import os import spaces import gradio as gr import torch from colpali_engine.models.paligemma_colbert_architecture import ColPali from colpali_engine.trainer.retrieval_evaluator import CustomEvaluator from colpali_engine.utils.colpali_processing_utils import ( process_images, process_queries, ) from pdf2image import convert_from_path from PIL import Image from torch.utils.data import DataLoader from tqdm import tqdm from transformers import AutoProcessor, Idefics3ForConditionalGeneration import re import time from PIL import Image import torch import subprocess subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) @spaces.GPU def model_inference( images, text, assistant_prefix= "Réfléchis step by step. Répond en faisant de belles phrases", decoding_strategy = "Greedy", temperature= 0.4, max_new_tokens=512, repetition_penalty=1.2, top_p=0.8 ): ## Load idefics id_processor = AutoProcessor.from_pretrained("HuggingFaceM4/Idefics3-8B-Llama3") id_model = Idefics3ForConditionalGeneration.from_pretrained("HuggingFaceM4/Idefics3-8B-Llama3", torch_dtype=torch.bfloat16, #_attn_implementation="flash_attention_2" ).to("cuda") BAD_WORDS_IDS = id_processor.tokenizer(["", ""], add_special_tokens=False).input_ids EOS_WORDS_IDS = [id_processor.tokenizer.eos_token_id] print(type(images)) print(images[0]) images = [Image.open(image[0]) for image in images] print(images) print(type(images)) if text == "" and not images: gr.Error("Please input a query and optionally image(s).") if text == "" and images: gr.Error("Please input a text query along the image(s).") if isinstance(images, Image.Image): images = [images] resulting_messages = [ { "role": "user", "content": [{"type": "image"}] + [ {"type": "text", "text": text} ] } ] if assistant_prefix: text = f"{assistant_prefix} {text}" prompt = id_processor.apply_chat_template(resulting_messages, add_generation_prompt=True) inputs = id_processor(text=prompt, images=images, return_tensors="pt") inputs = {k: v.to("cuda") for k, v in inputs.items()} generation_args = { "max_new_tokens": max_new_tokens, "repetition_penalty": repetition_penalty, } assert decoding_strategy in [ "Greedy", "Top P Sampling", ] if decoding_strategy == "Greedy": generation_args["do_sample"] = False elif decoding_strategy == "Top P Sampling": generation_args["temperature"] = temperature generation_args["do_sample"] = True generation_args["top_p"] = top_p generation_args.update(inputs) # Generate generated_ids = id_model.generate(**generation_args) generated_texts = id_processor.batch_decode(generated_ids[:, generation_args["input_ids"].size(1):], skip_special_tokens=True) return generated_texts[0] @spaces.GPU def search(query: str, ds, images, k): # Load colpali model model_name = "vidore/colpali-v1.2" token = os.environ.get("HF_TOKEN") model = ColPali.from_pretrained( "vidore/colpaligemma-3b-pt-448-base", torch_dtype=torch.bfloat16, device_map="cuda", token = token).eval() model.load_adapter(model_name) model = model.eval() processor = AutoProcessor.from_pretrained(model_name, token = token) mock_image = Image.new("RGB", (448, 448), (255, 255, 255)) device = "cuda:0" if torch.cuda.is_available() else "cpu" if device != model.device: model.to(device) qs = [] with torch.no_grad(): batch_query = process_queries(processor, [query], mock_image) batch_query = {k: v.to(device) for k, v in batch_query.items()} embeddings_query = model(**batch_query) qs.extend(list(torch.unbind(embeddings_query.to("cpu")))) retriever_evaluator = CustomEvaluator(is_multi_vector=True) scores = retriever_evaluator.evaluate(qs, ds) top_k_indices = scores.argsort(axis=1)[0][-k:][::-1] results = [] for idx in top_k_indices: results.append((images[idx])) #, f"Page {idx}" del model del processor print("done") return results def index(files, ds): print("Converting files") images = convert_files(files) print(f"Files converted with {len(images)} images.") return index_gpu(images, ds) def convert_files(files): images = [] for f in files: images.extend(convert_from_path(f, thread_count=4)) if len(images) >= 150: raise gr.Error("The number of images in the dataset should be less than 150.") return images @spaces.GPU def index_gpu(images, ds): """Example script to run inference with ColPali""" # Load colpali model model_name = "vidore/colpali-v1.2" token = os.environ.get("HF_TOKEN") model = ColPali.from_pretrained( "vidore/colpaligemma-3b-pt-448-base", torch_dtype=torch.bfloat16, device_map="cuda", token = token).eval() model.load_adapter(model_name) model = model.eval() processor = AutoProcessor.from_pretrained(model_name, token = token) mock_image = Image.new("RGB", (448, 448), (255, 255, 255)) # run inference - docs dataloader = DataLoader( images, batch_size=4, shuffle=False, collate_fn=lambda x: process_images(processor, x), ) device = "cuda:0" if torch.cuda.is_available() else "cpu" if device != model.device: model.to(device) for batch_doc in tqdm(dataloader): with torch.no_grad(): batch_doc = {k: v.to(device) for k, v in batch_doc.items()} embeddings_doc = model(**batch_doc) ds.extend(list(torch.unbind(embeddings_doc.to("cpu")))) del model del processor print("done") return f"Uploaded and converted {len(images)} pages", ds, images @spaces.GPU def answer_gpu(): return 0 def get_example(): return [[["climate_youth_magazine.pdf"], "How much tropical forest is cut annually ?"]] with gr.Blocks(theme=gr.themes.Soft()) as demo: gr.Markdown("# ColPali: Efficient Document Retrieval with Vision Language Models 📚") with gr.Row(): with gr.Column(scale=2): gr.Markdown("## 1️⃣ Upload PDFs") file = gr.File(file_types=["pdf"], file_count="multiple", label="Upload PDFs") convert_button = gr.Button("🔄 Index documents") message = gr.Textbox("Files not yet uploaded", label="Status") embeds = gr.State(value=[]) imgs = gr.State(value=[]) img_chunk = gr.State(value=[]) with gr.Column(scale=3): gr.Markdown("## 2️⃣ Search") query = gr.Textbox(placeholder="Enter your query here", label="Query") k = gr.Slider(minimum=1, maximum=10, step=1, label="Number of results", value=5) # with gr.Row(): # gr.Examples( # examples=get_example(), # inputs=[file, query], # ) # Define the actions search_button = gr.Button("🔍 Search", variant="primary") output_gallery = gr.Gallery(label="Retrieved Documents", height=600, show_label=True) convert_button.click(index, inputs=[file, embeds], outputs=[message, embeds, imgs]) search_button.click(search, inputs=[query, embeds, imgs, k], outputs=[output_gallery]) answer_button = gr.Button("Answer", variant="primary") output = gr.Textbox(label="Output") answer_button.click(model_inference, inputs=[output_gallery, query], outputs=output) if __name__ == "__main__": demo.queue(max_size=10).launch(debug=True)