Ediashta/EmotionClassification

1import streamlit as st2from streamlit_option_menu import option_menu3from streamlit_webrtc import webrtc_streamer, VideoTransformerBase4from prediction import predict_emotion5import prediction6import eda7import model_result8 9st.sidebar.header("Emotion Classification")10st.title("Facial Emotion Classification")11 12class EmotionDetectionTransformer(VideoTransformerBase):13    def transform(self, frame):14        annotated_frame = predict_emotion(frame)15        return annotated_frame16 17def main():18    st.title('Emotion Detection App')19    st.write("Press Start")20 21    webrtc_streamer(key="example", video_processor_factory=EmotionDetectionTransformer)22 23with st.sidebar:24    st.write("Ediashta Revindra - FTDS-020")25    selected = option_menu(26        "Menu",27        [28            "Distribution",29            "Image Sample",30            "Model Result",31            "Webcam Classification",32            "Image Classification"33        ],34        icons=["bar-chart", "link-45deg", "code-square"],35        menu_icon="cast",36        default_index=0,37    )38 39if selected == "Distribution":40    eda.distribution()41elif selected == "Image Sample":42    eda.samples()43elif selected == "Model Result":44    model_result.report()45elif selected == "Webcam Classification":46    main()  # Call the main function for emotion detection47elif selected == "Image Classification":48    prediction.image_prediction()49