Upload ocr_project.py

ocr_project.py

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+import numpy as np
+import easyocr
+from transformers import AutoModel, AutoTokenizer
+from PIL import Image
+import warnings
+from transformers import logging
+import re
+
+#To Surpaas warnings
+warnings.filterwarnings("ignore", message="The attention mask and the pad token id were not set.")
+warnings.filterwarnings("ignore", message="Setting `pad_token_id` to `eos_token_id`")
+warnings.filterwarnings("ignore", message="The `seen_tokens` attribute is deprecated")
+
+logging.set_verbosity_error()
+
+
+tokenizer = AutoTokenizer.from_pretrained('srimanth-d/GOT_CPU', trust_remote_code=True)
+model = AutoModel.from_pretrained('srimanth-d/GOT_CPU', trust_remote_code=True, low_cpu_mem_usage=True, use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
+model = model.eval()
+
+
+easyocr_reader = easyocr.Reader(['hi'], gpu=False)  
+
+# Function to perform OCR based on selected language
+def perform_ocr(image, language):
+    if language == "Hindi":
+        image_np = np.array(image)  
+        result = easyocr_reader.readtext(image_np, detail=0)
+        return ' '.join(result)
+    elif language == "English":
+        image_path = 'temp_image.png'
+        image.save(image_path)
+        result = model.chat(tokenizer, image_path, ocr_type='ocr')
+        return result
+    else:
+        return "Invalid language selection. Please choose Hindi or English."
+
+def process_keyword(image, language, keyword):
+    extracted_text = perform_ocr(image, language)  
+    if keyword:
+        keyword_regex = re.escape(keyword)
+        highlighted_text = re.sub(
+            f'({keyword_regex})', r'<mark style="background-color: yellow">\1</mark>', extracted_text, flags=re.IGNORECASE
+        )
+        
+        if highlighted_text != extracted_text:
+            return highlighted_text
+        else:
+            return f"No keyword '{keyword}' found in the text."
+    else:
+        return extracted_text