app.py

import base64
import io
import os
import random

import gradio as gr
import numpy as np
import torch
from colpali_engine.models import ColPali, ColPaliProcessor
from datasets import load_dataset
from dotenv import load_dotenv
from PIL import Image, ImageDraw
from qdrant_client import QdrantClient
from qdrant_client.http import models
from tqdm import tqdm
from gradio.themes.base import Base
from gradio.themes.utils import colors, fonts, sizes
from typing import Iterable

# Load environment variables
load_dotenv()

# Set up device
if torch.cuda.is_available():
    device = "cuda:0"
elif torch.backends.mps.is_available():
    device = "mps"
else:
    device = "cpu"
print(f"Using device: {device}")

# Set up Qdrant client
QDRANT_URL = os.getenv("QDRANT_URL")
QDRANT_API_KEY = os.getenv("QDRANT_API_KEY")
qdrant_client = QdrantClient(
    url=QDRANT_URL,
    api_key=QDRANT_API_KEY,
    prefer_grpc=True,
)


# Load dataset and set up model
dataset = load_dataset("davanstrien/ufo-ColPali", split="train")
collection_name = "ufo"

model_name = "davanstrien/finetune_colpali_v1_2-ufo-4bit"
colpali_model = ColPali.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map=device,
)
colpali_processor = ColPaliProcessor.from_pretrained(
    "vidore/colpaligemma-3b-pt-448-base"
)


def search_images_by_text(query_text, top_k=5):
    with torch.no_grad():
        batch_query = colpali_processor.process_queries([query_text]).to(
            colpali_model.device
        )
        query_embedding = colpali_model(**batch_query)

    multivector_query = query_embedding[0].cpu().float().numpy().tolist()
    results = qdrant_client.query_points(
        collection_name=collection_name,
        query=multivector_query,
        limit=top_k,
        timeout=60,
    )
    print(results)
    return results


def search_by_text_and_return_images(query_text, top_k=5):
    results = search_images_by_text(query_text, top_k)
    print(results)
    row_ids = [r.id for r in results.points]
    subset = dataset.select(row_ids)
    return list(subset["image"])


class Geocities90s(Base):
    def __init__(
        self,
        *,
        primary_hue: colors.Color | str = colors.yellow,
        secondary_hue: colors.Color | str = colors.purple,
        neutral_hue: colors.Color | str = colors.gray,
        font: fonts.Font | str = fonts.GoogleFont("Comic Neue"),
        font_mono: fonts.Font | str = fonts.GoogleFont("VT323"),
    ):
        super().__init__(
            primary_hue=primary_hue,
            secondary_hue=secondary_hue,
            neutral_hue=neutral_hue,
            font=(font, "Comic Sans MS", "ui-sans-serif", "sans-serif"),
            font_mono=(font_mono, "Courier New", "monospace"),
        )
        self.set(
            body_background_fill="url('https://web.archive.org/web/20091020152706/http://hk.geocities.com/neonlightfantasy/image/stars.gif')",
            button_primary_background_fill="linear-gradient(90deg, *primary_500, *secondary_500)",
            button_primary_background_fill_hover="linear-gradient(90deg, *secondary_500, *primary_500)",
            button_primary_text_color="*neutral_50",
        )


geocities90s = Geocities90s()

css = """
body {
    margin: 0;
    padding: 0;
    color: #00ff00;
    font-family: 'Comic Sans MS', cursive;
}
.gradio-container {
    background-image: url('https://i.ytimg.com/vi/5WapcCXEcXA/maxresdefault.jpg');
    background-repeat: repeat;
    background-size: 300px 300px;
}
h1 {
    text-align: center;
    color: #ff00ff;
    text-shadow: 2px 2px #000000;
    font-size: 36px;
}
.yellow-text {
    color: #ffff00;
    text-shadow: 2px 2px #000000;
    font-weight: bold;
}
"""

demo = gr.Interface(
    fn=search_by_text_and_return_images,
    inputs=[
        gr.Textbox(
            label="Enter your cosmic query",
            placeholder="e.g., alien abduction, crop circles",
        ),
        gr.Slider(
            minimum=1,
            maximum=10,
            step=1,
            label="Number of classified documents",
            value=5,
        ),
    ],
    outputs=gr.Gallery(label="Declassified UFO Sightings", elem_id="gallery"),
    title="🛸 Top Secret UFO Document Search 🛸",
    description="<marquee direction='left' scrollamount='5' class='yellow-text'>Uncover the truth that's out there! The government doesn't want you to know!</marquee>",
    css=css,
    allow_flagging="never",
    theme=geocities90s,
)

if __name__ == "__main__":
    demo.launch()