+Pillow==9.4.0
+plotly-express==0.4.1
+torch==1.13.1
+transformers==4.25.1