Delete app.py

app.py

@@ -1,109 +0,0 @@
-import streamlit as st
-from transformers import LayoutLMv3ForTokenClassification, LayoutLMv3Processor
-from PIL import Image
-import torch
-import easyocr
-from PIL import Image
-import re
-
-# Load the model and processor from Hugging Face
-model_name = "capitaletech/LayoutLMv3-v1"  # Replace with your model repository name
-model = LayoutLMv3ForTokenClassification.from_pretrained(model_name)
-processor = LayoutLMv3Processor.from_pretrained(model_name)
-
-st.title("LayoutLMv3 Text Extraction")
-st.write("Upload an image to get text predictions using the fine-tuned LayoutLMv3 model.")
-
-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...")
-
-    # Process the image
-    words = uploaded_file["tokens"]
-    boxes = uploaded_file["bboxes"]
-    word_labels = uploaded_file["ner_tags"]
-
-    encoding = processor(image, words, boxes=boxes, word_labels=word_labels, return_tensors="pt")
-
-    with torch.no_grad():
-        outputs = model(**encoding)
-
-    logits = outputs.logits
-    predictions = logits.argmax(-1).squeeze().cpu.tolist()
-    labels = encoding['labels'].squeeze().tolist()
-
-
-    # Set up the EasyOCR reader for multiple languages
-    languages = ["ru", "rs_cyrillic", "be", "bg", "uk", "mn", "en"]
-    reader = easyocr.Reader(languages)
-
-    # Load the image
-    image_path = example["img_path"]
-    image = Image.open(image_path)
-
-    # Perform text detection
-    ocr_results = reader.readtext(image_path, detail=1)
-
-    # Extract text and bounding boxes, filtering non-alphabetic characters from text
-    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]
-            ])
-
-    words = words[1:]
-
-
-    def unnormalize_box(bbox, width, height):
-        return [
-         width * (bbox[0] / 1000),
-         height * (bbox[1] / 1000),
-         width * (bbox[2] / 1000),
-         height * (bbox[3] / 1000),
-         ]
-
-    token_boxes = encoding["bbox"].squeeze().tolist()
-    width, height = image.size
-
-    true_predictions = [model.config.id2label[pred] for pred, label in zip(predictions, labels) if label != - 100]
-    true_labels = [model.config.id2label[label] for prediction, label in zip(predictions, 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
-
-    # Associate languages with their levels
-    languages_with_levels = {}
-    current_language = None
-
-    j=0
-
-    for i in range(0, len(true_labels)):
-        if true_labels[i] == 'language':
-            current_language = words[j]
-            j= j+1
-            languages_with_levels[current_language] = true_labels[i+1]
-
-    print(languages_with_levels)
-
-    input_ids = encoding["input_ids"]
-    bbox = encoding["bbox"]
-    attention_mask = encoding["attention_mask"]
-
-    st.write("Predicted labels:")
-    # Print languages with their levels
-    for language, level in languages_with_levels.items():
-        st.write(f"{language}: {level}")
-