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Create app.py
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app.py
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import streamlit as st
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import pandas as pd
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import numpy as np
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import altair as alt
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import joblib
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import webbrowser
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pipe_lr = joblib.load(open("D:/BEEBOX/visionott/scri.pkl", "rb"))
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emotions_emoji_dict = {"anger": "๐ ", "disgust": "๐คฎ", "fear": "๐จ๐ฑ", "happy": "๐ค", "joy": "๐", "neutral": "๐", "sad": "๐",
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"sadness": "๐", "shame": "๐ณ", "surprise": "๐ฎ"}
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def predict_emotions(docx):
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results = pipe_lr.predict([docx])
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return results[0]
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def get_prediction_proba(docx):
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results = pipe_lr.predict_proba([docx])
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return results
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def main():
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st.title("How was your Day Dude ? ๐ค ")
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st.subheader("Detect Emotions In Text and Recommend Movies")
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with st.form(key='my_form'):
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raw_text = st.text_area("Type Here")
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submit_text = st.form_submit_button(label='Submit')
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if submit_text:
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col1, col2 = st.columns(2)
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prediction = predict_emotions(raw_text)
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probability = get_prediction_proba(raw_text)
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emotion = prediction
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youtube_link = f"https://www.youtube.com/results?search_query={emotion}+movie"
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st.info(f"Recommended movies for {emotion}:")
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st.write(youtube_link)
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if st.button("Open YouTube"):
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webbrowser.open(youtube_link)
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with col1:
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st.success("Original Text")
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st.write(raw_text)
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st.success("Prediction")
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emoji_icon = emotions_emoji_dict[prediction]
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st.write("{}:{}".format(prediction, emoji_icon))
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st.write("Confidence:{}".format(np.max(probability)))
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with col2:
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st.success("Prediction Probability")
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proba_df = pd.DataFrame(probability, columns=pipe_lr.classes_)
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proba_df_clean = proba_df.T.reset_index()
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proba_df_clean.columns = ["emotions", "probability"]
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fig = alt.Chart(proba_df_clean).mark_bar().encode(x='emotions', y='probability', color='emotions')
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st.altair_chart(fig, use_container_width=True)
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if __name__ == '__main__':
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main()
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