Upload OCRFINAL.py

OCRFINAL.py

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+import gradio as gr
+import os
+import uuid
+import shutil
+import re
+from transformers import AutoModel, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True)
+model = AutoModel.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True, low_cpu_mem_usage=True, device_map='cpu', use_safetensors=True)
+model = model.eval()
+
+UPLOAD_FOLDER = "./uploads"
+RESULTS_FOLDER = "./results"
+
+for folder in [UPLOAD_FOLDER, RESULTS_FOLDER]:
+    if not os.path.exists(folder):
+        os.makedirs(folder)
+
+
+def run_GOT(image, search_term):
+    unique_id = str(uuid.uuid4())
+    image_path = os.path.join(UPLOAD_FOLDER, f"{unique_id}.png")
+    
+  
+    shutil.copy(image, image_path)
+    
+    try:
+        
+        res = model.chat(tokenizer, image_path, ocr_type='ocr')
+        
+        highlighted_text = highlight_text(res, search_term)
+        
+        return highlighted_text, None
+    except Exception as e:
+        return f"Error: {str(e)}", None
+    finally:
+        if os.path.exists(image_path):
+            os.remove(image_path)
+
+
+def highlight_text(text, search_term):
+    if not search_term:
+        return text
+    pattern = re.compile(re.escape(search_term), re.IGNORECASE)
+    return pattern.sub(lambda m: f'<span style="background-color: yellow;">{m.group()}</span>', text)
+
+title_html = """
+<h2> <span class="gradient-text" id="text">General OCR Theory (GOT)</span>: Multi-Language OCR HINDI AND ENGLISH</h2>
+"""
+
+with gr.Blocks() as demo:
+    gr.HTML(title_html)
+    gr.Markdown("""
+    ### Instructions
+    Upload your respective image below and click "Submit" to extract text in both English and Hindi. If you want you can select the word to be highlighted.
+    """)
+
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="filepath", label="Upload your image")
+            search_input = gr.Textbox(label="Enter a word to search", placeholder="Search term")
+            submit_button = gr.Button("Submit")
+        
+        with gr.Column():
+            ocr_result = gr.HTML(label="Extracted Text:")
+
+    
+    submit_button.click(run_GOT, inputs=[image_input, search_input], outputs=[ocr_result])
+
+if __name__ == "__main__":
+    demo.launch()