# Import necessary libraries
import matplotlib

# Use Agg backend for Matplotlib
matplotlib.use("Agg")

# Libraries for the app
import streamlit as st
import time
import io
import argparse
import sys
import os.path
import subprocess
import tempfile
import logging

# Visualization libraries
import altair as alt
import av

# Machine Learning and Image Processing libraries
import numpy as np
import pandas as pd
import cv2 as cv
from PIL import Image, ImageOps
from tqdm import tqdm

# Custom modules
import inference
from app_utils import *

@st.cache_data
def load_video(video_url):
    video_bytes = open(video_url, "rb").read()
    return video_bytes

@st.cache_data
def load_historical(fpath):
    return pd.read_csv(fpath)

st.set_page_config(layout="wide")


def process_uploaded_file():
    st.subheader("Upload your own video...")

    # Initialize accepted file types for upload
    img_types = ["jpg", "png", "jpeg"]
    video_types = ["mp4", "avi"]

    # Allow user to upload an image or video file
    uploaded_file = st.file_uploader("Select an image or video file...", type=img_types + video_types)

    # Display the uploaded file
    if uploaded_file is not None:
        if str(uploaded_file.type).split("/")[-1] in img_types:
            # Display uploaded image
            image = Image.open(uploaded_file)
            st.image(image, caption="Uploaded image", use_column_width=True)

            # TBD: Inference code to run and display for single image

        elif str(uploaded_file.type).split("/")[-1] in video_types:
            # Display uploaded video
            st.video(uploaded_file)

            # Convert streamlit video object to OpenCV format to run inferences
            tfile = tempfile.NamedTemporaryFile(delete=False)
            tfile.write(uploaded_file.read())
            vf = cv.VideoCapture(tfile.name)

            # Run inference on the uploaded video
            with st.spinner("Running inference..."):
                frames, counts, timestamps = inference.main(vf)
            logging.info("INFO: Completed running inference on frames")
            st.balloons()

            # Convert OpenCV Numpy frames in-memory to IO Bytes for streamlit
            streamlit_video_file = frames_to_video(frames=frames, fps=11)

            # Show processed video and provide download button
            st.video(streamlit_video_file)
            st.download_button(
                label="Download processed video",
                data=streamlit_video_file,
                mime="mp4",
                file_name="processed_video.mp4",
            )

            # Create dataframe for fish counts and timestamps
            df_counts_time = pd.DataFrame(
                data={"fish_count": counts, "timestamps": timestamps[1:]}
            )

            # Display fish count vs. timestamp chart
            st.altair_chart(
                plot_count_date(dataframe=df_counts_time),
                use_container_width=True,
            )

    else:
        st.write("No file uploaded")


# Define the main function to run the Streamlit app
def run_app():
    # Set Streamlit options
    st.set_option("deprecation.showfileUploaderEncoding", False)
    
    # App title and description
    st.title("MIT Count Fish Counter")
    st.text("Upload a video file to detect and count fish")
    
    # Example video URL or file path (replace with actual video URL or file path)
    video_url = "yolo2_out_py.mp4"
    video_bytes = load_video(video_url)

    # Load historical herring
    df_historical_herring = load_historical(fpath="herring_count_all.csv")

    main_tab, upload_tab = st.tabs(["Analysis", "Upload video for analysis"])



    with main_tab:
        # Create two columns for layout
        col1, col2 = st.columns(2)
        ## Col1 #########################################
        with col1:
            ## Initial visualizations
            # Plot historical data
            st.altair_chart(
                plot_historical_data(df_historical_herring),
                use_container_width=True,
            )

            # Display map of fishery locations
            st.subheader("Map of Fishery Locations")
            st.map(
                pd.DataFrame(
                    np.random.randn(5, 2) / [50, 50] + [42.41, -71.38],
                    columns=["lat", "lon"],
                ),use_container_width=True
            )
        with col2:
            # Display example processed video
            st.subheader("Example of processed video")
            st.video(video_bytes)
    with upload_tab:
        process_uploaded_file()

# Run the app if the script is executed directly
if __name__ == "__main__":
    run_app()