app.py

# Python In-built packages
from pathlib import Path
import PIL
# External packages
import streamlit as st
from streamlit_login_auth_ui.widgets import __login__
from structures.essentials import load_model
# Local Modules
import settings
import helper

# Setting page layout
st.set_page_config(
        page_title="S.A.D.A.K",
        page_icon="🤖",
        layout="wide",
        initial_sidebar_state="expanded"
    )
st.title("S.A.D.A.K")
__login__obj = __login__(auth_token = settings.COURIER_API_KEY, 
                    company_name = "S.A.D.A.K",
                    width = 200, height = 250, 
                    logout_button_name = 'Logout', hide_menu_bool = False, 
                    hide_footer_bool = False, 
                    lottie_url = 'https://assets2.lottiefiles.com/packages/lf20_jcikwtux.json')

LOGGED_IN = __login__obj.build_login_ui()

if LOGGED_IN == True:
    # Main page heading
    # Sidebar
    st.sidebar.header("Configuration")
    helper.startup()
    # Model Options
    model_type = st.sidebar.radio(
        "Select Task", ['Detection', 'Segmentation'])

    confidence = float(st.sidebar.slider(
        "Select Model Confidence", 25, 100, 40)) / 100

    # Selecting Detection Or Segmentation
    if model_type == 'Detection':
        model_path = Path(settings.DETECTION_MODEL)
    elif model_type == 'Segmentation':
        model_path = Path(settings.SEGMENTATION_MODEL)

    # Load Pre-trained ML Model
    try:
        model = 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("Image/Video Config")
    source_radio = st.sidebar.radio(
        "Select Source", settings.SOURCES_LIST)

    source_img = None
    # If image is selected
    if source_radio == settings.IMAGE:
        source_img = st.sidebar.file_uploader(
            "Choose an image...", 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="Default Image",
                            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='Detected Image',
                        use_column_width=True)
            else:
                if st.sidebar.button('Detect Objects'):
                    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.VIDEO:
        helper.play_stored_video(confidence, model)

    elif source_radio == settings.RTSP:
        helper.play_rtsp_stream(confidence, model)

    elif source_radio == settings.YOUTUBE:
        helper.play_youtube_video(confidence, model)
        
    elif source_radio == settings.ENCROACHMENT:
        helper.enchroachment()
        
    elif source_radio == settings.JUNCTION:  
        helper.junctionEvaluationDataset()
        
    elif source_radio == settings.JUNCTIONEVAL:
        helper.junctionEvaluation()
        
    elif source_radio == settings.BENCHMARKING:
        helper.benchMarking()
    else:
        st.error("Please select a valid source type!")