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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()
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