import logging
import queue
from pathlib import Path
from typing import List, NamedTuple
import mediapipe as mp
import av
import cv2
import numpy as np
import streamlit as st
from streamlit_webrtc import WebRtcMode, webrtc_streamer
from sample_utils.download import download_file
from sample_utils.turn import get_ice_servers
from cvzone.HandTrackingModule import HandDetector
from cvzone.SelfiSegmentationModule import SelfiSegmentation
import os
import time

logger = logging.getLogger(_name_)

# Streamlit settings
st.set_page_config(page_title="Virtual Keyboard", layout="wide")
st.title("Interactive Virtual Keyboard")
st.subheader('''Turn on the webcam and use hand gestures to interact with the virtual keyboard.
Use 'a' and 'd' from the keyboard to change the background.''')



# # Logging setup
# logger = logging.getLogger(__name__)


# Streamlit settings
st.set_page_config(page_title="Virtual Keyboard", layout="wide")
st.title("Interactive Virtual Keyboard")
st.subheader('''Turn on the webcam and use hand gestures to interact with the virtual keyboard.
Use 'a' and 'd' from the keyboard to change the background.''')

# Initialize modules
detector = HandDetector(maxHands=1, detectionCon=0.8)
segmentor = SelfiSegmentation()

# Define virtual keyboard layout
keys = [["Q", "W", "E", "R", "T", "Y", "U", "I", "O", "P"],
        ["A", "S", "D", "F", "G", "H", "J", "K", "L", ";"],
        ["Z", "X", "C", "V", "B", "N", "M", ",", ".", "/"]]

class Button:
    def _init_(self, pos, text, size=[100, 100]):
        self.pos = pos
        self.size = size
        self.text = text

class Detection(NamedTuple):
    label: str
    score: float
    box: np.ndarray

result_queue: "queue.Queue[List[Detection]]" = queue.Queue()

listImg = os.listdir('model/street') if os.path.exists('model/street') else []
if not listImg:
    st.error("Error: 'street' directory is missing or empty. Please add background images.")
    st.stop()
else:
    imgList = [cv2.imread(f'model/street/{imgPath}') for imgPath in listImg if cv2.imread(f'model/street/{imgPath}') is not None]

indexImg = 0
prev_key_time = [time.time()] * 2
output_text = ""

if "output_text" not in st.session_state:
    st.session_state["output_text"] = ""

def video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame:
    global indexImg, output_text

    img = frame.to_ndarray(format="bgr24")

    detections = []
    if hands:
        for i, hand in enumerate(hands):
            lmList = hand['lmList']
            bbox = hand['bbox']
            label = "Hand"
            score = hand['score']
            box = np.array([bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]])
    detections.append(Detection(label=label, score=score, box=box))
    result_queue.put(detections)
    st.session_state["output_text"] = output_text
    return av.VideoFrame.from_ndarray(imgOut, format="bgr24")

webrtc_streamer(
    key="virtual-keyboard",
    mode=WebRtcMode.SENDRECV,
    rtc_configuration={"iceServers": get_ice_servers(), "iceTransportPolicy": "relay"},
    media_stream_constraints={"video": True, "audio": False},
    video_frame_callback=video_frame_callback,
    async_processing=True,
)