app.py

from time import time
from io import BytesIO
import torch
import streamlit as st
import streamlit.components.v1 as components
import numpy as np
import torch
import logging
from os import environ
from transformers import OwlViTProcessor, OwlViTForObjectDetection
from bot import Bot, Message
from myscaledb import Client
from classifier import Classifier, prompt2vec, tune, SplitLayer
from query_model import simple_query, topk_obj_query, rev_query
from card_model import card, obj_card, style
from box_utils import postprocess

environ["TOKENIZERS_PARALLELISM"] = "true"

OBJ_DB_NAME = "mqdb_demo.coco_owl_vit_b_32_objects"
IMG_DB_NAME = "mqdb_demo.coco_owl_vit_b_32_images"
MODEL_ID = "google/owlvit-base-patch32"
DIMS = 512

qtime = 0


def build_model(name="google/owlvit-base-patch32"):
    """Model builder function

    Args:
        name (str, optional): Name for HuggingFace OwlViT model. Defaults to "google/owlvit-base-patch32".

    Returns:
        (model, processor): OwlViT model and its processor for both image and text
    """
    device = "cpu"
    if torch.cuda.is_available():
        device = "cuda"
    model = OwlViTForObjectDetection.from_pretrained(name).to(device)
    processor = OwlViTProcessor.from_pretrained(name)
    return model, processor


@st.experimental_singleton(show_spinner=False)
def init_owlvit():
    """Initialize OwlViT Model

    Returns:
        model, processor
    """
    model, processor = build_model(MODEL_ID)
    return model, processor


@st.experimental_singleton(show_spinner=False)
def init_db():
    """Initialize the Database Connection

    Returns:
        meta_field: Meta field that records if an image is viewed or not
        client:     Database connection object
    """
    meta = []
    client = Client(
        url=st.secrets["DB_URL"], user=st.secrets["USER"], password=st.secrets["PASSWD"]
    )
    # We can check if the connection is alive
    assert client.is_alive()
    return meta, client


def refresh_index():
    """Clean the session"""
    del st.session_state["meta"]
    st.session_state.meta = []
    st.session_state.query_num = 0
    logging.info(f"Refresh for '{st.session_state.meta}'")
    # Need to clear singleton function with streamlit API
    init_db.clear()
    # refresh session states
    st.session_state.meta, st.session_state.index = init_db()
    if "clf" in st.session_state:
        del st.session_state.clf
    if "xq" in st.session_state:
        del st.session_state.xq
    if "topk_img_id" in st.session_state:
        del st.session_state.topk_img_id


def query(xq, exclude_list=None):
    """Query matched w.r.t a given vector

    In this part, we will retrieve A LOT OF data from the server,
    including TopK boxes and their embeddings, the counterpart of non-TopK boxes in TopK images.

    Args:
        xq (numpy.ndarray or list of floats): Query vector

    Returns:
        matches: list of Records object. Keys referrring to selected columns group by images.
                 Exclude the user's viewlist.
        img_matches: list of Records object. Containing other non-TopK but hit objects among TopK images.
        side_matches: list of Records object. Containing REAL TopK objects disregard the user's view history
    """
    attempt = 0
    xq = xq
    xq = xq / np.linalg.norm(xq, axis=-1, ord=2, keepdims=True)
    status_bar = [st.empty(), st.empty()]
    status_bar[0].write("Retrieving Another TopK Images...")
    pbar = status_bar[1].progress(0)
    while attempt < 3:
        try:
            matches = topk_obj_query(
                st.session_state.index,
                xq,
                IMG_DB_NAME,
                OBJ_DB_NAME,
                exclude_list=exclude_list,
                topk=5000,
            )
            img_ids = [r["img_id"] for r in matches]
            if "topk_img_id" not in st.session_state:
                st.session_state.topk_img_id = img_ids
            status_bar[0].write("Retrieving TopK Images...")
            pbar.progress(25)
            o_matches = rev_query(
                st.session_state.index,
                xq,
                st.session_state.topk_img_id,
                IMG_DB_NAME,
                OBJ_DB_NAME,
                thresh=0.1,
            )
            status_bar[0].write("Retrieving TopKs Objects...")
            pbar.progress(50)
            side_matches = simple_query(
                st.session_state.index,
                xq,
                IMG_DB_NAME,
                OBJ_DB_NAME,
                thresh=-1,
                topk=5000,
            )
            status_bar[0].write("Retrieving Non-TopK in Another TopK Images...")
            pbar.progress(75)
            if len(img_ids) > 0:
                img_matches = rev_query(
                    st.session_state.index,
                    xq,
                    img_ids,
                    IMG_DB_NAME,
                    OBJ_DB_NAME,
                    thresh=0.1,
                )
            else:
                img_matches = []
            status_bar[0].write("DONE!")
            pbar.progress(100)
            break
        except Exception as e:
            # force reload if we have trouble on connections or something else
            logging.warning(str(e))
            st.session_state.meta, st.session_state.index = init_db()
            attempt += 1
            matches = []
    _ = [s.empty() for s in status_bar]
    if len(matches) == 0:
        logging.error(f"No matches found for '{OBJ_DB_NAME}'")
    return matches, img_matches, side_matches, o_matches


@st.experimental_singleton(show_spinner=False)
def init_random_query():
    """Initialize a random query vector

    Returns:
        xq: a random vector
    """
    xq = np.random.rand(1, DIMS)
    xq /= np.linalg.norm(xq, keepdims=True, axis=-1)
    return xq


def submit(meta):
    """Tune the model w.r.t given score from user."""
    # Only updating the meta if the train button is pressed
    st.session_state.meta.extend(meta)
    st.session_state.step += 1
    matches = st.session_state.matched_boxes
    X, y = list(
        zip(
            *(
                (
                    v[-1],
                    st.session_state.text_prompts.index(st.session_state[f"label-{i}"]),
                )
                for i, v in matches.items()
            )
        )
    )
    st.session_state.xq = tune(
        st.session_state.clf, X, y, iters=int(st.session_state.iters)
    )
    (
        st.session_state.matches,
        st.session_state.img_matches,
        st.session_state.side_matches,
        st.session_state.o_matches,
    ) = query(st.session_state.xq, st.session_state.meta)


# st.set_page_config(layout="wide")
# To hack the streamlit style we define our own style.
# Boxes are drawn in SVGs.
st.write(style(), unsafe_allow_html=True)

bot = Bot(app_name="HF OwlViT", enabled=True, bot_key=st.secrets['BOT_KEY'])
try:
    with st.spinner("Connecting DB..."):
        st.session_state.meta, st.session_state.index = init_db()

    with st.spinner("Loading Models..."):
        # Initialize model
        model, tokenizer = init_owlvit()
    # If its a fresh start... (query not set)
    if "xq" not in st.session_state:
        with st.container():
            st.title("Object Detection Safari")
            start = [st.empty() for _ in range(8)]
            start[0].info(
                """
                        We extracted boxes from **287,104** images in COCO Dataset, including its train / val / test / 
                        unlabeled images, collecting **165,371,904 boxes** which are then filtered with common prompts. 
                        You can search with almost any words or phrases you can think of. Please enjoy your journey of 
                        an adventure to COCO.
                        """
            )
            prompt = start[1].text_input(
                "Prompt:",
                value="",
                placeholder="Examples: football, billboard, stop sign, watermark ...",
            )
            with start[2].container():
                st.write(
                    "You can search with multiple keywords. Plese separate with commas but with no space."
                )
                st.write("For example: `cat,dog,tree`")
                st.markdown(
                    """
                            <p style="color:gray;"> Don\'t know what to search? Try <b>Random</b>!</p>
                            """,
                    unsafe_allow_html=True,
                )

            upld_model = start[4].file_uploader(
                "Or you can upload your previous run!", type="onnx"
            )
            upld_btn = start[5].button(
                "Use Loaded Weights", disabled=upld_model is None, on_click=refresh_index
            )

            with start[3]:
                col = st.columns(8)
                has_no_prompt = len(prompt) == 0 and upld_model is None
                prompt_xq = col[6].button(
                    "Prompt", disabled=len(prompt) == 0, on_click=refresh_index
                )
                random_xq = col[7].button(
                    "Random", disabled=not has_no_prompt, on_click=refresh_index
                )
            matches = []
            img_matches = []
            if random_xq:
                xq = init_random_query()
                st.session_state.xq = xq
                prompt = "unknown"
                st.session_state.text_prompts = prompt.split(",") + ["none"]
                _ = [elem.empty() for elem in start]
                t0 = time()
                (
                    st.session_state.matches,
                    st.session_state.img_matches,
                    st.session_state.side_matches,
                    st.session_state.o_matches,
                ) = query(st.session_state.xq, st.session_state.meta)
                t1 = time()
                qtime = (t1 - t0) * 1000
            elif prompt_xq or upld_btn:
                if upld_model is not None:
                    import onnx
                    from onnx import numpy_helper

                    _model = onnx.load(upld_model)
                    st.session_state.text_prompts = [
                        node.name for node in _model.graph.output
                    ] + ["none"]
                    weights = _model.graph.initializer
                    xq = numpy_helper.to_array(weights[0]).T
                    assert (
                        xq.shape[0] == len(st.session_state.text_prompts) - 1
                        and xq.shape[1] == DIMS
                    )
                    st.session_state.xq = xq
                    _ = [elem.empty() for elem in start]
                else:
                    logging.info(f"Input prompt is {prompt}")
                    st.session_state.text_prompts = prompt.split(",") + ["none"]
                    input_ids, xq = prompt2vec(
                        st.session_state.text_prompts[:-1], model, tokenizer
                    )
                    st.session_state.xq = xq
                    _ = [elem.empty() for elem in start]
                t0 = time()
                (
                    st.session_state.matches,
                    st.session_state.img_matches,
                    st.session_state.side_matches,
                    st.session_state.o_matches,
                ) = query(st.session_state.xq, st.session_state.meta)
                t1 = time()
                qtime = (t1 - t0) * 1000

    # If its not a fresh start (query is set)
    if "xq" in st.session_state:
        o_matches = st.session_state.o_matches
        side_matches = st.session_state.side_matches
        img_matches = st.session_state.img_matches
        matches = st.session_state.matches
        # initialize classifier
        if "clf" not in st.session_state:
            st.session_state.clf = Classifier(st.session_state.xq)
            st.session_state.step = 0
        if qtime > 0:
            st.info(
                "Query done in {0:.2f} ms and returned {1:d} images with {2:d} boxes".format(
                    qtime,
                    len(matches),
                    sum(
                        [
                            len(m["box_id"]) + len(im["box_id"])
                            for m, im in zip(matches, img_matches)
                        ]
                    ),
                )
            )

        # export the model into executable ONNX
        st.session_state.dnld_model = BytesIO()
        torch.onnx.export(
            torch.nn.Sequential(st.session_state.clf.model, SplitLayer()),
            torch.zeros([1, len(st.session_state.xq[0])]),
            st.session_state.dnld_model,
            input_names=["input"],
            output_names=st.session_state.text_prompts[:-1],
        )

        dnld_nam = st.text_input(
            "Download Name:",
            f'{("_".join([i.replace(" ", "-") for i in st.session_state.text_prompts[:-1]]) if "text_prompts" in st.session_state else "model")}.onnx',
            max_chars=50,
        )
        dnld_btn = st.download_button(
            "Download your classifier!", st.session_state.dnld_model, dnld_nam
        )
        # build up a sidebar to display REAL TopK in DB
        # this will change during user's finetune. But sometime it would lead to bad results
        side_bar_len = min(240 // len(st.session_state.text_prompts), 120)
        with st.sidebar:
            with st.expander("Top-K Images"):
                with st.container():
                    boxes_w_img, _ = postprocess(
                        o_matches, st.session_state.text_prompts, None
                    )
                    boxes_w_img = sorted(boxes_w_img, key=lambda x: x[4], reverse=True)
                    for img_id, img_url, img_w, img_h, img_score, boxes in boxes_w_img:
                        args = img_url, img_w, img_h, boxes
                        st.write(card(*args), unsafe_allow_html=True)

            with st.expander("Top-K Objects", expanded=True):
                side_cols = st.columns(len(st.session_state.text_prompts[:-1]))
                for _cols, m in zip(side_cols, side_matches):
                    with _cols.container():
                        for cx, cy, w, h, logit, img_url, img_w, img_h in zip(
                            m["cx"],
                            m["cy"],
                            m["w"],
                            m["h"],
                            m["logit"],
                            m["img_url"],
                            m["img_w"],
                            m["img_h"],
                        ):
                            st.write(
                                "{:s}: {:.4f}".format(
                                    st.session_state.text_prompts[m["label"]], logit
                                )
                            )
                            _html = obj_card(
                                img_url, img_w, img_h, cx, cy, w, h, dst_len=side_bar_len
                            )
                            components.html(_html, side_bar_len, side_bar_len)
        with st.container():
            # Here let the user interact with batch labeling
            with st.form("batch", clear_on_submit=False):
                col = st.columns([1, 9])

                # If there is nothing to show about
                if len(matches) <= 0:
                    st.warning(
                        "Oops! We didn't find anything relevant to your query! Pleas try another one :/"
                    )
                else:
                    st.session_state.iters = st.slider(
                        "Number of Iterations to Update",
                        min_value=0,
                        max_value=10,
                        step=1,
                        value=2,
                    )
                # No matter what happened the user wants a way back
                col[1].form_submit_button("Choose a new prompt", on_click=refresh_index)

                # If there are things to show
                if len(matches) > 0:
                    with st.container():
                        prompt_labels = st.session_state.text_prompts

                        # Post processing boxes regarding to their score, intersection
                        boxes_w_img, meta = postprocess(
                            matches, st.session_state.text_prompts, img_matches
                        )

                        # Sort the result according to their relavancy
                        boxes_w_img = sorted(boxes_w_img, key=lambda x: x[4], reverse=True)

                        st.session_state.matched_boxes = {}
                        # For each images in the retrieved images, DISPLAY
                        for img_id, img_url, img_w, img_h, img_score, boxes in boxes_w_img:

                            # prepare inputs for training
                            st.session_state.matched_boxes.update({b[0]: b for b in boxes})
                            args = img_url, img_w, img_h, boxes

                            # display boxes
                            with st.expander(
                                "{:s}: {:.4f}".format(img_id, img_score), expanded=True
                            ):
                                ind_b = 0
                                # 4 columns: (img, obj, obj, obj)
                                img_row = st.columns([4, 2, 2, 2])
                                img_row[0].write(card(*args), unsafe_allow_html=True)
                                # crop objects out of the original image
                                for b in boxes:
                                    _id, cx, cy, w, h, label, logit, is_selected, _ = b
                                    with img_row[1 + ind_b % 3].container():
                                        st.write("{:s}: {:.4f}".format(label, logit))
                                        # quite hacky: with streamlit components API
                                        _html = obj_card(
                                            img_url, img_w, img_h, *b[1:5], dst_len=120
                                        )
                                        components.html(_html, 120, 120)
                                        # the user will choose the right label of the given object
                                        st.selectbox(
                                            "Class",
                                            prompt_labels,
                                            index=prompt_labels.index(label),
                                            key=f"label-{_id}",
                                        )
                                    ind_b += 1
                    col[0].form_submit_button("Train!", on_click=lambda: submit(meta))
except Exception as e:
    msg = Message()
    msg.content = str(e.with_traceback(None))
    msg.type_hint = str(type(e).__name__)
    bot.incident(msg)