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
from PIL import Image

initialize_login()
initialize_level()

LEVEL = 4


def step4_page():
    st.header("Face Recognition: Trying It Out")
    st.write(
        """
        Once the face encodings are obtained, they can be stored in a database or used for face recognition tasks. 
        During face recognition, the encodings of input faces are compared to the stored encodings (our known-face database) 
        to determine if a match exists. Various similarity metrics, such as Euclidean distance or cosine similarity, 
        can be utilized to measure the similarity between face encodings and determine potential matches.
        """
    )
    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 = face_encoding.split(".")[0]
            known_face_encodings.append(known_face_encoding)
            known_face_names.append(face_name)

    st.info("Select an image to analyze!")
    input_type = st.radio("Select the Input Type", ["Image", "Camera"])

    if input_type == "Camera":
        picture = st.camera_input("Take a picture")
    else:
        picture = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
    if picture:
        image = face_recognition.load_image_file(picture)
        face_locations = face_recognition.face_locations(image)
        face_encodings = face_recognition.face_encodings(image, face_locations)

        st.image(image)
        # Loop through each face in this image
        cols = st.columns(len(face_encodings))
        i = 0
        # st.info("Select the tolerance level you want for your model! (How much distance between faces to consider it a match. "
        #         "Lower is more strict. 0.6 is typical best performance.)")
        # tolerance = st.slider('Select tolerance level', 0.0, 1.0, 0.3, 0.1)
        # if tolerance:
        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.
            face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)

            # Calculate the row sums
            row_sums = np.sum(face_distances, axis=1)
            best_match_index = np.argmin(row_sums)
            if best_match_index is not None:
                name = known_face_names[best_match_index]

            face_image = image[top:bottom, left:right]
            pil_image = Image.fromarray(face_image)
            cols[i].image(pil_image, use_column_width=True)
            cols[i].write("Person name: " +name)
            i+=1

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


render_page(step4_page, LEVEL)