import tempfile
import cv2
import dlib
import numpy as np
from scipy.spatial import distance as dist
from imutils import face_utils
import gradio as gr

def detect_eye_movements(video_path):
    detector = dlib.get_frontal_face_detector()
    predictor = dlib.shape_predictor("assets/models/shape_predictor_68_face_landmarks.dat")

    cap = cv2.VideoCapture(video_path)
    frame_width, frame_height = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    
    with tempfile.NamedTemporaryFile(delete=False, suffix='.avi') as temp_file:
        out = cv2.VideoWriter(temp_file.name, cv2.VideoWriter_fourcc(*'XVID'), 20.0, (frame_width, frame_height))
        gaze_points = []

        while cap.isOpened():
            ret, frame = cap.read()
            if not ret:
                break

            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
            for rect in detector(gray, 0):
                shape = face_utils.shape_to_np(predictor(gray, rect))
                for eye in [shape[36:42], shape[42:48]]:
                    eye_center = eye.mean(axis=0).astype("int")
                    gaze_points.append(eye_center)
                    cv2.circle(frame, tuple(eye_center), 3, (0, 255, 0), -1)

            out.write(frame)

        cap.release()
        out.release()

    fixed_threshold = 10
    fixed_gaze_count = sum(dist.euclidean(gaze_points[i-1], gaze_points[i]) < fixed_threshold 
                           for i in range(1, len(gaze_points)))
    gaze_type = "Fixed Gaze" if fixed_gaze_count > len(gaze_points) // 2 else "Scattered Gaze"

    return temp_file.name, gaze_type

def create_gaze_estimation_tab():
    with gr.Row():
        with gr.Column(scale=1):
            input_video = gr.Video(label="Input Video")
            with gr.Row():
                clear_btn = gr.Button("Clear")
                submit_btn = gr.Button("Analyze", elem_classes="submit")
        with gr.Column(scale=1, elem_classes="dl4"):
            output_video = gr.Video(label="Processed Video", elem_classes="video2")
            output_gaze_type = gr.Label(label="Gaze Type")
    
    submit_btn.click(detect_eye_movements, inputs=input_video, outputs=[output_video, output_gaze_type], queue=True)
    clear_btn.click(lambda: (None, None, None), outputs=[input_video, output_video, output_gaze_type], queue=True)
    gr.Examples(["./assets/videos/fitness.mp4"], inputs=[input_video])