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streamlit-api
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from pathlib import Path
import PIL

# External packages
import streamlit as st

# Local Modules
import settings
import helper
import test_api.algorithm as algorithm
import multiprocessing 
import time
import requests
import socketio
import base64
import io
from PIL import Image


st.set_page_config(
    page_title="YOLOv8 目标检测",
    page_icon="🤖",
    layout="wide",
    initial_sidebar_state="expanded"  # 或者 "collapsed"
)


# # Main page heading
st.title("YOLOv8 目标检测")

# Sidebar
st.sidebar.header("模型配置")

# Model Options
model_type = st.sidebar.selectbox(
    "任务选择", ['检测', '分割',"越界检测","行为检测"])

confidence = float(st.sidebar.slider(
    "选择模型Confidence", 25, 100, 40)) / 100

# Selecting Detection Or Segmentation
if model_type == '检测':
    model_path = Path(settings.DETECTION_MODEL)
elif model_type == '分割':
    model_path = Path(settings.SEGMENTATION_MODEL)
elif model_type == "越界检测":
    model_path = Path(settings.SEGMENTATION_MODEL)
elif model_type == "行为检测":
    model_path = Path(settings.SEGMENTATION_MODEL)

# Load Pre-trained ML Model
try:
    model = helper.load_model(model_path)
except Exception as ex:
    st.error(f"Unable to load model. Check the specified path: {model_path}")
    st.error(ex)

st.sidebar.header("图像/视频 配置")
source_radio = st.sidebar.radio(
    "选择来源", settings.SOURCES_LIST)

source_img = None
# If image is selected
if source_radio == settings.IMAGE:
    source_img = st.sidebar.file_uploader(
        "选择一张图像...", type=("jpg", "jpeg", "png", 'bmp', 'webp'))

    col1, col2 = st.columns(2)

    with col1:
        try:
            if source_img is None:
                default_image_path = str(settings.DEFAULT_IMAGE)
                default_image = PIL.Image.open(default_image_path)
                st.image(default_image_path, caption="默认图像",
                         use_column_width=True)
            else:
                uploaded_image = PIL.Image.open(source_img)
                st.image(source_img, caption="Uploaded Image",
                         use_column_width=True)
        except Exception as ex:
            st.error("Error occurred while opening the image.")
            st.error(ex)

    with col2:
        if source_img is None:
            default_detected_image_path = str(settings.DEFAULT_DETECT_IMAGE)
            default_detected_image = PIL.Image.open(
                default_detected_image_path)
            st.image(default_detected_image_path, caption='检测图像',
                     use_column_width=True)
        else:
            if st.sidebar.button('检测目标'):
                res = model.predict(uploaded_image,
                                    conf=confidence
                                    )
                boxes = res[0].boxes
                res_plotted = res[0].plot()[:, :, ::-1]
                st.image(res_plotted, caption='Detected Image',
                         use_column_width=True)
                try:
                    with st.expander("Detection Results"):
                        for box in boxes:
                            st.write(box.data)
                except Exception as ex:
                    # st.write(ex)
                    st.write("No image is uploaded yet!")




elif source_radio == settings.RTSP:
    if model_type == '检测':
        src ={
	"video_url": "http://127.0.0.1:8999/live/test.live.flv"
}
        start = st.sidebar.button('检测目标')
        stop = st.sidebar.button('停止') 

        if start:
            response = requests.post('http://192.168.110.232:7555/analyzerControlAdd', json=src)
            print(response.text)

    elif model_type == '分割':
        yolov8=algorithm.YoloV8Detection()
        yolov8.play_rtsp_stream(confidence,model,display_video=True)

    elif model_type == "越界检测":
        yolov8_b=algorithm.BoundaryDetection()
        yolov8_b.play_rtsp_stream(confidence,model,display_video=True)


    #helper.play_rtsp_stream(confidence, model)

else:
    st.error("Please select a valid source type!")


# 创建 SocketIO 实例
sio = socketio.Client()

# 连接到 WebSocket 服务器
sio.connect('http://192.168.110.232:7555')
# 接收帧数据并显示
@sio.on('frame_data')
def handle_frame_data(data):
    image_bytes = base64.b64decode(data['image'])
    image = Image.open(io.BytesIO(image_bytes))
    st.image(image, caption='Real-time Detection', channels='BGR')

# 等待连接断开
st.text('Waiting for real-time updates...')
sio.wait()