app.py

import streamlit as st
import pandas as pd
import numpy as np
import altair as alt
import joblib
import webbrowser

pipe_lr = joblib.load(open("scri.pkl", "rb"))

emotions_emoji_dict = {"anger": "😠", "disgust": "🤮", "fear": "😨😱", "happy": "🤗", "joy": "😂", "neutral": "😐", "sad": "😔",
                       "sadness": "😔", "shame": "😳", "surprise": "😮"}


def predict_emotions(docx):
    results = pipe_lr.predict([docx])
    return results[0]


def get_prediction_proba(docx):
    results = pipe_lr.predict_proba([docx])
    return results


def main():
    st.title("How was your Day Dude ? 🤔 ")
    st.subheader("Detect Emotions In Text and Recommend Movies")



    with st.form(key='my_form'):
        raw_text = st.text_area("Type Here")
        submit_text = st.form_submit_button(label='Submit')



    if submit_text:
        col1, col2 = st.columns(2)

        prediction = predict_emotions(raw_text)
        probability = get_prediction_proba(raw_text)

        emotion = prediction
        youtube_link = f"https://www.youtube.com/results?search_query={emotion}+movie"
        st.info(f"Recommended movies for {emotion}:")
        st.write(youtube_link)
        if st.button("Open YouTube"):
            webbrowser.open(youtube_link)

        with col1:
            st.success("Original Text")
            st.write(raw_text)

            st.success("Prediction")
            emoji_icon = emotions_emoji_dict[prediction]
            st.write("{}:{}".format(prediction, emoji_icon))
            st.write("Confidence:{}".format(np.max(probability)))

        with col2:
            st.success("Prediction Probability")
            proba_df = pd.DataFrame(probability, columns=pipe_lr.classes_)
            proba_df_clean = proba_df.T.reset_index()
            proba_df_clean.columns = ["emotions", "probability"]

            fig = alt.Chart(proba_df_clean).mark_bar().encode(x='emotions', y='probability', color='emotions')
            st.altair_chart(fig, use_container_width=True)









if __name__ == '__main__':
    main()