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import gradio as gr
import torch
from PIL import Image
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import re
min_pixels = 256 * 28 * 28
max_pixels = 1280 * 28 * 28
def model_inference(images):
model = Qwen2VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2-VL-2B-Instruct",
trust_remote_code=True,
torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
images = [{"type": "image", "image": Image.open(image[0])} for image in images]
messages = [{"role": "user", "content": "Please extract only the text from the following image."}, {"role": "user", "content": images}]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
device = "cuda" if torch.cuda.is_available() else "cpu"
inputs = inputs.to(device)
model = model.to(device)
generated_ids = model.generate(**inputs, max_new_tokens=512)
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
)
del model
del processor
return output_text[0]
def search_and_highlight(text, keywords):
if not keywords:
return text
keywords = [kw.strip().lower() for kw in keywords.split(',')]
highlighted_text = text
for keyword in keywords:
pattern = re.compile(re.escape(keyword), re.IGNORECASE)
highlighted_text = pattern.sub(f'**{keyword}**', highlighted_text)
return highlighted_text
def extract_and_search(images, keywords):
extracted_text = model_inference(images)
highlighted_text = search_and_highlight(extracted_text, keywords)
return extracted_text, highlighted_text
with gr.Blocks(theme=gr.themes.Soft()) as demo:
with gr.Row():
output_gallery = gr.Gallery(label="Image", height=300, show_label=True)
keywords = gr.Textbox(placeholder="Enter keywords to search (comma-separated)", label="Search Keywords")
extract_button = gr.Button("Extract Text and Search", variant="primary")
with gr.Row():
raw_output = gr.Textbox(label="Extracted Text")
highlighted_output = gr.Markdown(label="Highlighted Search Results")
extract_button.click(extract_and_search, inputs=[output_gallery, keywords], outputs=[raw_output, highlighted_output])
if __name__ == "__main__":
demo.queue(max_size=10).launch(share=True) |