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import spaces |
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import gradio as gr |
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from pdf2image import convert_from_path |
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from byaldi import RAGMultiModalModel |
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from transformers import Qwen2VLForConditionalGeneration, AutoProcessor |
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from qwen_vl_utils import process_vision_info |
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import torch |
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import subprocess |
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subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) |
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RAG = RAGMultiModalModel.from_pretrained("vidore/colpali") |
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model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", |
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trust_remote_code=True, torch_dtype=torch.bfloat16).cuda().eval() |
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processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True) |
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@spaces.GPU() |
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def process_pdf_and_query(pdf_file, user_query): |
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images = convert_from_path(pdf_file.name) |
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num_images = len(images) |
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RAG.index( |
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input_path=pdf_file.name, |
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index_name="image_index", |
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store_collection_with_index=False, |
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overwrite=True |
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) |
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results = RAG.search(user_query, k=1) |
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if not results: |
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return "No results found.", num_images |
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image_index = results[0]["page_num"] - 1 |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{ |
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"type": "image", |
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"image": images[image_index], |
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}, |
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{"type": "text", "text": user_query}, |
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], |
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} |
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] |
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text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = processor( |
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text=[text], |
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images=image_inputs, |
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videos=video_inputs, |
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padding=True, |
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return_tensors="pt", |
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) |
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inputs = inputs.to("cuda") |
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generated_ids = model.generate(**inputs, max_new_tokens=50) |
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generated_ids_trimmed = [ |
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out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
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] |
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output_text = processor.batch_decode( |
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False |
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) |
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return output_text[0], num_images |
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pdf_input = gr.File(label="Upload PDF") |
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query_input = gr.Textbox(label="Enter your query", placeholder="Ask a question about the PDF") |
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output_text = gr.Textbox(label="Model Answer") |
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output_images = gr.Textbox(label="Number of Images in PDF") |
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demo = gr.Interface( |
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fn=process_pdf_and_query, |
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inputs=[pdf_input, query_input], |
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outputs=[output_text, output_images], |
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title="Multimodal RAG with Image Query - By Pejman Ebrahimi" |
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) |
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demo.launch(debug=True) |
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