made app

app.py

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+import gradio as gr
+from PIL import Image
+from surya.ocr import run_ocr
+from surya.model.detection.model import load_model as load_det_model, load_processor as load_det_processor
+from surya.model.recognition.model import load_model as load_rec_model
+from surya.model.recognition.processor import load_processor as load_rec_processor
+import re
+from transformers import AutoModel, AutoTokenizer
+import torch
+import tempfile
+import os
+
+# device = "cuda"  
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+print(device)
+got_model_name = "stepfun-ai/GOT-OCR2_0" if device == 'cuda' else "aarishshahmohsin/got_ocr_2"
+
+det_processor, det_model = load_det_processor(), load_det_model()
+det_model.to(device)
+rec_model, rec_processor = load_rec_model(), load_rec_processor()
+rec_model.to(device)
+
+tokenizer = AutoTokenizer.from_pretrained(got_model_name, trust_remote_code=True, device_map=device)
+got_model = AutoModel.from_pretrained(got_model_name, trust_remote_code=True, low_cpu_mem_usage=True, device_map=device, use_safetensors=True)
+got_model = got_model.eval().to(device)
+
+def extract_hindi(text):
+    hindi_pattern = re.compile(r'[\u0900-\u097F]+')  # Unicode range for Devanagari script
+    hindi_words = hindi_pattern.findall(text)
+    return ' '.join(hindi_words)
+
+def process_image(image):
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
+        image.save(temp_file.name)
+        temp_file_path = temp_file.name
+
+    image = Image.open(temp_file_path)
+    image = image.convert("RGB")  
+
+    langs = ["hi"]  
+    surya_predictions = run_ocr([image], [langs], det_model, det_processor, rec_model, rec_processor)
+    
+    surya_text_list = re.findall(r"text='(.*?)'", str(surya_predictions[0]))
+    surya_text = '\n'.join(surya_text_list)  
+    surya_text = extract_hindi(surya_text)
+    
+    got_res = got_model.chat(tokenizer, temp_file_path, ocr_type='ocr')
+
+    combined_text = f"<h2> Hindi Text (Surya OCR) </h2> <br>{surya_text}<br> <br> <h2> English Text (GOT OCR) </h2> <br> {got_res}"
+
+    if os.path.exists(temp_file_path):
+        os.remove(temp_file_path)
+
+    return combined_text
+
+def highlight_search(text, query):
+    if query:
+        pattern = re.compile(re.escape(query), re.IGNORECASE)
+        highlighted_text = pattern.sub(lambda m: f"<span style='background-color: limegreen;'>{m.group(0)}</span>", text)
+        return highlighted_text
+    return text
+
+with gr.Blocks() as ocr_interface:
+    gr.Markdown("# OCR Application (IIT Roorkee Assignment)")
+    gr.Markdown("Upload an image for OCR processing. This uses Surya OCR (for both Hindi) and GOT-OCR (for English). The results from both models will be concatenated.")
+
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Upload an Image")
+            run_ocr_button = gr.Button("Run OCR")
+
+        with gr.Column():
+            output_text = gr.HTML(label="Extracted Text")
+            query_input = gr.Textbox(label="Search in extracted text (optional)", placeholder="Type to search...")
+            search_button = gr.Button("Search")
+    
+    def process_and_display(image):
+        combined_text = process_image(image)
+        return combined_text
+
+    def search_text(combined_text, query):
+        highlighted = highlight_search(combined_text, query)
+        return highlighted
+
+    run_ocr_button.click(fn=process_and_display, inputs=image_input, outputs=output_text)
+
+    search_button.click(fn=search_text, inputs=[output_text, query_input], outputs=output_text)
+
+ocr_interface.launch()