Upload app.py

app.py

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+import streamlit as st
+from transformers import LayoutLMv3ForTokenClassification, LayoutLMv3Processor
+from PIL import Image
+import torch
+import easyocr
+import re
+
+# Load the LayoutLMv3 model and processor
+model_name = "your-username/your-model-name"  # Replace with your model repository name
+model = LayoutLMv3ForTokenClassification.from_pretrained(model_name)
+processor = LayoutLMv3Processor.from_pretrained(model_name)
+
+# Initialize EasyOCR reader for multiple languages
+languages = ["ru", "rs_cyrillic", "be", "bg", "uk", "mn", "en"]
+reader = easyocr.Reader(languages)
+
+st.title("LayoutLMv3 and EasyOCR Text Extraction")
+st.write("Upload an image to get text predictions using the fine-tuned LayoutLMv3 model and EasyOCR.")
+
+uploaded_file = st.file_uploader("Choose an image...", type="png")
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
+    st.write("")
+    st.write("Classifying...")
+
+    # Perform text detection with EasyOCR
+    ocr_results = reader.readtext(uploaded_file, detail=1)
+
+    words = []
+    boxes = []
+
+    # Define a regular expression pattern for non-alphabetic characters
+    non_alphabet_pattern = re.compile(r'[^a-zA-Z]+')
+
+    for result in ocr_results:
+        bbox, text, _ = result
+        filtered_text = re.sub(non_alphabet_pattern, '', text)
+        if filtered_text:  # Only append if there are alphabetic characters left
+            words.append(filtered_text)
+            boxes.append([
+                bbox[0][0], bbox[0][1],
+                bbox[2][0], bbox[2][1]
+            ])
+
+    # Convert to layoutlmv3 format
+    encoding = processor(image, words=words, boxes=boxes, return_tensors="pt")
+
+    # Perform inference with LayoutLMv3
+    with torch.no_grad():
+        outputs = model(**encoding)
+
+    logits = outputs.logits
+    predictions = logits.argmax(-1).squeeze().cpu().tolist()
+    labels = encoding['labels'].squeeze().tolist()
+
+    # Unnormalize bounding boxes
+    def unnormalize_box(bbox, width, height):
+        return [
+            width * (bbox[0] / 1000),
+            height * (bbox[1] / 1000),
+            width * (bbox[2] / 1000),
+            height * (bbox[3] / 1000),
+        ]
+
+    width, height = image.size
+    token_boxes = encoding["bbox"].squeeze().tolist()
+
+    true_predictions = [model.config.id2label[pred] for pred, label in zip(predictions, labels) if label != -100]
+    true_labels = [model.config.id2label[label] for label in labels if label != -100]
+    true_boxes = [unnormalize_box(box, width, height) for box, label in zip(token_boxes, labels) if label != -100]
+    true_tokens = words
+
+    # Display results
+    st.write("Predicted labels:")
+    for word, box, pred in zip(true_tokens, true_boxes, true_predictions):
+        st.write(f"Word: {word}, Box: {box}, Prediction: {pred}")
+
+    # Associate languages with their levels
+    languages_with_levels = {}
+    current_language = None
+
+    j = 0
+    for i in range(len(true_labels)):
+        if true_labels[i] == 'language':
+            current_language = true_tokens[j]
+            j += 1
+            if i + 1 < len(true_labels):
+                languages_with_levels[current_language] = true_labels[i + 1]
+
+    st.write("Languages and Levels:")
+    for language, level in languages_with_levels.items():
+        st.write(f"{language}: {level}")