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import re
import io
import torch
import gradio as gr
from PIL import Image
from qwen_vl_utils import process_vision_info
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
def OCRmodel():
# default: Load the model on the available device(s)
model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True, torch_dtype=torch.float32)
# default processer
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True)
# Move the model to the correct device (GPU if available, otherwise CPU)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
return model, processor
model, processor = OCRmodel()
# Function to read the image and process it for OCR
def ocr(image_data):
"""
Process the uploaded image and extract text using the OCR model.
Args:
image_data: Image data in bytes.
Returns:
Extracted text as a string.
"""
text_query = "Extract all the text in Sanskrit and English from the image."
# Prepare messages for the model with the image
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": image_data},
{"type": "text", "text": text_query}],
}
]
# Prepare text and image input for the model
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, _ = process_vision_info(messages)
# Process inputs
inputs = processor(
text=[text],
images=image_inputs,
padding=True,
return_tensors="pt"
)
inputs = inputs.to("cuda" if torch.cuda.is_available() else "cpu") # Move to GPU if available
# Generate the output from the model
with torch.no_grad():
# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=2000, no_repeat_ngram_size=3, temperature=0.7)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
return " ".join(output_text).strip()
# Function to highlight search terms in the text
def highlight_keywords(text, keywords):
pattern = "|".join(re.escape(keyword) for keyword in keywords)
highlighted_text = re.sub(f"({pattern})", rf'<mark style="background-color:{"red"};">\1</mark>', text, flags=re.IGNORECASE)
return highlighted_text
# Gradio interface function
def process_image(image, search_query):
extracted_text = ocr(image)
if search_query:
# Highlight matching keywords
keywords = search_query.split() # Split input into individual keywords
highlighted_text = highlight_keywords(extracted_text, keywords)
else:
highlighted_text = extracted_text
return highlighted_text
# Gradio Interface
application = gr.Interface(
fn=process_image, # Function to process the image and search query
inputs=[
gr.Image(type="pil", label="Upload Image"), # Image input
gr.Textbox(label="Enter search keywords") # Textbox for search query
],
outputs=gr.HTML(label="Extracted and Highlighted Text") # Output area
)
# Launch the Gradio app
application.launch(share=True) |