import os
import sys
import tempfile
import os.path as osp
from PIL import Image
from io import BytesIO
import numpy as np
import pandas as pd
import streamlit as st
from PIL import ImageOps
from matplotlib import pyplot as plt
import altair as alt

root_path = osp.abspath(osp.join(__file__, osp.pardir))
sys.path.append(root_path)

from registry_utils import import_registered_modules
from app_utils import (
    extract_frames,
    is_image,
    is_video,
    convert_diameter,
    overlay_text_on_frame,
    process_frames,
    process_video,
    resize_frame,
)

import_registered_modules()

CAM_METHODS = ["CAM"]
TV_MODELS = ["ResNet18", "ResNet50"]
SR_METHODS = ["GFPGAN", "CodeFormer", "RealESRGAN", "SRResNet", "HAT"]
UPSCALE = [2, 4]
UPSCALE_METHODS = ["BILINEAR", "BICUBIC"]
LABEL_MAP = ["left_pupil", "right_pupil"]


def main():
    st.set_page_config(page_title="Pupil Diameter Estimator", layout="wide")
    st.title("EyeDentify Playground")
    cols = st.columns((1, 1))
    cols[0].header("Input")
    cols[-1].header("Prediction")

    st.sidebar.title("Upload Face or Eye")
    uploaded_file = st.sidebar.file_uploader(
        "Upload Image or Video", type=["png", "jpeg", "jpg", "mp4", "avi", "mov", "mkv", "webm"]
    )

    if uploaded_file is not None:
        file_extension = uploaded_file.name.split(".")[-1]

        if is_image(file_extension):
            input_img = Image.open(BytesIO(uploaded_file.read())).convert("RGB")
            # NOTE: images taken with phone camera has an EXIF data field which often rotates images taken with the phone in a tilted position. PIL has a utility function that removes this data and ‘uprights’ the image.
            input_img = ImageOps.exif_transpose(input_img)
            input_img = resize_frame(input_img, max_width=640, max_height=480)
            input_img = resize_frame(input_img, max_width=640, max_height=480)
            cols[0].image(input_img, use_column_width=True)
            st.session_state.total_frames = 1

        elif is_video(file_extension):
            tfile = tempfile.NamedTemporaryFile(delete=False)
            tfile.write(uploaded_file.read())
            video_path = tfile.name
            video_frames = extract_frames(video_path)
            cols[0].video(video_path)
            st.session_state.total_frames = len(video_frames)

        st.session_state.current_frame = 0
        st.session_state.frame_placeholder = cols[0].empty()
        txt = f"<p style='font-size:20px;'> Number of Frames Processed: <strong>{st.session_state.current_frame} / {st.session_state.total_frames}</strong> </p>"
        st.session_state.frame_placeholder.markdown(txt, unsafe_allow_html=True)

    st.sidebar.title("Setup")
    pupil_selection = st.sidebar.selectbox(
        "Pupil Selection", ["both"] + LABEL_MAP, help="Select left or right pupil OR both for diameter estimation"
    )
    tv_model = st.sidebar.selectbox("Classification model", ["ResNet18", "ResNet50"], help="Supported Models")

    blink_detection = st.sidebar.checkbox("Detect Blinks")

    st.markdown("<style>#vg-tooltip-element{z-index: 1000051}</style>", unsafe_allow_html=True)

    if st.sidebar.button("Predict Diameter & Compute CAM"):
        if uploaded_file is None:
            st.sidebar.error("Please upload an image or video")
        else:
            with st.spinner("Analyzing..."):

                if is_image(file_extension):
                    input_frames, output_frames, predicted_diameters, face_frames, eyes_ratios = process_frames(
                        cols,
                        [input_img],
                        tv_model,
                        pupil_selection,
                        cam_method=CAM_METHODS[-1],
                        blink_detection=blink_detection,
                    )
                    # for ff in face_frames:
                    #     if ff["has_face"]:
                    #         cols[1].image(face_frames[0]["img"], use_column_width=True)

                    input_frames_keys = input_frames.keys()
                    video_cols = cols[1].columns(len(input_frames_keys))
                    for i, eye_type in enumerate(input_frames_keys):
                        video_cols[i].image(input_frames[eye_type][-1], use_column_width=True)

                    output_frames_keys = output_frames.keys()
                    fig, axs = plt.subplots(1, len(output_frames_keys), figsize=(10, 5))
                    for i, eye_type in enumerate(output_frames_keys):
                        height, width, c = output_frames[eye_type][0].shape
                        video_cols[i].image(output_frames[eye_type][-1], use_column_width=True)

                        frame = np.zeros((height, width, c), dtype=np.uint8)
                        text = f"{predicted_diameters[eye_type][0]:.2f}"
                        frame = overlay_text_on_frame(frame, text)
                        video_cols[i].image(frame, use_column_width=True)

                elif is_video(file_extension):
                    output_video_path = f"{root_path}/tmp.webm"
                    input_frames, output_frames, predicted_diameters, face_frames, eyes_ratios = process_video(
                        cols,
                        video_frames,
                        tv_model,
                        pupil_selection,
                        output_video_path,
                        cam_method=CAM_METHODS[-1],
                        blink_detection=blink_detection,
                    )
                    os.remove(video_path)

                    num_columns = len(predicted_diameters)

                    # Create a layout for the charts
                    cols = st.columns(num_columns)

                    # colors = ["#2ca02c", "#d62728", "#1f77b4", "#ff7f0e"]  # Green, Red, Blue, Orange
                    colors = ["#1f77b4", "#ff7f0e", "#636363"]  # Blue, Orange, Gray

                    # Iterate through categories and assign charts to columns
                    for i, (category, values) in enumerate(predicted_diameters.items()):
                        with cols[i]:  # Directly use the column index
                            # st.subheader(category)  # Add a subheader for the category

                            # Convert values to numeric, replacing non-numeric values with None
                            values = [convert_diameter(value) for value in values]

                            # Create a DataFrame from the values for Altair
                            df = pd.DataFrame(values, columns=[category])
                            df["Frame"] = range(1, len(values) + 1)  # Create a frame column starting from 1

                            # Get the min and max values for y-axis limits, ignoring None
                            min_value = min(filter(lambda x: x is not None, values), default=None)
                            max_value = max(filter(lambda x: x is not None, values), default=None)

                            # Create an Altair chart with y-axis limits
                            line_chart = (
                                alt.Chart(df)
                                .mark_line(color=colors[i])
                                .encode(
                                    x=alt.X("Frame:Q", title="Frame Number"),
                                    y=alt.Y(
                                        f"{category}:Q",
                                        title="Diameter",
                                        scale=alt.Scale(domain=[min_value, max_value]),
                                    ),
                                    tooltip=[
                                        "Frame",
                                        alt.Tooltip(f"{category}:Q", title="Diameter"),
                                    ],
                                )
                                # .properties(title=f"{category} - Predicted Diameters")
                                # .configure_axis(grid=True)
                            )
                            points_chart = line_chart.mark_point(color=colors[i], filled=True)

                            final_chart = (
                                line_chart.properties(title=f"{category} - Predicted Diameters") + points_chart
                            ).interactive()

                            final_chart = final_chart.configure_axis(grid=True)

                            # Display the Altair chart
                            st.altair_chart(final_chart, use_container_width=True)

                    if eyes_ratios is not None and len(eyes_ratios) > 0:
                        df = pd.DataFrame(eyes_ratios, columns=["EAR"])
                        df["Frame"] = range(1, len(eyes_ratios) + 1)  # Create a frame column starting from 1

                        # Create an Altair chart for eyes_ratios
                        line_chart = (
                            alt.Chart(df)
                            .mark_line(color=colors[-1])  # Set color of the line
                            .encode(
                                x=alt.X("Frame:Q", title="Frame Number"),
                                y=alt.Y("EAR:Q", title="Eyes Aspect Ratio"),
                                tooltip=["Frame", "EAR"],
                            )
                            # .properties(title="Eyes Aspect Ratios (EARs)")
                            # .configure_axis(grid=True)
                        )
                        points_chart = line_chart.mark_point(color=colors[-1], filled=True)

                        # Create a horizontal rule at y=0.22
                        line1 = alt.Chart(pd.DataFrame({"y": [0.22]})).mark_rule(color="red").encode(y="y:Q")

                        line2 = alt.Chart(pd.DataFrame({"y": [0.25]})).mark_rule(color="green").encode(y="y:Q")

                        # Add text annotations for the lines
                        text1 = (
                            alt.Chart(pd.DataFrame({"y": [0.22], "label": ["Definite Blinks (<=0.22)"]}))
                            .mark_text(align="left", dx=100, dy=9, color="red", size=16)
                            .encode(y="y:Q", text="label:N")
                        )

                        text2 = (
                            alt.Chart(pd.DataFrame({"y": [0.25], "label": ["No Blinks (>=0.25)"]}))
                            .mark_text(align="left", dx=-150, dy=-9, color="green", size=16)
                            .encode(y="y:Q", text="label:N")
                        )

                        # Add gray area text for the region between red and green lines
                        gray_area_text = (
                            alt.Chart(pd.DataFrame({"y": [0.235], "label": ["Gray Area"]}))
                            .mark_text(align="left", dx=0, dy=0, color="gray", size=16)
                            .encode(y="y:Q", text="label:N")
                        )

                        # Combine all elements: line chart, points, rules, and text annotations
                        final_chart = (
                            line_chart.properties(title="Eyes Aspect Ratios (EARs)")
                            + points_chart
                            + line1
                            + line2
                            + text1
                            + text2
                            + gray_area_text
                        ).interactive()

                        # Configure axis properties at the chart level
                        final_chart = final_chart.configure_axis(grid=True)

                        # Display the Altair chart
                        # st.subheader("Eyes Aspect Ratios (EARs)")
                        st.altair_chart(final_chart, use_container_width=True)


if __name__ == "__main__":
    main()