Spaces:
Running
Running
File size: 4,081 Bytes
b093688 c542962 b093688 c542962 1fedf30 c542962 b093688 c542962 b093688 1fedf30 c542962 1fedf30 c542962 1fedf30 c542962 b093688 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
from transformers import AutoModel, AutoTokenizer, Qwen2VLForConditionalGeneration, AutoProcessor
import streamlit as st
import os
from PIL import Image
import requests
import torch
from torchvision import io
from typing import Dict
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: yellow;">{m.group()}</span>', text)
st.title("Image - Text OCR (General OCR Theory - GOT)")
st.write("Upload an image for OCR")
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) |