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)


## 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]

# 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))

@spaces.GPU
def model_inference(
    images, text, assistant_prefix= None, decoding_strategy = "Greedy", temperature= 0.4, max_new_tokens=512,
    repetition_penalty=1.2, top_p=0.8
):
    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):

    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}"))

    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"""
    
    # 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"))))
    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)