Start app

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
+from easyocr import Reader
+from PIL import Image
+from torch.utils.data import Dataset, DataLoader
+
+from transformers import (
+    LayoutLMv3FeatureExtractor, 
+    LayoutLMv3TokenizerFast, 
+    LayoutLMv3Processor, 
+    LayoutLMv3ForSequenceClassification
+    )
+
+DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"
+MICROSOFT_MODEL_NAME = "microsoft/layoutlmv3-base"
+MODEL_NAME = "curiousily/layoutlmv3-financial-document-classification"
+
+def creat_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.experimental_singleton
+def create_ocr_reader():
+    return Reader(["en"])
+
+@st.experimental_singleton
+def create_processor():
+    feature_extractor = LayoutLMv3FeatureExtractor(apply_ocr=False)
+    tokenizer = LayoutLMv3TokenizerFast.from_pretrained(MICROSOFT_MODEL_NAME)
+    return LayoutLMv3Processor(feature_extractor, tokenizer)
+
+@st.experimental_singleton
+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(creat_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.detach().item(), probabilities 
+
+
+reader = create_ocr_reader()
+processor = create_processor()
+model = create_model()
+
+uploaded_file = st.file_uploader("Upload Document Image", ["jpg", "png"])
+if uploaded_file is not None:
+    bytes_data = io.BytesIO(uploaded_file.getvalue())
+    image = Image.open(bytes_data)
+    st.image(image, "Your Document")
+
+    predicted_class, probabilities = predict(image, reader, processor, model)
+    predicted_label = model.config.id2label[predicted_class]
+    
+    st.markdown(f"Predicted document type: **{predicted_label}**")
+
+    df_predictions = pd.DataFrame(
+        {"Document": list(model.config.id2label.values()), "Confidence": probabilities}
+    )
+
+    fig = px.bar(df_predictions, x="Document", y="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