"""App to display images in a gallery"""

import json
import pandas as pd
import numpy as np
import streamlit as st
from image_preprocessing import get_image_caption, get_images, resize_image
import plotly.express as px

from park_statistics import get_plot_from_most_common_elements, aggregate_fauna_elements
from data_models.sql_connection import get_db_connection
from data_models.park_manager import ParkManager
from data_models.image_manager import ImageManager
from data_models.openai_manager import OpenAIManager
from cloud_storage import GoogleCloudStorage

engine, session = get_db_connection()
park_manager = ParkManager()
image_manager = ImageManager()
openai_manager = OpenAIManager()
gcs = GoogleCloudStorage()


def get_image_predictions(images: list[dict]) -> dict:
    """Get predictions for a list of images

    Args:
        images (list): list of images to get predictions for

    Returns:
        list: list of predictions for the images
    """
    predictions = {}
    for image in images:
        predictions[image["id"]] = openai_manager.get_predictions(image["id"])
    return predictions


def image_gallery(images: list[dict], predictions: dict) -> None:
    """Display a gallery of images in a streamlit app

    Args:
        images (list): list of images to display
    """
    st.title("Welcome")

    columns = st.columns(3)
    for index, image in enumerate(images):
        with columns[index % 3]:
            image_blob = gcs.download_blob("suad_park", image["name"])
            st.image(
                image_blob, width=200, caption=f"Image id #{image['id']}"
            )
            if not image["id"] in predictions or predictions[image["id"]] is None:
                st.write("No predictions available")
                continue
            st.json(predictions[image["id"]])


def get_park_list() -> list:
    return park_manager.get_parks()


def get_park_images(park: str) -> list:
    return image_manager.get_images_by_park(park)


def sidebar() -> dict | None:
    """Create a sidebar to select the park

    Returns:
        selected_park: selected park in the sidebar
    """
    park_list = get_park_list()

    def _park_list_formatter(park):
        return park["name"]

    return st.sidebar.selectbox(
        label="Park List",
        options=park_list,
        index=None,
        format_func=_park_list_formatter,
    )


def display_stats() -> None:
    """Display statistics about the images"""
    st.title("Statistics")
    st.write("Number of images: ", image_manager.get_image_count())
    st.write("Number of parks: ", park_manager.get_park_count())

    predictions = openai_manager.get_all_predictions()
    df = pd.DataFrame(predictions)
    st.markdown("## Most common elements")
    st.plotly_chart(get_plot_from_most_common_elements(df, "built_elements", "elements"))

    st.markdown("## Fauna identification")
    fauna_elements = aggregate_fauna_elements(df)
    fauna_elements = pd.DataFrame(fauna_elements.items(), columns=["fauna", "count"])
    st.plotly_chart(px.pie(fauna_elements, names="fauna", values="count", labels={"count": "# Animals", "fauna": "Fauna"}))

    st.markdown("## Vegetation detection")
    st.plotly_chart(get_plot_from_most_common_elements(df, "vegetation_detection", "vegetation"))


def main() -> None:
    """Main function to run the app"""
    park = sidebar()
    if not park:
        display_stats()
        st.stop()

    images = get_park_images(park["id"])
    predictions = get_image_predictions(images)
    image_gallery(images, predictions)


if __name__ == "__main__":
    main()