import streamlit as st
from utils.levels import complete_level, render_page, initialize_level
from utils.login import get_login, initialize_login
from utils.inference import query
import os
import time
import face_recognition
import cv2
import numpy as np

initialize_login()
initialize_level()

LEVEL = 4


def infer(image):
    time.sleep(1)
    output = query(image)
    cols = st.columns(2)
    cols[0].image(image, use_column_width=True)
    with cols[1]:
        for item in output:
            st.progress(item["score"], text=item["label"])

# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)

def step4_page():
    st.header("Trying It Out")
    st.info(
        "Now that we know how our face recognition application works, let's try it out!"
    )
    face_encodings_dir = os.path.join(".sessions", get_login()["username"], "face_encodings")
    face_encodings = os.listdir(face_encodings_dir)
    known_face_encodings = []
    known_face_names = []
    if len(face_encodings) > 0:
        for i, face_encoding in enumerate(face_encodings):
            known_face_encoding = np.load(os.path.join(face_encodings_dir, face_encoding))
            face_name = img.split(".")[0]
            known_face_encodings.append(known_face_encoding)
            known_face_names.append(face_name)

    while True:
            # Grab a single frame of video
            ret, frame = video_capture.read()

            # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
            rgb_frame = frame[:, :, ::-1]

            # Find all the faces and face encodings in the frame of video
            face_locations = face_recognition.face_locations(rgb_frame)
            face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

            # Loop through each face in this frame of video
            for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
                # See if the face is a match for the known face(s)
                matches = face_recognition.compare_faces(known_face_encodings, face_encoding)

                name = "Unknown"

                # If a match was found in known_face_encodings, just use the first one.
                # if True in matches:
                #     first_match_index = matches.index(True)
                #     name = known_face_names[first_match_index]

                # Or instead, use the known face with the smallest distance to the new face
                face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
                best_match_index = np.argmin(face_distances)
                if matches[best_match_index]:
                    name = known_face_names[best_match_index]

                # Draw a box around the face
                cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

                # Draw a label with a name below the face
                cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
                font = cv2.FONT_HERSHEY_DUPLEX
                cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

            # Display the resulting image
            cv2.imshow('Video', frame)
            # Hit 'q' on the keyboard to quit!
            if cv2.waitKey(1) & 0xFF == ord('q'):
                break
    # Release handle to the webcam
    video_capture.release()
    cv2.destroyAllWindows()

    st.info("Click on the button below to complete this level!")
    if st.button("Complete Level"):
        complete_level(LEVEL)


render_page(step4_page, LEVEL)