import os

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


def search(query: str, ds, images, k):
    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):
    """Example script to run inference with ColPali"""
    images = []
    for f in files:
        images.extend(convert_from_path(f))

    if len(images) >= 150:
        raise gr.Error("The number of images in the dataset should be less than 150.")

    # run inference - docs
    dataloader = DataLoader(
        images,
        batch_size=4,
        shuffle=False,
        collate_fn=lambda x: process_images(processor, x),
    )
    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

cache_dir = os.path.join(os.getcwd(), "data/", "model_cache/")
# Load model
model_name = "vidore/colpali"
token = os.environ.get("HF_TOKEN")
model = ColPali.from_pretrained(
    "google/paligemma-3b-mix-448", torch_dtype=torch.bfloat16, device_map="cuda", token = token, cache_dir=cache_dir
).eval()

model.load_adapter(model_name)
processor = AutoProcessor.from_pretrained(model_name, cache_dir=cache_dir, token = token)

device = model.device

mock_image = Image.new("RGB", (448, 448), (255, 255, 255))

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# ColPali: Efficient Document Retrieval with Vision Language Models 📚")
    gr.Markdown("""Demo to test ColPali on PDF documents. The inference code is based on the [ViDoRe benchmark](https://github.com/illuin-tech/vidore-benchmark).

    ColPali is model implemented from the [ColPali paper](https://arxiv.org/abs/2407.01449).

    This demo allows you to upload PDF files and search for the most relevant pages based on your 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")

            convert_button = gr.Button("🔄 Convert and upload")
            message = gr.Textbox("Files not yet uploaded", label="Status")
            embeds = gr.State(value=[])
            imgs = 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=3)

    # 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])

if __name__ == "__main__":
    demo.queue(max_size=10).launch(debug=True, server_name="0.0.0.0", server_port=7861)