app.py

import gradio as gr
from PIL import Image
import os
from IndicPhotoOCR.ocr import OCR  # Ensure OCR class is saved in a file named ocr.py
from IndicPhotoOCR.theme import Seafoam
from IndicPhotoOCR.utils.helper import detect_para

# Possible values for identifier_lang
VALID_IDENTIFIER_LANGS = ["hindi", "assamese", "bengali", "gujarati", "kannada", "malayalam","odia", "punjabi", "tamil", "telugu", "auto"]  # Add more as needed

def process_image(image, identifier_lang):
    """
    Processes the uploaded image for text detection and recognition. 
    - Detects bounding boxes in the image
    - Draws bounding boxes on the image and identifies script in each detected area
    - Recognizes text in each cropped region and returns the annotated image and recognized text

    Parameters:
    image (PIL.Image): The input image to be processed.
    identifier_lang (str): The script identifier model to use.

    Returns:
    tuple: A PIL.Image with bounding boxes and a string of recognized text.
    """
    
    # Save the input image temporarily
    image_path = "input_image.jpg"
    image.save(image_path)

    # Initialize OCR with the selected identifier language
    ocr = OCR(device="cpu", identifier_lang=identifier_lang, verbose=False)
    
    # Detect bounding boxes on the image using OCR
    detections = ocr.detect(image_path)
    
    # Draw bounding boxes on the image and save it as output
    ocr.visualize_detection(image_path, detections, save_path="output_image.png")
    
    # Load the annotated image with bounding boxes drawn
    output_image = Image.open("output_image.png")
    
    # Recognize text from the detected areas
    recognized_text = ocr.ocr(image_path)
    recognized_text = '\n'.join([' '.join(line) for line in recognized_text])
    
    return output_image, recognized_text

# Custom HTML for interface header with logos and alignment
interface_html = """
<div style="text-align: left; padding: 10px;">
    <div style="background-color: white; padding: 10px; display: inline-block;">
        <img src="https://iitj.ac.in/images/logo/Design-of-New-Logo-of-IITJ-2.png" alt="IITJ Logo" style="width: 100px; height: 100px;">
    </div>
    <img src="https://play-lh.googleusercontent.com/_FXSr4xmhPfBykmNJvKvC0GIAVJmOLhFl6RA5fobCjV-8zVSypxX8yb8ka6zu6-4TEft=w240-h480-rw" alt="Bhashini Logo" style="width: 100px; height: 100px; float: right;">
</div>
"""



# Links to GitHub and Dataset repositories with GitHub icon
links_html = """
<div style="text-align: center; padding-top: 20px;">
    <a href="https://github.com/Bhashini-IITJ/IndicPhotoOCR" target="_blank" style="margin-right: 20px; font-size: 18px; text-decoration: none;">
        GitHub Repository
    </a>
    <a href="https://github.com/Bhashini-IITJ/BharatSceneTextDataset" target="_blank" style="font-size: 18px; text-decoration: none;">
        Dataset Repository
    </a>
</div>
"""

# Custom CSS to style the text box font size
custom_css = """
.custom-textbox textarea {
    font-size: 20px !important;
}
"""

# Create an instance of the Seafoam theme for a consistent visual style
seafoam = Seafoam()

# Define examples for users to try out
examples = [
    ["test_images/image_141.jpg", "hindi"],
    ["test_images/image_1164.jpg", "auto"]
]

title = "<h1 style='text-align: center;'>Developed by IITJ</h1>"


# Define the Gradio interface
iface = gr.Interface(
    fn=process_image,
    inputs=[
        gr.Image(type="pil", image_mode="RGB"), 
        gr.Dropdown(VALID_IDENTIFIER_LANGS, label="Identifier Language", value="hindi")
    ],
    outputs=[
        gr.Image(type="pil", label="Processed Image"), 
        gr.Textbox(label="Recognized Text")
    ],
    title="IndicPhotoOCR - Indic Scene Text Recogniser Toolkit",
    description=title+interface_html+links_html,
    theme=seafoam,
    css=custom_css,
    examples=examples
)

# Server setup and launch configuration
# if __name__ == "__main__":
#     server = "0.0.0.0"  # IP address for server
#     port = 7866  # Port to run the server on
#     iface.launch(server_name=server, server_port=port, share=False)

iface.launch()