# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

# ---------------------------------------------------------------------------------------------------------------------------------------------------
"""
CLIP Interrogator made by @pharmapsychotic modified to work with our WebUI.

# CLIP Interrogator by @pharmapsychotic
Twitter: https://twitter.com/pharmapsychotic
Github: https://github.com/pharmapsychotic/clip-interrogator

Description:
What do the different OpenAI CLIP models see in an image? What might be a good text prompt to create similar images using CLIP guided diffusion
or another text to image model? The CLIP Interrogator is here to get you answers!

Please consider buying him a coffee via [ko-fi](https://ko-fi.com/pharmapsychotic) or following him on [twitter](https://twitter.com/pharmapsychotic).

And if you're looking for more Ai art tools check out my [Ai generative art tools list](https://pharmapsychotic.com/tools.html).

"""
# ---------------------------------------------------------------------------------------------------------------------------------------------------

# base webui import and utils.
from sd_utils import st, logger, server_state, server_state_lock, random

# streamlit imports

# streamlit components section

# other imports

import clip
import open_clip
import gc
import os
import pandas as pd

# import requests
import torch
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from ldm.models.blip import blip_decoder

# import hashlib

# end of imports
# ---------------------------------------------------------------------------------------------------------------

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
blip_image_eval_size = 512

st.session_state["log"] = []


def load_blip_model():
    logger.info("Loading BLIP Model")
    if "log" not in st.session_state:
        st.session_state["log"] = []

    st.session_state["log"].append("Loading BLIP Model")
    st.session_state["log_message"].code(
        "\n".join(st.session_state["log"]), language=""
    )

    if "blip_model" not in server_state:
        with server_state_lock["blip_model"]:
            server_state["blip_model"] = blip_decoder(
                pretrained="models/blip/model__base_caption.pth",
                image_size=blip_image_eval_size,
                vit="base",
                med_config="configs/blip/med_config.json",
            )

            server_state["blip_model"] = server_state["blip_model"].eval()

            server_state["blip_model"] = server_state["blip_model"].to(device).half()

            logger.info("BLIP Model Loaded")
            st.session_state["log"].append("BLIP Model Loaded")
            st.session_state["log_message"].code(
                "\n".join(st.session_state["log"]), language=""
            )
    else:
        logger.info("BLIP Model already loaded")
        st.session_state["log"].append("BLIP Model already loaded")
        st.session_state["log_message"].code(
            "\n".join(st.session_state["log"]), language=""
        )


def generate_caption(pil_image):
    load_blip_model()

    gpu_image = (
        transforms.Compose(
            [  # type: ignore
                transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),  # type: ignore
                transforms.ToTensor(),  # type: ignore
                transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),  # type: ignore
            ]
        )(pil_image)
        .unsqueeze(0)
        .to(device)
        .half()
    )

    with torch.no_grad():
        caption = server_state["blip_model"].generate(
            gpu_image, sample=False, num_beams=3, max_length=20, min_length=5
        )

    return caption[0]


def load_list(filename):
    with open(filename, "r", encoding="utf-8", errors="replace") as f:
        items = [line.strip() for line in f.readlines()]
        return items


def rank(model, image_features, text_array, top_count=1):
    top_count = min(top_count, len(text_array))
    text_tokens = clip.tokenize([text for text in text_array]).cuda()
    with torch.no_grad():
        text_features = model.encode_text(text_tokens).float()
    text_features /= text_features.norm(dim=-1, keepdim=True)

    similarity = torch.zeros((1, len(text_array))).to(device)
    for i in range(image_features.shape[0]):
        similarity += (
            100.0 * image_features[i].unsqueeze(0) @ text_features.T
        ).softmax(dim=-1)
    similarity /= image_features.shape[0]

    top_probs, top_labels = similarity.cpu().topk(top_count, dim=-1)
    return [
        (text_array[top_labels[0][i].numpy()], (top_probs[0][i].numpy() * 100))
        for i in range(top_count)
    ]


def clear_cuda():
    torch.cuda.empty_cache()
    gc.collect()


def batch_rank(
    model,
    image_features,
    text_array,
    batch_size=st.session_state["defaults"].img2txt.batch_size,
):
    batch_size = min(batch_size, len(text_array))
    batch_count = int(len(text_array) / batch_size)
    batches = [
        text_array[i * batch_size : (i + 1) * batch_size] for i in range(batch_count)
    ]
    ranks = []
    for batch in batches:
        ranks += rank(model, image_features, batch)
    return ranks


def interrogate(image, models):
    load_blip_model()

    logger.info("Generating Caption")
    st.session_state["log"].append("Generating Caption")
    st.session_state["log_message"].code(
        "\n".join(st.session_state["log"]), language=""
    )
    caption = generate_caption(image)

    if st.session_state["defaults"].general.optimized:
        del server_state["blip_model"]
        clear_cuda()

    logger.info("Caption Generated")
    st.session_state["log"].append("Caption Generated")
    st.session_state["log_message"].code(
        "\n".join(st.session_state["log"]), language=""
    )

    if len(models) == 0:
        logger.info(f"\n\n{caption}")
        return

    table = []
    bests = [[("", 0)]] * 7

    logger.info("Ranking Text")
    st.session_state["log"].append("Ranking Text")
    st.session_state["log_message"].code(
        "\n".join(st.session_state["log"]), language=""
    )

    for model_name in models:
        with torch.no_grad(), torch.autocast("cuda", dtype=torch.float16):
            logger.info(f"Interrogating with {model_name}...")
            st.session_state["log"].append(f"Interrogating with {model_name}...")
            st.session_state["log_message"].code(
                "\n".join(st.session_state["log"]), language=""
            )

            if model_name not in server_state["clip_models"]:
                if not st.session_state["defaults"].img2txt.keep_all_models_loaded:
                    model_to_delete = []
                    for model in server_state["clip_models"]:
                        if model != model_name:
                            model_to_delete.append(model)
                    for model in model_to_delete:
                        del server_state["clip_models"][model]
                        del server_state["preprocesses"][model]
                        clear_cuda()
                if model_name == "ViT-H-14":
                    (
                        server_state["clip_models"][model_name],
                        _,
                        server_state["preprocesses"][model_name],
                    ) = open_clip.create_model_and_transforms(
                        model_name,
                        pretrained="laion2b_s32b_b79k",
                        cache_dir="models/clip",
                    )
                elif model_name == "ViT-g-14":
                    (
                        server_state["clip_models"][model_name],
                        _,
                        server_state["preprocesses"][model_name],
                    ) = open_clip.create_model_and_transforms(
                        model_name,
                        pretrained="laion2b_s12b_b42k",
                        cache_dir="models/clip",
                    )
                else:
                    (
                        server_state["clip_models"][model_name],
                        server_state["preprocesses"][model_name],
                    ) = clip.load(
                        model_name, device=device, download_root="models/clip"
                    )
                server_state["clip_models"][model_name] = (
                    server_state["clip_models"][model_name].cuda().eval()
                )

            images = server_state["preprocesses"][model_name](image).unsqueeze(0).cuda()

            image_features = (
                server_state["clip_models"][model_name].encode_image(images).float()
            )

            image_features /= image_features.norm(dim=-1, keepdim=True)

            if st.session_state["defaults"].general.optimized:
                clear_cuda()

            ranks = []
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    server_state["mediums"],
                )
            )
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    ["by " + artist for artist in server_state["artists"]],
                )
            )
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    server_state["trending_list"],
                )
            )
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    server_state["movements"],
                )
            )
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    server_state["flavors"],
                )
            )
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["domains"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["subreddits"]))
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    server_state["techniques"],
                )
            )
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    server_state["tags"],
                )
            )

            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["genres"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["styles"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["subjects"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["colors"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["moods"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["themes"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["keywords"]))

            # print (bests)
            # print (ranks)

            for i in range(len(ranks)):
                confidence_sum = 0
                for ci in range(len(ranks[i])):
                    confidence_sum += ranks[i][ci][1]
                if confidence_sum > sum(bests[i][t][1] for t in range(len(bests[i]))):
                    bests[i] = ranks[i]

            for best in bests:
                best.sort(key=lambda x: x[1], reverse=True)
                # prune to 3
                best = best[:3]

            row = [model_name]

            for r in ranks:
                row.append(", ".join([f"{x[0]} ({x[1]:0.1f}%)" for x in r]))

            # for rank in ranks:
            #    rank.sort(key=lambda x: x[1], reverse=True)
            #    row.append(f'{rank[0][0]} {rank[0][1]:.2f}%')

            table.append(row)

            if st.session_state["defaults"].general.optimized:
                del server_state["clip_models"][model_name]
                gc.collect()

    st.session_state["prediction_table"][
        st.session_state["processed_image_count"]
    ].dataframe(
        pd.DataFrame(
            table,
            columns=[
                "Model",
                "Medium",
                "Artist",
                "Trending",
                "Movement",
                "Flavors",
                "Techniques",
                "Tags",
            ],
        )
    )

    medium = bests[0][0][0]
    artist = bests[1][0][0]
    trending = bests[2][0][0]
    movement = bests[3][0][0]
    flavors = bests[4][0][0]
    # domains = bests[5][0][0]
    # subreddits = bests[6][0][0]
    techniques = bests[5][0][0]
    tags = bests[6][0][0]

    if caption.startswith(medium):
        st.session_state["text_result"][st.session_state["processed_image_count"]].code(
            f"\n\n{caption} {artist}, {trending}, {movement}, {techniques}, {flavors}, {tags}",
            language="",
        )
    else:
        st.session_state["text_result"][st.session_state["processed_image_count"]].code(
            f"\n\n{caption}, {medium} {artist}, {trending}, {movement}, {techniques}, {flavors}, {tags}",
            language="",
        )

    logger.info("Finished Interrogating.")
    st.session_state["log"].append("Finished Interrogating.")
    st.session_state["log_message"].code(
        "\n".join(st.session_state["log"]), language=""
    )


def img2txt():
    models = []

    if st.session_state["ViT-L/14"]:
        models.append("ViT-L/14")
    if st.session_state["ViT-H-14"]:
        models.append("ViT-H-14")
    if st.session_state["ViT-g-14"]:
        models.append("ViT-g-14")

    if st.session_state["ViTB32"]:
        models.append("ViT-B/32")
    if st.session_state["ViTB16"]:
        models.append("ViT-B/16")

    if st.session_state["ViTL14_336px"]:
        models.append("ViT-L/14@336px")
    if st.session_state["RN101"]:
        models.append("RN101")
    if st.session_state["RN50"]:
        models.append("RN50")
    if st.session_state["RN50x4"]:
        models.append("RN50x4")
    if st.session_state["RN50x16"]:
        models.append("RN50x16")
    if st.session_state["RN50x64"]:
        models.append("RN50x64")

    # if str(image_path_or_url).startswith('http://') or str(image_path_or_url).startswith('https://'):
    # image = Image.open(requests.get(image_path_or_url, stream=True).raw).convert('RGB')
    # else:
    # image = Image.open(image_path_or_url).convert('RGB')

    # thumb = st.session_state["uploaded_image"].image.copy()
    # thumb.thumbnail([blip_image_eval_size, blip_image_eval_size])
    # display(thumb)

    st.session_state["processed_image_count"] = 0

    for i in range(len(st.session_state["uploaded_image"])):
        interrogate(st.session_state["uploaded_image"][i].pil_image, models=models)
        # increase counter.
        st.session_state["processed_image_count"] += 1


#


def layout():
    # set_page_title("Image-to-Text - Stable Diffusion WebUI")
    # st.info("Under Construction. :construction_worker:")
    #
    if "clip_models" not in server_state:
        server_state["clip_models"] = {}
    if "preprocesses" not in server_state:
        server_state["preprocesses"] = {}
    data_path = "data/"
    if "artists" not in server_state:
        server_state["artists"] = load_list(
            os.path.join(data_path, "img2txt", "artists.txt")
        )
    if "flavors" not in server_state:
        server_state["flavors"] = random.choices(
            load_list(os.path.join(data_path, "img2txt", "flavors.txt")), k=2000
        )
    if "mediums" not in server_state:
        server_state["mediums"] = load_list(
            os.path.join(data_path, "img2txt", "mediums.txt")
        )
    if "movements" not in server_state:
        server_state["movements"] = load_list(
            os.path.join(data_path, "img2txt", "movements.txt")
        )
    if "sites" not in server_state:
        server_state["sites"] = load_list(
            os.path.join(data_path, "img2txt", "sites.txt")
        )
    # server_state["domains"] = load_list(os.path.join(data_path, 'img2txt', 'domains.txt'))
    # server_state["subreddits"] = load_list(os.path.join(data_path, 'img2txt', 'subreddits.txt'))
    if "techniques" not in server_state:
        server_state["techniques"] = load_list(
            os.path.join(data_path, "img2txt", "techniques.txt")
        )
    if "tags" not in server_state:
        server_state["tags"] = load_list(os.path.join(data_path, "img2txt", "tags.txt"))
    # server_state["genres"] = load_list(os.path.join(data_path, 'img2txt', 'genres.txt'))
    # server_state["styles"] = load_list(os.path.join(data_path, 'img2txt', 'styles.txt'))
    # server_state["subjects"] = load_list(os.path.join(data_path, 'img2txt', 'subjects.txt'))
    if "trending_list" not in server_state:
        server_state["trending_list"] = [site for site in server_state["sites"]]
        server_state["trending_list"].extend(
            ["trending on " + site for site in server_state["sites"]]
        )
        server_state["trending_list"].extend(
            ["featured on " + site for site in server_state["sites"]]
        )
        server_state["trending_list"].extend(
            [site + " contest winner" for site in server_state["sites"]]
        )
    with st.form("img2txt-inputs"):
        st.session_state["generation_mode"] = "img2txt"

        # st.write("---")
        # creating the page layout using columns
        col1, col2 = st.columns([1, 4], gap="large")

        with col1:
            st.session_state["uploaded_image"] = st.file_uploader(
                "Input Image",
                type=["png", "jpg", "jpeg", "jfif", "webp"],
                accept_multiple_files=True,
            )

            with st.expander("CLIP models", expanded=True):
                st.session_state["ViT-L/14"] = st.checkbox(
                    "ViT-L/14", value=True, help="ViT-L/14 model."
                )
                st.session_state["ViT-H-14"] = st.checkbox(
                    "ViT-H-14", value=False, help="ViT-H-14 model."
                )
                st.session_state["ViT-g-14"] = st.checkbox(
                    "ViT-g-14", value=False, help="ViT-g-14 model."
                )

            with st.expander("Others"):
                st.info(
                    "For DiscoDiffusion and JAX enable all the same models here as you intend to use when generating your images."
                )

                st.session_state["ViTL14_336px"] = st.checkbox(
                    "ViTL14_336px", value=False, help="ViTL14_336px model."
                )
                st.session_state["ViTB16"] = st.checkbox(
                    "ViTB16", value=False, help="ViTB16 model."
                )
                st.session_state["ViTB32"] = st.checkbox(
                    "ViTB32", value=False, help="ViTB32 model."
                )
                st.session_state["RN50"] = st.checkbox(
                    "RN50", value=False, help="RN50 model."
                )
                st.session_state["RN50x4"] = st.checkbox(
                    "RN50x4", value=False, help="RN50x4 model."
                )
                st.session_state["RN50x16"] = st.checkbox(
                    "RN50x16", value=False, help="RN50x16 model."
                )
                st.session_state["RN50x64"] = st.checkbox(
                    "RN50x64", value=False, help="RN50x64 model."
                )
                st.session_state["RN101"] = st.checkbox(
                    "RN101", value=False, help="RN101 model."
                )

            #
            # st.subheader("Logs:")

            st.session_state["log_message"] = st.empty()
            st.session_state["log_message"].code("", language="")

        with col2:
            st.subheader("Image")

            image_col1, image_col2 = st.columns([10, 25])
            with image_col1:
                st.form_submit_button(
                    "Update Preview Image",
                    help="Refresh the image preview to show your uploaded image instead of the default placeholder.",
                )

            if st.session_state["uploaded_image"]:
                # print (type(st.session_state["uploaded_image"]))
                # if len(st.session_state["uploaded_image"]) == 1:
                st.session_state["input_image_preview"] = []
                st.session_state["input_image_preview_container"] = []
                st.session_state["prediction_table"] = []
                st.session_state["text_result"] = []

                for i in range(len(st.session_state["uploaded_image"])):
                    st.session_state["input_image_preview_container"].append(i)
                    st.session_state["input_image_preview_container"][i] = st.empty()

                    with st.session_state["input_image_preview_container"][
                        i
                    ].container():
                        col1_output, col2_output = st.columns([2, 10], gap="medium")
                        with col1_output:
                            st.session_state["input_image_preview"].append(i)
                            st.session_state["input_image_preview"][i] = st.empty()
                            st.session_state["uploaded_image"][
                                i
                            ].pil_image = Image.open(
                                st.session_state["uploaded_image"][i]
                            ).convert(
                                "RGB"
                            )

                            st.session_state["input_image_preview"][i].image(
                                st.session_state["uploaded_image"][i].pil_image,
                                use_column_width=True,
                                clamp=True,
                            )

                    with st.session_state["input_image_preview_container"][
                        i
                    ].container():
                        with col2_output:
                            st.session_state["prediction_table"].append(i)
                            st.session_state["prediction_table"][i] = st.empty()
                            st.session_state["prediction_table"][i].table()

                            st.session_state["text_result"].append(i)
                            st.session_state["text_result"][i] = st.empty()
                            st.session_state["text_result"][i].code("", language="")

            else:
                # st.session_state["input_image_preview"].code('', language="")
                st.image("images/streamlit/img2txt_placeholder.png", clamp=True)

        with image_col2:
            #
            # Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
            # generate_col1.title("")
            # generate_col1.title("")
            generate_button = st.form_submit_button(
                "Generate!",
                help="Start interrogating the images to generate a prompt from each of the selected images",
            )

    if generate_button:
        # if model, pipe, RealESRGAN or GFPGAN is in st.session_state remove the model and pipe form session_state so that they are reloaded.
        if "model" in server_state and st.session_state["defaults"].general.optimized:
            del server_state["model"]
        if "pipe" in server_state and st.session_state["defaults"].general.optimized:
            del server_state["pipe"]
        if (
            "RealESRGAN" in server_state
            and st.session_state["defaults"].general.optimized
        ):
            del server_state["RealESRGAN"]
        if "GFPGAN" in server_state and st.session_state["defaults"].general.optimized:
            del server_state["GFPGAN"]

        # run clip interrogator
        img2txt()