from PIL import Image import pytesseract import gradio as gr from datetime import datetime import os from flair.data import Sentence from flair.models import SequenceTagger from segtok.segmenter import split_single import pandas as pd # tagger = SequenceTagger.load("ner-ontonotes") tagger = SequenceTagger.load("flair/ner-english-ontonotes") langs = [] choices = os.popen("tesseract --list-langs").read().split("\n")[1:-1] blocks = gr.Blocks() def get_named_entities(ocr_text: str): sentence = [Sentence(sent, use_tokenizer=True) for sent in split_single(ocr_text)] tagger.predict(sentence) entities = [] for token in sentence: for entity in token.get_spans("ner"): entity = str(entity) entities.append(entity) entities = "\n".join(entities) return entities def run(image, lang="eng"): print("Image ", image) print("Image type ", type(image)) result = pytesseract.image_to_string(image, lang=None if lang == [] else lang) ner = get_named_entities(result) return result, ner def download_output(ocr_text: str, named_entities: str, image_name="test"): try: named_entities_list = named_entities.split("\n") now = datetime.now() datetime_now = now.strftime("%Y%m%d_%H%M%S") output_file = f"{image_name}_{datetime_now}.xlsx" ocr_df = pd.Series(ocr_text) print("OCR ", ocr_df) ner_df = pd.Series(named_entities_list) print("NER ", ner_df) with pd.ExcelWriter(output_file) as writer: ocr_df.to_excel(writer, sheet_name="OCR text") ner_df.to_excel(writer, sheet_name="Named entities") return output_file except Exception as e: raise gr.Error(f"Something went wrong: here's the error: {e}") with gr.Blocks() as demo: gr.Markdown("## Theatre Programmer") with gr.Row(): with gr.Column(): image_in = gr.Image(type="pil") lang = gr.Dropdown(choices, value="eng") btn = gr.Button("Run") with gr.Column(): ocr_text = gr.TextArea(label="OCR output") with gr.Column(): ner = gr.TextArea(label="Named entities") # with gr.Column(): # gr.CheckboxGroup(ner, label="Named entities") with gr.Row(): download_btn = gr.Button("Download output") btn.click(fn=run, inputs=[image_in, lang], outputs=[ocr_text, ner]) download_btn.click( fn=download_output, inputs=[ocr_text, ner], outputs=[gr.components.File()], ) demo.launch()