import gradio as gr from transformers import AutoModel, AutoTokenizer from PIL import Image # Load the tokenizer and model 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='cuda', use_safetensors=True, pad_token_id=tokenizer.eos_token_id) model = model.eval().cuda() # Define the OCR function def perform_ocr(image): # Convert PIL image to RGB format (if necessary) if image.mode != "RGB": image = image.convert("RGB") # Save the image to a temporary path image_file_path = 'temp_image.jpg' image.save(image_file_path) # Perform OCR using the model res = model.chat(tokenizer, image_file_path, ocr_type='ocr') return res # Define the search function def search_keyword(extracted_text, keyword): # Check if keyword is provided if not keyword.strip(): return "Please enter a keyword." # Search for the keyword in the extracted text if keyword.lower() in extracted_text.lower(): return f"Keyword '{keyword}' found in the extracted text!" else: return f"Keyword '{keyword}' not found in the extracted text." # Define the interface with both OCR and keyword search functionality def ocr_and_search(image, keyword): # Perform OCR to extract text from the image extracted_text = perform_ocr(image) # Perform keyword search within the extracted text search_result = search_keyword(extracted_text, keyword) # Return both the extracted text and the search result return extracted_text, search_result # Define the Gradio interface interface = gr.Interface( fn=ocr_and_search, inputs=[gr.Image(type="pil", label="Upload Image"), gr.Textbox(label="Enter Keyword to Search")], outputs=[gr.Textbox(label="Extracted Text"), gr.Textbox(label="Search Result")], title="OCR and Document Search Web Application", description="Upload an image to extract text using the GOT-OCR2_0 model and search for a keyword within the extracted text." ) # Launch the Gradio app interface.launch()