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| from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor | |
| from qwen_vl_utils import process_vision_info | |
| # default: Load the model on the available device(s) | |
| model = Qwen2VLForConditionalGeneration.from_pretrained( | |
| "Qwen/Qwen2-VL-7B-Instruct", torch_dtype="auto", device_map="auto" | |
| ) | |
| # We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios. | |
| # model = Qwen2VLForConditionalGeneration.from_pretrained( | |
| # "Qwen/Qwen2-VL-7B-Instruct", | |
| # torch_dtype=torch.bfloat16, | |
| # attn_implementation="flash_attention_2", | |
| # device_map="auto", | |
| # ) | |
| # default processer | |
| processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct") | |
| # The default range for the number of visual tokens per image in the model is 4-16384. You can set min_pixels and max_pixels according to your needs, such as a token count range of 256-1280, to balance speed and memory usage. | |
| # min_pixels = 256*28*28 | |
| # max_pixels = 1280*28*28 | |
| # processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels) | |
| # 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) | |
| 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("cuda") | |
| # 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) | |
| ] | |
| output_text = processor.batch_decode( | |
| generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False | |
| ) | |
| st.subheader("Extracted Text:") | |
| st.write(output_text) | |
| # Keyword search functionality | |
| 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 extracted text | |
| if search_query.lower() in extracted_text.lower(): | |
| highlighted_text = extracted_text.replace(search_query, f"**{search_query}**") | |
| st.write(f"Matching Text: {highlighted_text}") | |
| else: | |
| st.write("No matching text found.") |