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import gradio as gr
import numpy as np
from PIL import Image
import cv2
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from huggingface_hub import hf_hub_download
import torch
import re
# Download and load the GOT OCR model
got_model_path = hf_hub_download(repo_id="junyeopkim/got_2.0_torch_script", filename="got_2.0_tiny.torchscript")
got_model = torch.jit.load(got_model_path)
# Load the Surya-OCR model
surya_processor = TrOCRProcessor.from_pretrained("suryavarmaaddala/suryaocr")
surya_model = VisionEncoderDecoderModel.from_pretrained("suryavarmaaddala/suryaocr")
def preprocess_image(image):
if isinstance(image, str):
image = Image.open(image).convert("RGB")
elif isinstance(image, np.ndarray):
image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
return image
def got_ocr(image):
image = preprocess_image(image)
image = image.resize((224, 224))
input_tensor = torch.from_numpy(np.array(image)).permute(2, 0, 1).float() / 255.0
input_tensor = input_tensor.unsqueeze(0)
with torch.no_grad():
output = got_model(input_tensor)
return output[0].item()
def surya_ocr(image):
image = preprocess_image(image)
pixel_values = surya_processor(image, return_tensors="pt").pixel_values
generated_ids = surya_model.generate(pixel_values)
generated_text = surya_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return generated_text
def post_process_text(text):
# Simple post-processing to split into lines
return '\n'.join(text.split('. '))
def search_text(text, query):
try:
pattern = re.compile(query, re.IGNORECASE)
lines = text.split('\n')
matching_lines = [line for line in lines if pattern.search(line)]
return '\n'.join(matching_lines) if matching_lines else "No matches found."
except re.error:
return "Invalid regex pattern. Please try again."
def process_and_search(image, search_query):
try:
got_score = got_ocr(image)
surya_text = surya_ocr(image)
result = f"GOT OCR Score: {got_score:.4f}\n\nExtracted Text:\n{surya_text}"
processed_text = post_process_text(result)
search = None
if search_query:
search = search_text(processed_text, search_query)
return image, processed_text, search
except Exception as e:
return None, f"An error occurred: {str(e)}", None
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=1):
image_input = gr.Image(type="filepath", label="Upload your image")
search_query_input = gr.Textbox(label="Enter search query")
submit_button = gr.Button("Submit")
with gr.Column(scale=2):
displayed_image = gr.Image(label="Uploaded Image")
ocr_result = gr.Textbox(label="OCR Result", lines=10)
search_result = gr.Textbox(label="Search Result", lines=5)
submit_button.click(
fn=process_and_search,
inputs=[image_input, search_query_input],
outputs=[displayed_image, ocr_result, search_result]
)
if __name__ == "__main__":
demo.launch() |