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from transformers import AutoModel, AutoTokenizer, Qwen2VLForConditionalGeneration, AutoProcessor
import streamlit as st
import os
from PIL import Image
import torch
import re
@st.cache_resource
def init_model():
tokenizer = AutoTokenizer.from_pretrained('srimanth-d/GOT_CPU', trust_remote_code=True)
model = AutoModel.from_pretrained('srimanth-d/GOT_CPU', trust_remote_code=True, use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
model = model.eval()
return model, tokenizer
# def init_gpu_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='cuda', use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
# model = model.eval().cuda()
# return model, tokenizer
def init_qwen_model():
model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", device_map="cpu", torch_dtype=torch.float16)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
return model, processor
def get_quen_op(image_file, model, processor):
try:
image = Image.open(image_file).convert('RGB')
conversation = [
{
"role":"user",
"content":[
{
"type":"image",
},
{
"type":"text",
"text":"Extract text from this image."
}
]
}
]
text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
inputs = processor(text=[text_prompt], images=[image], padding=True, return_tensors="pt")
inputs = {k: v.to(torch.float32) if torch.is_floating_point(v) else v for k, v in inputs.items()}
generation_config = {
"max_new_tokens": 32,
"do_sample": False,
"top_k": 20,
"top_p": 0.90,
"temperature": 0.4,
"num_return_sequences": 1,
"pad_token_id": processor.tokenizer.pad_token_id,
"eos_token_id": processor.tokenizer.eos_token_id,
}
output_ids = model.generate(**inputs, **generation_config)
if 'input_ids' in inputs:
generated_ids = output_ids[:, inputs['input_ids'].shape[1]:]
else:
generated_ids = output_ids
output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
return output_text[:] if output_text else "No text extracted from the image."
except Exception as e:
return f"An error occurred: {str(e)}"
@st.cache_data
def get_text(image_file, _model, _tokenizer):
res = _model.chat(_tokenizer, image_file, ocr_type='ocr')
return res
def highlight_text(text, search_term):
if not search_term:
return text
pattern = re.compile(re.escape(search_term), re.IGNORECASE)
return pattern.sub(lambda m: f'<span style="background-color: green;">{m.group()}</span>', text)
st.title("Image To Text")
st.write("Upload an image")
MODEL, PROCESSOR = init_model()
image_file = st.file_uploader("Upload Image", type=['jpg', 'png', 'jpeg'])
if image_file:
if not os.path.exists("images"):
os.makedirs("images")
with open(f"images/{image_file.name}", "wb") as f:
f.write(image_file.getbuffer())
image_file = f"images/{image_file.name}"
text = get_text(image_file, MODEL, PROCESSOR)
print(text)
# Add search functionality
search_term = st.text_input("Enter a word or phrase to search:")
highlighted_text = highlight_text(text, search_term)
st.markdown(highlighted_text, unsafe_allow_html=True) |