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import pickle |
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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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st.title("Hello streamlit") |
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def predict(names): |
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model = pickle.load(open( |
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"model.pkl", "rb")) |
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result = model.predict([names]) |
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return result[0] |
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def main(): |
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CRIM = st.number_input("CRIM") |
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ZN = st.number_input("proportion of residential land zoned") |
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INDUS = st.number_input("proportion of non-retail business acres per town") |
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CHAS = st.number_input( |
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"Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)") |
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NOX = st.number_input("nitric oxides concentration") |
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RM = st.number_input("average number of rooms per dwelling") |
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AGE = st.number_input( |
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"proportion of owner-occupied units built prior to 1940") |
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DIS = st.number_input( |
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"weighted distances to five Boston employment centers") |
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RAD = st.number_input("index of accessibility to radial highways") |
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TAX = st.number_input("full-value property-tax rate per $10,000") |
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PTRATIO = st.number_input("pupil-teacher ratio by town") |
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B = st.number_input("B") |
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LSTAT = st.number_input("LSTAT") |
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names = [CRIM, ZN, INDUS, CHAS, NOX, RM, AGE, DIS, RAD, TAX, PTRATIO, B, LSTAT] |
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if st.button("prediction"): |
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ans = predict(names) |
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st.success(f"your home price could be - {ans}") |
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if __name__ == "__main__": |
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main() |
