import cv2
import mediapipe as mp
import streamlit as st
from streamlit_webrtc import VideoTransformerBase, webrtc_streamer
import os
from twilio.rest import Client

account_sid = os.environ['TWILIO_ACCOUNT_SID']
auth_token = os.environ['TWILIO_AUTH_TOKEN']
client = Client(account_sid, auth_token)

token = client.tokens.create()

# Initialize mediapipe pose solution
mp_pose = mp.solutions.pose
mp_draw = mp.solutions.drawing_utils
pose = mp_pose.Pose()

# Load the background image
background_img_path = 'stage.jpg'
background_img = cv2.imread(background_img_path)

# Initialize Streamlit app
st.title("SwiftAi Avatar Dance")

class PoseTransformer(VideoTransformerBase):
    def __init__(self):
        self.pose = mp_pose.Pose()

    def transform(self, frame):
        img = frame.to_ndarray(format="bgr24")

        # Resize the frame to fit the display window
        img = cv2.resize(img, (600, 400))

        # Perform pose detection on the frame
        results = self.pose.process(img)

        # Resize the background image to match the size of the frame
        background_img_resized = cv2.resize(background_img, (600, 400))

        # Draw extracted pose on the background image with green color
        mp_draw.draw_landmarks(background_img_resized, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,
                               mp_draw.DrawingSpec((0, 255, 0), 4, 4),
                               mp_draw.DrawingSpec((0, 255, 0), 6, 6))

        return background_img_resized

webrtc_streamer(rtc_configuration={"iceServers": token.ice_servers},key="example", video_transformer_factory=PoseTransformer)