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import cv2
import gradio as gr
import boot
from ocr import detection, recognition
boot.warmup_model()
def extract_text_from_image(image):
"""
Main function that will be called by Gradio.
This function processes the image and returns both the image and extracted text.
"""
if image is None:
return None, "Please upload an image first"
progress = gr.Progress()
# Progress callback function for OCR
def ocr_progress(current, total, message):
progress(current / total, desc=message)
detection_result = detection.inference(image, lang="mya", psm=6, min_conf=-1)
recognition_result = recognition.inference(detection_result, progress_callback=ocr_progress)
result_img = detection.draw_boxes(image, detection_result)
image_with_detection_boxes = cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB)
return image_with_detection_boxes, recognition_result
# Create the Gradio interface
with gr.Blocks(title="Myanmar OCR Demo", theme=gr.themes.Soft()) as demo:
gr.Markdown("# Myanmar OCR Demo Application")
with gr.Row():
# Left column - Input
with gr.Column(scale=1):
gr.Markdown("## Input")
input_image = gr.Image(label="Upload Image", type="numpy", height=400)
process_btn = gr.Button("Extract Text", variant="primary", size="lg")
# Right column - Output
with gr.Column(scale=1):
gr.Markdown("## Output")
# Top part - Show input image
with gr.Group():
gr.Markdown("### Text Detection (Tesseract)")
output_image = gr.Image(label="Detection Result", height=600, interactive=False)
# Bottom part - Show extracted text
with gr.Group():
gr.Markdown("### Text Recognition (SMOL-VLM)")
output_text = gr.Textbox(
label="OCR Results",
lines=10,
max_lines=15,
interactive=False,
placeholder="Extracted text will appear here...",
)
# Connect the button to the function
process_btn.click(fn=extract_text_from_image, inputs=[input_image], outputs=[output_image, output_text])
if __name__ == "__main__":
demo.launch()
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