import io import streamlit as st from transformers import AutoModel, AutoTokenizer, Qwen2VLForConditionalGeneration, AutoProcessor from PIL import Image import torch import os import re import json import io import base64 from groq import Groq from st_keyup import st_keyup from st_img_pastebutton import paste from text_highlighter import text_highlighter # Page configuration st.set_page_config(page_title="DualTextOCRFusion", page_icon="🔍", layout="wide") device = "cuda" if torch.cuda.is_available() else "cpu" # Load GOT Models @st.cache_resource def init_got_model(): tokenizer = AutoTokenizer.from_pretrained( 'srimanth-d/GOT_CPU', trust_remote_code=True) model = AutoModel.from_pretrained( 'srimanth-d/GOT_CPU', trust_remote_code=True, use_safetensors=True, pad_token_id=tokenizer.eos_token_id) return model.eval(), tokenizer @st.cache_resource def init_got_gpu_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) return model.eval().cuda(), tokenizer # Load Qwen Model @st.cache_resource def init_qwen_model(): model = Qwen2VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2-VL-2B-Instruct", device_map="cpu", torch_dtype=torch.float16) processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct") return model.eval(), processor # Text Cleaning AI - Clean spaces, handle dual languages def clean_extracted_text(text): cleaned_text = re.sub(r'\s+', ' ', text).strip() cleaned_text = re.sub(r'\s([?.!,])', r'\1', cleaned_text) return cleaned_text # Polish the text using a model def polish_text_with_ai(cleaned_text): prompt = f"Remove unwanted spaces between and inside words to join incomplete words, creating a meaningful sentence in either Hindi, English, or Hinglish without altering any words from the given extracted text. Then, return the corrected text with adjusted spaces, keeping it as close to the original as possible, along with relevant details or insights that an AI can provide about the extracted text. Extracted Text : {cleaned_text}" client = Groq( api_key="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") chat_completion = client.chat.completions.create( messages=[ { "role": "system", "content": "You are a pedantic sentence corrector. Remove extra spaces between and within words to make the sentence meaningful in English, Hindi, or Hinglish, according to the context of the sentence, without changing any words." }, { "role": "user", "content": prompt, } ], model="gemma2-9b-it", ) polished_text = chat_completion.choices[0].message.content return polished_text # Extract text using GOT def extract_text_got(image_file, model, tokenizer): return model.chat(tokenizer, image_file, ocr_type='ocr') # Extract text using Qwen def extract_text_qwen(image_file, model, processor): try: image = Image.open(image_file).convert('RGB') conversation = [{"role": "user", "content": [{"type": "image"}, { "type": "text", "text": "Extract text from this image."}]}] text_prompt = processor.apply_chat_template( conversation, add_generation_prompt=True) inputs = processor(text=[text_prompt], images=[ image], return_tensors="pt") output_ids = model.generate(**inputs) output_text = processor.batch_decode( output_ids, skip_special_tokens=True) return output_text[0] if output_text else "No text extracted from the image." except Exception as e: return f"An error occurred: {str(e)}" # Function to highlight the keyword in the text def highlight_text(cleaned_text, start, end): text_highlighter( text=cleaned_text, labels=[("KEYWORD", "#0000FF")], annotations=[ {"start": start, "end": end, "tag": "KEYWORD"}, ], ) # Title and UI st.title("DualTextOCRFusion - 🔍") st.header("OCR Application - Multimodel Support") st.write("Upload an image for OCR using various models, with support for English, Hindi, and Hinglish.") # Sidebar Configuration st.sidebar.header("Configuration") model_choice = st.sidebar.selectbox( "Select OCR Model:", ("GOT_CPU", "GOT_GPU", "Qwen")) # Upload Section uploaded_file = st.sidebar.file_uploader( "Choose An Image : ", type=["png", "jpg", "jpeg"]) # Input from clipboard # Paste image button clipboard_use = False image_data = paste( label="Paste From Clipboard", key="image_clipboard") if image_data is not None: clipboard_use = True header, encoded = image_data.split(",", 1) decoded_bytes = base64.b64decode(encoded) img_stream = io.BytesIO(decoded_bytes) uploaded_file = img_stream # Input from camera camera_file = st.sidebar.camera_input("Capture From Camera : ") if camera_file: uploaded_file = camera_file # Predict button predict_button = st.sidebar.button("Predict") # Main columns col1, col2 = st.columns([2, 1]) cleaned_text = "" polished_text = "" # Display image preview if uploaded_file: image = Image.open(uploaded_file) with col1: col1.image(image, caption='Uploaded Image', use_column_width=False, width=300) # Save uploaded image to 'images' folder images_dir = 'images' os.makedirs(images_dir, exist_ok=True) image_path = os.path.join( images_dir, "temp_file.png" if clipboard_use else uploaded_file.name) with open(image_path, 'wb') as f: f.write(uploaded_file.getvalue()) # Check if the result already exists results_dir = 'results' os.makedirs(results_dir, exist_ok=True) result_path = os.path.join( results_dir, "temp_file_result.json" if clipboard_use else f"{uploaded_file.name}_result.json") # Display extracted text if 'cleaned_text' not in st.session_state: st.session_state.cleaned_text = "" if 'polished_text' not in st.session_state: st.session_state.polished_text = "" # Handle predictions if predict_button: if os.path.exists(result_path): with open(result_path, 'r') as f: result_data = json.load(f) extracted_text = result_data["extracted_text"] cleaned_text = result_data["cleaned_text"] polished_text = result_data["polished_text"] else: with st.spinner("Processing..."): if model_choice == "GOT_CPU": got_model, tokenizer = init_got_model() extracted_text = extract_text_got( image_path, got_model, tokenizer) elif model_choice == "GOT_GPU": got_gpu_model, tokenizer = init_got_gpu_model() extracted_text = extract_text_got( image_path, got_gpu_model, tokenizer) elif model_choice == "Qwen": qwen_model, qwen_processor = init_qwen_model() extracted_text = extract_text_qwen( image_path, qwen_model, qwen_processor) # Clean and polish extracted text if not cleaned_text and polished_text: cleaned_text = clean_extracted_text(extracted_text) polished_text = polish_text_with_ai(cleaned_text) if model_choice in [ "GOT_CPU", "GOT_GPU"] else cleaned_text # Save results to JSON file if not os.path.exists(result_path): result_data = {"extracted_text": extracted_text, "cleaned_text": cleaned_text, "polished_text": polished_text} with open(result_path, 'w') as f: json.dump(result_data, f) # Save results to session state st.session_state.cleaned_text = cleaned_text st.session_state.polished_text = polished_text # Display extracted text st.subheader("Extracted Text (Cleaned & Polished)") st.markdown(st.session_state.cleaned_text, unsafe_allow_html=True) st.markdown(st.session_state.polished_text, unsafe_allow_html=True) # Input search term with real-time update on key press search_query = st_keyup("Search in extracted text:") if search_query: index = st.session_state.cleaned_text.find(search_query) start = index len = len(search_query) end = index + len if index != -1: highlight_text(st.session_state.cleaned_text, start, end) else: st.write("No Search Found.")