Update app.py

app.py

@@ -1,111 +1,110 @@
-# import all required libraries after doing research
-import gradio as gr
-from PIL import Image
-from surya.ocr import run_ocr  # dedicated GOT_OCR_2.0 for hindi languages
-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  # recognized hindi encoded pattern
-from transformers import AutoModel, AutoTokenizer
-import torch
-import tempfile
-import os
-
-# device = "cuda"
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-print(device)
-# load_desirable_model
-got_model_name = 'tdnathmlenthusiast/got-ocr-2.0-modified-hindi-version'
-
-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)
-
-# tokenized to extract individual character
-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)
-
-
-# function to extract hindi & english
-def extract_hindi(text):
-    # Unicode range for Devanagari script
-    hindi_pattern = re.compile(r'[\u0900-\u097F]+')
-    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
-
-# code to search words like documents
-
-
-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 for Hindi & English")
-    gr.Markdown(
-        "Upload an image for OCR processing.(Takes a little bit time or sometimes a lot due to the limitation of the resources)")
-
-    with gr.Row():
-        with gr.Column():
-            image_input = gr.Image(
-                type="pil", label="Upload an Image(Hindi/English/Hindi+English)")
-            run_ocr_button = gr.Button("Run OCR")
-
-        with gr.Column():
-            output_text = gr.HTML(label="Extracted Text in Hindi & English")
-            query_input = gr.Textbox(
-                label="Search in extracted text", 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()
-
-# Developed by Tirtha Debnath.
+# import all required libraries after doing research
+import gradio as gr
+from PIL import Image
+from surya.ocr import run_ocr  # dedicated GOT_OCR_2.0 for hindi languages
+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  # recognized hindi encoded pattern
+from transformers import AutoModel, AutoTokenizer
+import torch
+import tempfile
+import os
+
+# device = "cuda"
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+print(device)
+# load_desirable_model
+got_model_name = "tdnathmlenthusiast/td_ocr_2.0_hindi"
+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)
+
+# tokenized to extract individual character
+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)
+
+
+# function to extract hindi & english
+def extract_hindi(text):
+    # Unicode range for Devanagari script
+    hindi_pattern = re.compile(r'[\u0900-\u097F]+')
+    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
+
+# code to search words like documents
+
+
+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 for Hindi & English")
+    gr.Markdown(
+        "Upload an image for OCR processing.(Takes a little bit time or sometimes a lot due to the limitation of the resources)")
+
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(
+                type="pil", label="Upload an Image(Hindi/English/Hindi+English)")
+            run_ocr_button = gr.Button("Run OCR")
+
+        with gr.Column():
+            output_text = gr.HTML(label="Extracted Text in Hindi & English")
+            query_input = gr.Textbox(
+                label="Search in extracted text", 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()
+
+# Developed by Tirtha Debnath.