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from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import streamlit as st
import torch
from PIL import Image
# Default: Load the model on the available device(s)
@st.cache_resource
def init_qwen_model():
model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", torch_dtype="auto", device_map="auto")
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
return model, processor
MODEL, PROCESSOR = init_qwen_model()
# Streamlit app title
st.title("OCR Image Text Extraction")
# File uploader for images
uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
if uploaded_file is not None:
# Open the uploaded image file
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_column_width=True)
# Add the spinner here while the model is processing
with st.spinner("Extracting text..."):
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": image,
},
{"type": "text", "text": "Run Optical Character recognition on the image."},
],
}
]
# Preparation for inference
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",
)
inputs = inputs.to("cpu")
# Inference: Generation of the output
generated_ids = MODEL.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
structured_output = PROCESSOR.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]
# Convert structured output to plain text
plain_text_output = " ".join(structured_output.split()) # Remove any extra spaces or line breaks
# Display extracted plain text after the spinner ends
st.subheader("Extracted Plain Text:")
st.write(plain_text_output)
# Keyword search functionality on plain text
st.subheader("Keyword Search")
search_query = st.text_input("Enter keywords to search within the extracted text")
if search_query:
# Check if the search query is in the plain text output
if search_query.lower() in plain_text_output.lower():
# Highlight the search query in the plain text
highlighted_text = plain_text_output.replace(search_query, f"**{search_query}**", flags=re.IGNORECASE)
st.markdown(f"Matching Text: {highlighted_text}", unsafe_allow_html=True)
else:
st.write("No matching text found.")
else:
st.info("Please upload an image to extract text.")
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