Spaces:
Build error
Build error
File size: 2,003 Bytes
3c8c0e4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 |
import streamlit as st
from src.pipeline.predict_pipeline import CustomData, PredictPipeline
# Application title
st.set_page_config(page_title="Math Score Predictor")
st.title("Student Math Score Predictor")
st.write("This application predicts math scores based on student data.")
# Input form
with st.form(key="student_form"):
gender = st.selectbox("Gender", options=["male", "female"])
ethnicity = st.selectbox(
"Race or Ethnicity",
options=["group A", "group B", "group C", "group D", "group E"],
)
parental_education = st.selectbox(
"Parental Level of Education",
options=[
"associate's degree",
"bachelor's degree",
"high school",
"master's degree",
"some college",
"some high school",
],
)
lunch = st.selectbox("Lunch Type", options=["free/reduced", "standard"])
test_preparation_course = st.selectbox(
"Test Preparation Course", options=["none", "completed"]
)
reading_score = st.number_input(
"Reading Score (out of 100)", min_value=0, max_value=100, step=1
)
writing_score = st.number_input(
"Writing Score (out of 100)", min_value=0, max_value=100, step=1
)
# Submit button
submit_button = st.form_submit_button("Predict Exam Scores")
# Process prediction when button is pressed
if submit_button:
# Initialize data
data = CustomData(
gender=gender,
race_ethnicity=ethnicity,
parental_level_of_education=parental_education,
lunch=lunch,
test_preparation_course=test_preparation_course,
reading_score=reading_score,
writing_score=writing_score,
)
# Get data as DataFrame
pred_df = data.get_data_as_dataframe()
# Make predictions
predict_pipeline = PredictPipeline()
results = predict_pipeline.predict(pred_df)
# Display prediction result
st.success(f"The predicted Maths Score is {results[0]:.2f}")
|