import cv2
import streamlit as st
from fastai.vision.all import *
import base64
from streamlit_webrtc import webrtc_streamer
import av
import time


def video_frame_callback(frame):
    #time.sleep(1)
    return frame


webrtc_streamer(key="example",rtc_configuration={  # Add this config
        "iceServers": [
      {
        "urls": "stun:stun.relay.metered.ca:80",
      },
      {
        "urls": "turn:a.relay.metered.ca:80",
        "username": "1569e470fe2a2afdb9e2b963",
        "credential": "EG59c3tmBQzU7mMw",
      },
      {
        "urls": "turn:a.relay.metered.ca:80?transport=tcp",
        "username": "1569e470fe2a2afdb9e2b963",
        "credential": "EG59c3tmBQzU7mMw",
      },
      {
        "urls": "turn:a.relay.metered.ca:443",
        "username": "1569e470fe2a2afdb9e2b963",
        "credential": "EG59c3tmBQzU7mMw",
      },
      {
        "urls": "turn:a.relay.metered.ca:443?transport=tcp",
        "username": "1569e470fe2a2afdb9e2b963",
        "credential": "EG59c3tmBQzU7mMw",
      },
  ]
    })

st.markdown('<h1 style="color:black;">BDI Image classification model</h1>', unsafe_allow_html=True)
learn = load_learner('export (5).pkl')
upload = st.file_uploader('Insert image for classification', type=['png','jpg'])
c1, c2= st.columns(2)
def autoplay_audio(file_path: str):
    with open(file_path, "rb") as f:
        data = f.read()
        b64 = base64.b64encode(data).decode()
        md = f"""
            <audio controls autoplay="true">
            <source src="data:audio/mp3;base64,{b64}" type="audio/mp3">
            </audio>
            """
        st.markdown(
            md,
            unsafe_allow_html=True,
        )


if upload is not None:
    im= Image.open(upload)
    c1.header('Input Image')
    c1.image(im)
    pred, idx, probs = learn.predict(im)

    c2.header('Output')
    c2.subheader('Predicted class :')
    c2.write(pred)
    if pred == "Dog Destroying Stuff":
        autoplay_audio("B1eckLSj.mp3")