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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) | |
| 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") | |
| st.subheader("I used Qwen2-VL-7B-Instruct model to get better accuracy but as it is running on CPU it takes 25-30 minutes to run it. So please have patience.") | |
| # File uploader for images | |
| uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"]) | |
| if uploaded_file is not None: | |
| 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 and don't translate Hindi to English."}, | |
| ], | |
| } | |
| ] | |
| # 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=256) | |
| 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.") | |