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| import gradio as gr | |
| import subprocess | |
| from PIL import Image | |
| import tempfile | |
| import os | |
| import yaml | |
| import base64 | |
| import evaluate | |
| def resize_image(image, base_height): | |
| if image.size[1] == base_height: | |
| return image | |
| # Calculate aspect ratio | |
| w_percent = base_height / float(image.size[1]) | |
| w_size = int(float(image.size[0]) * float(w_percent)) | |
| # Resize the image | |
| return image.resize((w_size, base_height), Image.Resampling.LANCZOS) | |
| # Get images and respective transcriptions from the examples directory | |
| def get_example_data(folder_path="./examples/"): | |
| example_data = [] | |
| # Get list of all files in the folder | |
| all_files = os.listdir(folder_path) | |
| # Loop through the file list | |
| for file_name in all_files: | |
| file_path = os.path.join(folder_path, file_name) | |
| # Check if the file is an image (.png) | |
| if file_name.endswith(".jpg"): | |
| # Construct the corresponding .txt filename (same name) | |
| corresponding_text_file_name = file_name.replace(".jpg", ".txt") | |
| corresponding_text_file_path = os.path.join(folder_path, corresponding_text_file_name) | |
| # Initialize to a default value | |
| transcription = "Transcription not found." | |
| # Try to read the content from the .txt file | |
| try: | |
| with open(corresponding_text_file_path, "r") as f: | |
| transcription = f.read().strip() | |
| except FileNotFoundError: | |
| pass # If the corresponding .txt file is not found, leave the default value | |
| example_data.append([file_path, transcription]) | |
| return example_data | |
| def predict(input_image: Image.Image, ground_truth): | |
| cer = None | |
| try: | |
| # Try to resize the image to a fixed height of 128 pixels | |
| try: | |
| input_image = resize_image(input_image, 128) | |
| except Exception as e: | |
| print(f"Image resizing failed: {e}") | |
| return f"Image resizing failed: {e}" | |
| # Used as a context manager. Takes care of cleaning up the directory. | |
| # Even if an error is raised within the with block, the directory is removed. | |
| # No finally block needed | |
| with tempfile.TemporaryDirectory() as temp_dir: | |
| temp_image_path = os.path.join(temp_dir, 'temp_image.jpg') | |
| temp_list_path = os.path.join(temp_dir, 'temp_img_list.txt') | |
| temp_config_path = os.path.join(temp_dir, 'temp_config.yaml') | |
| input_image.save(temp_image_path) | |
| # Create a temporary img_list file | |
| with open(temp_list_path, 'w') as f: | |
| f.write(temp_image_path) | |
| # Read the original config file and create a temporary one | |
| with open('my_decode_config.yaml', 'r') as f: | |
| config_data = yaml.safe_load(f) | |
| config_data['img_list'] = temp_list_path | |
| with open(temp_config_path, 'w') as f: | |
| yaml.dump(config_data, f) | |
| try: | |
| subprocess.run(f"pylaia-htr-decode-ctc --config {temp_config_path} | tee predict.txt", shell=True, check=True) | |
| except subprocess.CalledProcessError as e: | |
| print(f"Command failed with error {e.returncode}, output:\n{e.output}") | |
| # # Write the output to predict.txt | |
| # with open('predict.txt', 'wb') as f: | |
| # f.write(output) | |
| # Read the output from predict.txt | |
| if os.path.exists('predict.txt'): | |
| with open('predict.txt', 'r') as f: | |
| output_line = f.read().strip().split('\n')[-1] # Last line | |
| _, prediction = output_line.split(' ', 1) # split only at the first space | |
| else: | |
| print('predict.txt does not exist') | |
| if ground_truth is not None and ground_truth.strip() != "": | |
| # Debug: Print lengths before computing metric | |
| print("Number of predictions:", len(prediction)) | |
| print("Number of references:", len(ground_truth)) | |
| # Check if lengths match | |
| if len(prediction) != len(ground_truth): | |
| print("Mismatch in number of predictions and references.") | |
| print("Predictions:", prediction) | |
| print("References:", ground_truth) | |
| print("\n") | |
| cer = cer_metric.compute(predictions=[prediction], references=[ground_truth]) | |
| # cer = f"{cer:.3f}" | |
| else: | |
| cer = "Ground truth not provided" | |
| return prediction, cer | |
| except subprocess.CalledProcessError as e: | |
| return f"Command failed with error {e.returncode}" | |
| # Encode images | |
| with open("assets/header.png", "rb") as img_file: | |
| logo_html = base64.b64encode(img_file.read()).decode('utf-8') | |
| with open("assets/teklia_logo.png", "rb") as img_file: | |
| footer_html = base64.b64encode(img_file.read()).decode('utf-8') | |
| title = """ | |
| <h1 style='text-align: center'> Hugging Face x Teklia: PyLaia HTR demo</p> | |
| """ | |
| description = """ | |
| [PyLaia](https://github.com/jpuigcerver/PyLaia) is a device agnostic, PyTorch-based, deep learning toolkit \ | |
| for handwritten document analysis. | |
| This model was trained using PyLaia library on Norwegian historical documents ([NorHand Dataset](https://zenodo.org/record/6542056)) \ | |
| during the [HUGIN-MUNIN project](https://hugin-munin-project.github.io) for handwritten text recognition (HTR). | |
| * HF `model card`: [Teklia/pylaia-huginmunin](https://huggingface.co/Teklia/pylaia-huginmunin) | \ | |
| [A Comprehensive Comparison of Open-Source Libraries for Handwritten Text Recognition in Norwegian](https://doi.org/10.1007/978-3-031-06555-2_27) | |
| """ | |
| examples = get_example_data() | |
| # pip install evaluate | |
| # pip install jiwer | |
| cer_metric = evaluate.load("cer") | |
| with gr.Blocks( | |
| theme=gr.themes.Soft(), | |
| title="PyLaia HTR", | |
| ) as demo: | |
| gr.HTML( | |
| f""" | |
| <div style='display: flex; justify-content: center; width: 100%;'> | |
| <img src='data:image/png;base64,{logo_html}' class='img-fluid' width='350px'> | |
| </div> | |
| """ | |
| ) | |
| #174x60 | |
| title = gr.HTML(title) | |
| description = gr.Markdown(description) | |
| with gr.Row(): | |
| with gr.Column(variant="panel"): | |
| input = gr.components.Image(type="pil", label="Input image:") | |
| with gr.Row(): | |
| btn_clear = gr.Button(value="Clear") | |
| button = gr.Button(value="Submit") | |
| with gr.Column(variant="panel"): | |
| output = gr.components.Textbox(label="Generated text:") | |
| ground_truth = gr.components.Textbox(value="", placeholder="Provide the ground truth, if available.", label="Ground truth:") | |
| cer_output = gr.components.Textbox(label="CER:") | |
| with gr.Row(): | |
| with gr.Accordion(label="Choose an example from test set:", open=False): | |
| gr.Examples( | |
| examples=examples, | |
| inputs = [input, ground_truth], | |
| label=None, | |
| ) | |
| with gr.Row(): | |
| gr.HTML( | |
| f""" | |
| <div style="display: flex; align-items: center; justify-content: center"> | |
| <a href="https://teklia.com/" target="_blank"> | |
| <img src="data:image/png;base64,{footer_html}" style="width: 100px; height: 80px; object-fit: contain; margin-right: 5px; margin-bottom: 5px"> | |
| </a> | |
| <p style="font-size: 13px"> | |
| | <a href="https://huggingface.co/Teklia">Teklia models on Hugging Face</a> | |
| </p> | |
| </div> | |
| """ | |
| ) | |
| button.click(predict, inputs=[input, ground_truth], outputs=[output, cer_output]) | |
| btn_clear.click(lambda: [None, "", "", ""], outputs=[input, output, ground_truth, cer_output]) | |
| # # Try to force light mode | |
| # js = """ | |
| # function () { | |
| # gradioURL = window.location.href | |
| # if (!gradioURL.endsWith('?__theme=light')) { | |
| # window.location.replace(gradioURL + '?__theme=light'); | |
| # } | |
| # }""" | |
| # demo.load(_js=js) | |
| if __name__ == "__main__": | |
| demo.launch(favicon_path="teklia_icon_grey.png") | |