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 Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor from qwen_vl_utils import process_vision_info 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, ): print(type(images)) print(images[0]) images = Image.open(images[0][0]) print(images) print(type(images)) # 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) messages = [ { "role": "user", "content": [ { "type": "image", "image": images, }, {"type": "text", "text": text}, ], } ] # 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 ) return output_text[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 def get_example(): return [ [["RAPPORT_DEVELOPPEMENT_DURABLE_2019.pdf"], "Quels sont les 4 axes majeurs des achats?"], [["RAPPORT_DEVELOPPEMENT_DURABLE_2019.pdf"], "Quelles sont les actions entreprise en Afrique du Sud?"], [["RAPPORT_DEVELOPPEMENT_DURABLE_2019.pdf"], "fais moi un tableau de la répartition homme femme"], ] with gr.Blocks(theme=gr.themes.Soft()) as demo: gr.Markdown("# ColPali + Idefics3: 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") message = gr.Textbox("Files not yet uploaded", label="Status") convert_button = gr.Button("🔄 Index documents") 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) search_button = gr.Button("🔍 Search", variant="primary") with gr.Row(): gr.Examples( examples=get_example(), inputs=[file, query], ) # Define the actions 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.Markdown(label="Output") answer_button.click(model_inference, inputs=[output_gallery, query], outputs=output) if __name__ == "__main__": demo.queue(max_size=10).launch(debug=True)