import io
import logging
import os
import re
import time

import certifi
import fitz  # PyMuPDF
import gradio as gr
import pycurl
import spaces
import yaml
from gradio_modal import Modal
from htrflow.pipeline.pipeline import Pipeline
from htrflow.pipeline.steps import init_step
from htrflow.volume.volume import Collection
from PIL import Image

from app.pipelines import PIPELINES

logger = logging.getLogger(__name__)

# Max number of images a user can upload at once
MAX_IMAGES = int(os.environ.get("MAX_IMAGES", 5))

# Setup the cache directory to point to the directory where the example images
# are located. The images must lay in the cache directory because otherwise they
# have to be reuploaded when drag-and-dropped to the input image widget.
GRADIO_CACHE = ".gradio_cache"
EXAMPLES_DIRECTORY = os.path.join(GRADIO_CACHE, "examples")

if os.environ.get("GRADIO_CACHE_DIR", GRADIO_CACHE) != GRADIO_CACHE:
    os.environ["GRADIO_CACHE_DIR"] = GRADIO_CACHE
    logger.warning("Setting GRADIO_CACHE_DIR to '%s' (overriding a previous value).")


class PipelineWithProgress(Pipeline):
    @classmethod
    def from_config(cls, config: dict[str, str]):
        """Init pipeline from config, ensuring the correct subclass is instantiated."""
        return cls(
            [
                init_step(step["step"], step.get("settings", {}))
                for step in config["steps"]
            ]
        )

    def run(self, collection, start=0, progress=None):
        """
        Run pipeline on collection with Gradio progress support.
        If progress is provided, it updates the Gradio progress bar during execution.
        """
        total_steps = len(self.steps[start:])
        for i, step in enumerate(self.steps[start:]):
            step_name = f"{step} (step {start + i + 1} / {total_steps})"

            try:
                progress((i + 1) / total_steps, desc=f"Running {step_name}")
                collection = step.run(collection)

            except Exception:
                if self.pickle_path:
                    gr.Error(
                        f"HTRflow: Pipeline failed on step {step_name}. A backup collection is saved at {self.pickle_path}"
                    )
                else:
                    gr.Error(
                        f"HTRflow: Pipeline failed on step {step_name}",
                    )
                raise
        return collection


def pdf_to_images(pdf_path):
    """
    Convert a PDF file to a list of PIL Image objects using PyMuPDF.
    Extracts full-resolution images with no DPI adjustment.

    Args:
        pdf_path (str): Path to the PDF file

    Returns:
        list: List of PIL Image objects
    """
    # Open the PDF
    pdf_document = fitz.open(pdf_path)

    # List to store the images
    images = []

    # Iterate through each page
    for page_num in range(len(pdf_document)):
        # Get the page
        page = pdf_document[page_num]

        # Get the pixmap at default resolution
        pixmap = page.get_pixmap(alpha=False)

        # Convert pixmap to PIL Image
        img_data = pixmap.tobytes("jpeg")
        img = Image.open(io.BytesIO(img_data))

        # Add the image to our list
        images.append(img)

    # Close the PDF
    pdf_document.close()

    return images


@spaces.GPU
def run_htrflow(custom_template_yaml, batch_image_gallery, progress=gr.Progress()):
    """
    Executes the HTRflow pipeline based on the provided YAML configuration and batch images.
    Args:
        custom_template_yaml (str): YAML string specifying the HTRflow pipeline configuration.
        batch_image_gallery (list): List of uploaded images to process in the pipeline.
    Returns:
        tuple: A collection of processed items, list of exported file paths, and a Gradio update object.
    """

    if custom_template_yaml is None or len(custom_template_yaml) < 1:
        gr.Warning("HTRflow: Please insert a HTRflow-yaml template")
    try:
        config = yaml.safe_load(custom_template_yaml)
    except Exception as e:
        gr.Warning(f"HTRflow: Error loading YAML configuration: {e}")
        return gr.skip()

    progress(0, desc="HTRflow: Starting")
    time.sleep(0.3)

    if batch_image_gallery is None:
        gr.Warning("HTRflow: You must upload atleast 1 image or more")

    images = [temp_img[0] for temp_img in batch_image_gallery]

    collection = Collection(images)

    pipe = PipelineWithProgress.from_config(config)

    gr.Info(
        f"HTRflow: processing {len(images)} {'image' if len(images) == 1 else 'images'}."
    )
    progress(0.1, desc="HTRflow: Processing")

    collection.label = "demo_output"

    collection = pipe.run(collection, progress=progress)

    progress(1, desc="HTRflow: Finish, redirecting to 'Results tab'")
    time.sleep(2)
    gr.Info("Completed succesfully ✨")

    yield collection, gr.skip()


def get_pipeline_description(pipeline: str) -> str:
    """
    Get the description of the given pipeline
    """
    return PIPELINES[pipeline]["description"]


def get_yaml(pipeline: str) -> str:
    """
    Get the yaml file for the given pipeline

    Args:
        pipeline: Name of pipeline (must be a key in the PIPELINES directory)
    """
    with open(PIPELINES[pipeline]["file"], "r") as f:
        pipeline = f.read()
    return pipeline


def all_example_images() -> list[str]:
    """
    Get paths to all example images.
    """
    examples = []
    for pipeline in PIPELINES.values():
        for example in pipeline.get("examples", []):
            examples.append(os.path.join(EXAMPLES_DIRECTORY, example))
    return examples


def get_selected_example_image(event: gr.SelectData) -> str:
    """
    Get path to the selected example image.
    """
    return [event.value["image"]["path"]]


def get_selected_example_pipeline(event: gr.SelectData) -> str | None:
    """
    Get the name of the pipeline that corresponds to the selected image.
    """
    for name, details in PIPELINES.items():
        if event.value["image"]["orig_name"] in details.get("examples", []):
            return name


def get_image_from_image_id(image_id):
    return [f"https://lbiiif.riksarkivet.se/arkis!{image_id}/full/max/0/default.jpg"]


# def get_images_from_iiif_manifest(iiif_manifest_url):
#     """
#     Read all images from a v2/v3 IIIF manifest.

#     Arguments:
#         manifest: IIIF manifest
#         height: Max height of returned images.
#     """
#     try:
#         response = requests.get(iiif_manifest_url, timeout=5)
#         response.raise_for_status()
#     except (requests.HTTPError, requests.ConnectionError) as e:
#         gr.Error(f"Could not fetch IIIF manifest from {iiif_manifest_url} ({e})")
#         return

#     # Hacky solution to get all images regardless of API version - treat
#     # the manifest as a string and match everything that looks like an IIIF
#     # image URL.
#     manifest = response.text
#     pattern = r'(?P<identifier>https?://[^"\s]*)/(?P<region>[^"\s]*?)/(?P<size>[^"\s]*?)/(?P<rotation>!?\d*?)/(?P<quality>[^"\s]*?)\.(?P<format>jpg|tif|png|gif|jp2|pdf|webp)'
#     height= 1200

#     images = set()  # create a set to eliminate duplicates (e.g. thumbnails and fullsize images)
#     for match in re.findall(pattern, manifest):
#         identifier, _, _, _, _, format_ = match
#         images.add(f"{identifier}/full/{height},/0/default.{format_}")

#     return sorted(images)


def get_images_from_iiif_manifest(iiif_manifest_url, max_images=20, height=1200):
    """
    Read images from a v2/v3 IIIF manifest, limited to max_images.

    Arguments:
        iiif_manifest_url: URL to IIIF manifest
        height: Max height of returned images
        max_images: Maximum number of images to return (default: 20)
    """
    try:
        buffer = io.BytesIO()
        c = pycurl.Curl()

        c.setopt(c.URL, iiif_manifest_url)
        c.setopt(c.WRITEDATA, buffer)
        c.setopt(c.CAINFO, certifi.where())
        c.setopt(c.FOLLOWLOCATION, 1)
        c.setopt(c.MAXREDIRS, 5)
        c.setopt(c.CONNECTTIMEOUT, 5)
        c.setopt(c.TIMEOUT, 10)
        c.setopt(c.NOSIGNAL, 1)
        c.setopt(c.USERAGENT, "curl/7.68.0")

        c.perform()

        http_code = c.getinfo(c.RESPONSE_CODE)
        if http_code != 200:
            raise Exception(f"HTTP Error: {http_code}")

        manifest = buffer.getvalue().decode("utf-8")
        c.close()

    except pycurl.error as e:
        error_code, error_msg = e.args
        raise Exception(
            f"Could not fetch IIIF manifest from {iiif_manifest_url} ({error_msg})"
        )

    # Hacky solution to get all images regardless of API version - treat
    # the manifest as a string and match everything that looks like an IIIF
    # image URL.
    pattern = r'(?P<identifier>https?://[^"\s]*)/(?P<region>[^"\s]*?)/(?P<size>[^"\s]*?)/(?P<rotation>!?\d*?)/(?P<quality>[^"\s]*?)\.(?P<format>jpg|tif|png|gif|jp2|pdf|webp)'

    images = (
        set()
    )  # create a set to eliminate duplicates (e.g. thumbnails and fullsize images)

    for match in re.findall(pattern, manifest):
        identifier, _, _, _, _, format_ = match
        images.add(f"{identifier}/full/{height},/0/default.{format_}")

        # Stop adding images if we've reached the maximum
        if len(images) >= max_images:
            break

    # Sort and limit the results to max_images
    return sorted(images)[:max_images], gr.update(visible=True)


with gr.Blocks() as submit:
    gr.Markdown("# Upload")
    gr.Markdown(
        "Select or upload the image you want to transcribe. Most common image formats are supported and you can upload max 5 images at a time in this hosted demo."
    )

    collection_submit_state = gr.State()

    with gr.Row(equal_height=True):
        with gr.Column(scale=2):
            batch_image_gallery = gr.Gallery(
                file_types=["image"],
                label="Image to transcribe",
                interactive=True,
                object_fit="scale-down",
            )

        with gr.Column(scale=1, variant="panel", elem_classes="panel-with-border"):
            with gr.Tabs():
                with gr.Tab("Examples"):
                    examples = gr.Gallery(
                        all_example_images(),
                        show_label=False,
                        interactive=False,
                        allow_preview=False,
                        object_fit="scale-down",
                        min_width=250,
                        height="100%",
                        columns=4,
                        container=False,
                    )

                with gr.Tab("Image ID"):
                    image_id = gr.Textbox(
                        label="Upload by image ID",
                        info=(
                            "Use any image from our digitized archives by pasting its image ID found in the "
                            "<a href='https://sok.riksarkivet.se/bildvisning/R0002231_00005' target='_blank'>image viewer</a>. "
                            "Press enter to submit."
                        ),
                        placeholder="R0002231_00005",
                    )

                with gr.Tab("IIIF Manifest"):
                    with gr.Group():
                        iiif_manifest_url = gr.Textbox(
                            label="IIIF Manifest",
                            info=(
                                "Use an image from a IIIF manifest by pasting a IIIF manifest URL. Press enter to submit."
                            ),
                            placeholder="",
                            scale=0,
                        )
                        max_images_iiif_manifest = gr.Number(
                            value=20,
                            min_width=50,
                            scale=0,
                            label="Number of image to return from IIIF manifest",
                            minimum=1,
                            visible=False,
                        )
                    iiif_gallery = gr.Gallery(
                        interactive=False,
                        columns=4,
                        allow_preview=False,
                        container=False,
                        show_label=False,
                        object_fit="scale-down",
                    )

                with gr.Tab("URL"):
                    image_url = gr.Textbox(
                        label="Image URL",
                        info="Upload an image by pasting its URL.",
                        placeholder="https://example.com/image.jpg",
                    )

                with gr.Tab("PDF"):
                    pdf_file = gr.File(label="PDF", file_types=[".pdf"])

                    pdf_gallery = gr.Gallery(
                        interactive=False,
                        columns=4,
                        allow_preview=False,
                        container=False,
                        show_label=False,
                        object_fit="scale-down",
                    )

    with gr.Column(variant="panel", elem_classes="panel-with-border"):
        gr.Markdown("## Settings")
        gr.Markdown(
            "Select a pipeline that best matches your image. The pipeline determines the processing workflow optimized for different handwritten text recognition tasks. "
            "If you select an example image, a suitable pipeline will be preselected automatically. However, you can edit the pipeline if you need to customize it further. "
            "Choosing the right pipeline significantly improves transcription quality. "
        )

        with gr.Row():
            with gr.Column(scale=0):
                pipeline_dropdown = gr.Dropdown(
                    PIPELINES,
                    container=False,
                    min_width=240,
                    scale=0,
                    elem_classes="pipeline-dropdown",
                )

            with gr.Column(scale=0, min_width=100):
                edit_pipeline_button = gr.Button("Edit", scale=0)
            with gr.Column(scale=3):
                progess_bar = gr.Textbox(visible=False, show_label=False)
            with gr.Column(scale=0, min_width=20):
                pass

        pipeline_description = gr.HTML(
            value=get_pipeline_description,
            inputs=pipeline_dropdown,
            elem_classes="pipeline-info",
            padding=False,
        )

    with Modal(visible=False) as edit_pipeline_modal:
        gr.Markdown(
            """
            ## Edit Pipeline
            The code snippet below is a YAML file that the HTRflow app uses to process the image. If you have chosen an
            image from the "Examples" section, the YAML is already a pre-made template tailored to fit the example image.

            Edit pipeline if needed:
            """
        )
        custom_template_yaml = gr.Code(
            value=get_yaml,
            inputs=pipeline_dropdown,
            language="yaml",
            container=False,
        )
        url = "https://ai-riksarkivet.github.io/htrflow/latest/getting_started/pipeline.html#example-pipelines"
        gr.HTML(
            f'See the <a href="{url}">documentation</a> for a detailed description on how to customize HTRflow pipelines.',
            padding=False,
            elem_classes="pipeline-help",
        )

    with gr.Row():
        run_button = gr.Button("Run HTR", variant="primary", scale=0, min_width=200)

    @batch_image_gallery.upload(
        inputs=batch_image_gallery,
        outputs=[batch_image_gallery],
    )
    def validate_images(images):
        if len(images) > MAX_IMAGES:
            gr.Warning(f"Maximum images you can upload is set to: {MAX_IMAGES}")
            return gr.update(value=None)
        return images

    image_id.submit(get_image_from_image_id, image_id, batch_image_gallery).then(
        fn=lambda: "Swedish - Spreads", outputs=pipeline_dropdown
    )
    iiif_manifest_url.submit(
        get_images_from_iiif_manifest,
        [iiif_manifest_url, max_images_iiif_manifest],
        [iiif_gallery, max_images_iiif_manifest],
    )
    image_url.submit(lambda url: [url], image_url, batch_image_gallery)

    pdf_file.upload(
        lambda imgs: pdf_to_images(imgs), inputs=pdf_file, outputs=pdf_gallery
    )

    run_button.click(
        fn=run_htrflow,
        inputs=[custom_template_yaml, batch_image_gallery],
        outputs=[collection_submit_state, batch_image_gallery],
    )

    examples.select(get_selected_example_image, None, batch_image_gallery)
    examples.select(get_selected_example_pipeline, None, pipeline_dropdown)

    iiif_gallery.select(get_selected_example_image, None, batch_image_gallery)
    pdf_gallery.select(get_selected_example_image, None, batch_image_gallery)

    edit_pipeline_button.click(lambda: Modal(visible=True), None, edit_pipeline_modal)