app.py

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 uniquement avec les informations du document fourni.", 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(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids
    EOS_WORDS_IDS = [id_processor.tokenizer.eos_token_id]
    print(type(images))
    print(images[0])
    images = Image.open(images[0][0])
    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


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 sur 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():
        gr.Examples(
            examples=get_example(),
            inputs=[file, query],
        )

    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")
   

    # 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)