import spaces import os import gradio as gr from pdf2image import convert_from_path from byaldi import RAGMultiModalModel from transformers import Qwen2VLForConditionalGeneration, AutoProcessor from qwen_vl_utils import process_vision_info import torch import torchvision import subprocess def install_poppler(): try: subprocess.run(["pdfinfo"], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) except FileNotFoundError: print("Poppler not found. Installing...") subprocess.run("apt-get update", shell=True) subprocess.run("apt-get install -y poppler-utils", shell=True) install_poppler() subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) RAG = RAGMultiModalModel.from_pretrained("vidore/colpali-v1.2") model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda().eval() processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True) @spaces.GPU() def process_pdf_and_query(pdf_file, user_query): images = convert_from_path(pdf_file.name) num_images = len(images) RAG.index( input_path=pdf_file.name, index_name="image_index", store_collection_with_index=False, overwrite=True ) results = RAG.search(user_query, k=1) if not results: return "No results found.", num_images image_index = results[0]["page_num"] - 1 messages = [ { "role": "user", "content": [ { "type": "image", "image": images[image_index], }, {"type": "text", "text": user_query}, ], } ] 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") generated_ids = model.generate(**inputs, max_new_tokens=50) 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 ) return output_text[0], num_images css = """ body { font-family: Arial, sans-serif; background-color: #2b2b2b; color: #e0e0e0; } .container { max-width: 800px; margin: 0 auto; padding: 20px; background-color: #363636; border-radius: 10px; box-shadow: 0 0 10px rgba(0,0,0,0.3); } .title { font-size: 24px; font-weight: bold; text-align: center; margin-bottom: 20px; color: #50fa7b; } .submit-btn { background-color: #50fa7b; color: #282a36; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer; font-size: 16px; font-weight: bold; } .submit-btn:hover { background-color: #45c967; } .duplicate-button { background-color: #8be9fd; color: #282a36; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer; font-size: 16px; font-weight: bold; margin-top: 20px; } .duplicate-button:hover { background-color: #79c7d8; } a { color: #8be9fd; text-decoration: none; } a:hover { text-decoration: underline; } """ explanation = """ <div style="background-color: #44475a; padding: 15px; border-radius: 5px; margin-bottom: 20px; color: #f8f8f2;"> <h3 style="color: #50fa7b;">About Multimodal RAG</h3> <p>Multimodal RAG (Retrieval-Augmented Generation) combines text and image processing to provide more context-aware responses. This demo uses:</p> <ul> <li><strong style="color: #ffb86c;">ColPali</strong>: A multimodal retriever for efficient information retrieval from images and text.</li> <li><strong style="color: #ffb86c;">Byaldi</strong>: A new library by answer.ai that simplifies the use of ColPali.</li> <li><strong style="color: #ffb86c;">Qwen/Qwen2-VL-2B-Instruct</strong>: A large language model capable of processing both text and visual inputs.</li> </ul> <p>This combination allows for more accurate and context-aware responses to queries about uploaded PDFs.</p> </div> """ footer = """ <div style="text-align: center; margin-top: 20px; color: #f8f8f2;"> <a href="https://www.linkedin.com/in/pejman-ebrahimi-4a60151a7/" target="_blank">LinkedIn</a> | <a href="https://github.com/arad1367" target="_blank">GitHub</a> | <a href="https://arad1367.pythonanywhere.com/" target="_blank">Live demo of my PhD defense</a> | <a href="https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct" target="_blank">Qwen/Qwen2-VL-2B-Instruct</a> | <a href="https://github.com/AnswerDotAI/byaldi" target="_blank">Byaldi</a> | <a href="https://github.com/illuin-tech/colpali" target="_blank">ColPali</a> <br> Made with 💖 by Pejman Ebrahimi </div> """ with gr.Blocks(css=css, theme='freddyaboulton/dracula_revamped') as demo: gr.HTML('<h1 style="text-align: center; font-size: 32px;"><a href="https://github.com/arad1367" target="_blank" style="text-decoration: none; color: #50fa7b;">Multimodal RAG with Image Query - By Pejman Ebrahimi (Please Like the Space)</a></h1>') gr.HTML(explanation) pdf_input = gr.File(label="Upload PDF") query_input = gr.Textbox(label="Enter your query", placeholder="Ask a question about the PDF") submit_btn = gr.Button("Submit", elem_classes="submit-btn") output_text = gr.Textbox(label="Model Answer") output_images = gr.Textbox(label="Number of Images in PDF") submit_btn.click(process_pdf_and_query, inputs=[pdf_input, query_input], outputs=[output_text, output_images]) gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button") gr.HTML(footer) demo.launch(debug=True)