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"""Lab07_Deployment_on_HuggingFace_Spaces_backend.ipynb |
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Automatically generated by Colaboratory. |
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Original file is located at |
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https://colab.research.google.com/drive/18LL9Kki70qRHvNJQFYMtTe4TwJRJ97x9 |
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""" |
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import joblib |
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import pandas as pd |
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import streamlit as st |
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EDU_DICT = {"bachelor's degree": 1, |
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'some college': 2, |
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"master's degree": 3, |
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"associate's degree": 4, |
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'high school': 5, |
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'some high school': 6, |
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} |
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model = joblib.load('model.joblib') |
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unique_values = joblib.load('unique_values.joblib') |
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unique_gender = unique_values["gender"] |
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unique_race_ethnicity = unique_values["race/ethnicity"] |
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unique_level_of_education = unique_values["parental level of education"] |
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unique_lunch = unique_values["lunch"] |
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def main(): |
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st.title("Students Performance Analysis") |
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with st.form("questionaire"): |
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gender = st.selectbox("gender", unique_gender) |
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race_ethnicity = st.selectbox("race/ethnicity", unique_race_ethnicity) |
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level_of_education = st.selectbox("parental level of education", unique_level_of_education) |
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lunch = st.selectbox("lunch", unique_lunch) |
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math_score = st.slider("math score", min_value=0, max_value=100) |
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reading_score = st.slider("reading score", min_value=17, max_value=100) |
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writing_score = st.slider("writing score", min_value=10, max_value=100) |
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clicked = st.form_submit_button("Predict Students Performance") |
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if clicked: |
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result= model.predict(pd.DataFrame({"gender": [gender], |
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"race/ethnicity": [race_ethnicity], |
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"parental level of education": [EDU_DICT[level_of_education]], |
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"lunch": [lunch], |
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"math score": [math_score], |
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"reading score": [reading_score], |
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"writing score": [writing_score] |
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})) |
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result = 'completed' if result[0] == 1 else 'none' |
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st.success('The predicted students performance is {}'.format(result)) |
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if __name__=='__main__': |
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main() |
