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 # Run the commands from setup.sh to install poppler-utils def install_poppler(): try: subprocess.run(["pdfinfo"], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) except FileNotFoundError: print("Poppler not found. Installing...") # Run the setup commands subprocess.run("apt-get update", shell=True) subprocess.run("apt-get install -y poppler-utils", shell=True) # Call the Poppler installation check install_poppler() # Install flash-attn if not already installed subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) # Load the RAG Model and the Qwen2-VL-2B-Instruct model RAG = RAGMultiModalModel.from_pretrained("vidore/colpali") 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 ) # Search the query in the RAG model results = RAG.search(user_query, k=1) if not results: return "No results found.", num_images # Retrieve the page number and process image 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 = """ .duplicate-button { background-color: #6272a4; color: white; font-weight: bold; border-radius: 5px; margin-top: 20px; padding: 10px; text-align: center; } .gradio-container { background-color: #282a36; color: #f8f8f2; font-family: 'Courier New', Courier, monospace; padding: 20px; border-radius: 10px; box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); } """ explanation = """ ### Multimodal RAG with Image Query This demo showcases the **Multimodal RAG (Retriever-Augmented Generation)** model. The RAG system integrates retrieval and generation, allowing it to retrieve relevant information from a multimodal database (like PDFs with text and images) and then generate detailed responses. We use **ColPali**, a state-of-the-art multimodal retriever, combined with the **Byaldi** library from **answer.ai**, which simplifies using ColPali. The language model used for generating answers is **Qwen/Qwen2-VL-2B-Instruct**, a powerful vision-language model capable of understanding both text and images. """ footer = """
LinkedIn | GitHub | Live demo of my PhD defense | Qwen/Qwen2-VL-2B-Instruct | Byaldi | ColPali
Made with 💖 by Pejman Ebrahimi
""" pdf_input = gr.File(label="Upload PDF") query_input = gr.Textbox(label="Enter your query", placeholder="Ask a question about the PDF") output_text = gr.Textbox(label="Model Answer") output_images = gr.Textbox(label="Number of Images in PDF") duplicate_button = gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button") # Launch the Gradio app demo = gr.Interface( fn=process_pdf_and_query, inputs=[pdf_input, query_input], outputs=[output_text, output_images], title="Multimodal RAG with Image Query - By Pejman Ebrahimi - Please like the space if it is useful", theme='freddyaboulton/dracula_revamped', css=css, description=explanation, allow_flagging="auto" ) with demo: gr.HTML(footer) duplicate_button demo.launch(debug=True)