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
from transformers import (
    AutoModelForSeq2SeqLM,
    AutoTokenizer,

)
import numpy as np
import torch
from collections import Counter

def Most_Common(lst):
    data = Counter(lst)
    return data.most_common(1)[0][0]


from IndicTransToolkit import IndicProcessor

# Initialize the OCR object for text detection and recognition
ocr = OCR(device='cpu',verbose=False)

def translate(given_str,lang='hindi'):
    DEVICE = 'cpu'
    model_name = "ai4bharat/indictrans2-en-indic-1B" if lang=="english" else "ai4bharat/indictrans2-indic-en-1B"
    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

    model = AutoModelForSeq2SeqLM.from_pretrained(model_name, trust_remote_code=True)

    ip = IndicProcessor(inference=True)

    model = model.to(DEVICE)
    model.eval()
    src_lang, tgt_lang = ("eng_Latn", "hin_Deva") if lang=="english" else ("hin_Deva", "eng_Latn" )
    
    batch = ip.preprocess_batch(
        [given_str],
        src_lang=src_lang,
        tgt_lang=tgt_lang,
    )
    inputs = tokenizer(
            batch,
            truncation=True,
            padding="longest",
            return_tensors="pt",
            return_attention_mask=True,
        ).to(DEVICE)
    with torch.no_grad():
        generated_tokens = model.generate(
            **inputs,
            use_cache=True,
            min_length=0,
            max_length=256,
            num_beams=5,
            num_return_sequences=1,
        )

    # Decode the generated tokens into text
    with tokenizer.as_target_tokenizer():
        generated_tokens = tokenizer.batch_decode(
            generated_tokens.detach().cpu().tolist(),
            skip_special_tokens=True,
            clean_up_tokenization_spaces=True,
        )
    translation = ip.postprocess_batch(generated_tokens, lang=tgt_lang)[0]
    return translation



        

    
def process_image(image):
    """
    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.

    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)
    
    # 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")
    
    # Initialize list to hold recognized text from each detected area
    recognized_texts = {}
    pil_image = Image.open(image_path)
    
    # # Process each detected bounding box for script identification and text recognition
    # for bbox in detections:
    #     # Identify the script and crop the image to this region
    #     script_lang, cropped_path = ocr.crop_and_identify_script(pil_image, bbox)
        
    #     if script_lang:  # Only proceed if a script language is identified
    #         # Recognize text in the cropped area
    #         recognized_text = ocr.recognise(cropped_path, script_lang)
    #         recognized_texts.append(recognized_text)
    langs = []
    for id, bbox in enumerate(detections):
        # Identify the script and crop the image to this region
        script_lang, cropped_path = ocr.crop_and_identify_script(pil_image, bbox)

        # Calculate bounding box coordinates
        x1 = min([bbox[i][0] for i in range(len(bbox))])
        y1 = min([bbox[i][1] for i in range(len(bbox))])
        x2 = max([bbox[i][0] for i in range(len(bbox))])
        y2 = max([bbox[i][1] for i in range(len(bbox))])

        if script_lang:
            recognized_text = ocr.recognise(cropped_path, script_lang)
            recognized_texts[f"img_{id}"] = {"txt": recognized_text, "bbox": [x1, y1, x2, y2]}
            langs.append(script_lang)

    # Combine recognized texts into a single string for display
    # recognized_texts_combined = " ".join(recognized_texts)
    string = detect_para(recognized_texts)

    recognized_texts_combined = '\n'.join([' '.join(line) for line in string])
    recognized_texts_combined = translate(recognized_texts_combined,Most_Common(langs))

    return output_image, recognized_texts_combined

# 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/208.jpg"],
    ["test_images/1310.jpg"]
]
title = "<h1 style='text-align: center;'>Developed by IITJ</h1>"

# Set up the Gradio Interface with the defined function and customizations
demo = gr.Interface(
    fn=process_image,
    inputs=gr.Image(type="pil", image_mode="RGB"),
    outputs=[
        gr.Image(type="pil", label="Detected Bounding Boxes"),
        gr.Textbox(label="Translated Text", elem_classes="custom-textbox")
    ],
    title="Scene Text Translator",
    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 = 7867  # Port to run the server on
#     demo.launch(server_name=server, server_port=port)

demo.launch()