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import streamlit as st
from PIL import Image
from surya.ocr import run_ocr
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
from transformers import AutoModel, AutoTokenizer
import torch
import tempfile
import os
os.environ["CUDA_VISIBLE_DEVICES"] = ""
st.set_page_config(page_title="OCR Application", page_icon="🖼️", layout="wide")
# Force CPU if CUDA is not available
device = "cuda" if torch.cuda.is_available() else "cpu"
@st.cache_resource
def load_surya_models():
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)
return det_processor, det_model, rec_model, rec_processor
@st.cache_resource
def load_got_ocr_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=device, use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
model.eval().to(device)
# Override .half() and .cuda() to ensure everything runs in float32 and on CPU
torch.Tensor.half = lambda x: x.float()
torch.Tensor.cuda = lambda x, **kwargs: x.cpu()
return tokenizer, model
det_processor, det_model, rec_model, rec_processor = load_surya_models()
tokenizer, got_model = load_got_ocr_model()
st.title("OCR Application (Aarish Shah Mohsin)")
st.write("Upload an image for OCR processing. Using GOT-OCR for English translations, Picked Surya OCR Model for English+Hindi Translations")
st.sidebar.header("Configuration")
model_choice = st.sidebar.selectbox("Select OCR Model:", ("For English + Hindi", "For English (GOT-OCR)"))
# Store the uploaded image and extracted text in session state
if 'uploaded_image' not in st.session_state:
st.session_state.uploaded_image = None
if 'extracted_text' not in st.session_state:
st.session_state.extracted_text = ""
uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
# Update the session state if a new file is uploaded
if uploaded_file is not None:
st.session_state.uploaded_image = uploaded_file
predict_button = st.sidebar.button("Predict", key="predict")
col1, col2 = st.columns([2, 1])
# Display the image preview if it's already uploaded
if st.session_state.uploaded_image:
image = Image.open(st.session_state.uploaded_image)
with col1:
# Display a smaller preview of the uploaded image (set width to 300px)
col1.image(image, caption='Uploaded Image', use_column_width=False, width=300)
# Handle predictions
if predict_button and st.session_state.uploaded_image:
with st.spinner("Processing..."):
# Save the uploaded file temporarily
with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
temp_file.write(st.session_state.uploaded_image.getvalue())
temp_file_path = temp_file.name
image = Image.open(temp_file_path)
image = image.convert("RGB")
if model_choice == "For English + Hindi":
langs = ["en", "hi"]
predictions = run_ocr([image], [langs], det_model, det_processor, rec_model, rec_processor)
text_list = re.findall(r"text='(.*?)'", str(predictions[0]))
extracted_text = ' '.join(text_list)
st.session_state.extracted_text = extracted_text # Save extracted text in session state
elif model_choice == "For English (GOT-OCR)":
image_file = temp_file_path
res = got_model.chat(tokenizer, image_file, ocr_type='ocr')
st.session_state.extracted_text = res # Save extracted text in session state
# Delete the temporary file after processing
if os.path.exists(temp_file_path):
os.remove(temp_file_path)
# Search functionality
if st.session_state.extracted_text:
search_query = st.text_input("Search in extracted text:", key="search_query", placeholder="Type to search...")
# Create a pattern to find the search query in a case-insensitive way
if search_query:
pattern = re.compile(re.escape(search_query), re.IGNORECASE)
highlighted_text = st.session_state.extracted_text
# Replace matching text with highlighted version (bright green)
highlighted_text = pattern.sub(lambda m: f"<span style='background-color: limegreen;'>{m.group(0)}</span>", highlighted_text)
st.markdown("### Highlighted Search Results:")
st.markdown(highlighted_text, unsafe_allow_html=True)
else:
# If no search query, show the original extracted text
st.markdown("### Extracted Text:")
st.markdown(st.session_state.extracted_text, unsafe_allow_html=True)
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