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import gradio as gr
import torch
import pytesseract
import cv2
import tempfile
from transformers import AutoTokenizer, AutoModel
pytesseract.pytesseract.tesseract_cmd = r'/opt/homebrew/bin/tesseract' # Update this if necessary
# Load the tokenizer and model
tokenizer_eng = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True)
model_eng = AutoModel.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True).eval()
def perform_ocr(image, language):
# Save the NumPy array as an image file temporarily
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_file:
temp_filename = temp_file.name
cv2.imwrite(temp_filename, image)
# Perform OCR for English
res_eng = model_eng.chat(tokenizer_eng, temp_filename, ocr_type='ocr')
# Clean up temporary file if needed
# os.remove(temp_filename)
return res_eng # Return results for English
def ocr_and_search(image, language):
# Call the perform_ocr function
english_text = perform_ocr(image, language)
return english_text # Return the OCR result for English
# Create Gradio interface
iface = gr.Interface(
fn=ocr_and_search,
inputs=[
gr.Image(type="numpy", label="Upload Image"),
gr.Dropdown(choices=["English", "Hindi"], label="Select Language")
],
outputs=gr.Textbox(label="Extracted Text"),
title="OCR Application",
description="Upload an image to extract text using OCR."
)
# Run the app
if __name__ == "__main__":
iface.launch()
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