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import streamlit as st
import joblib
import pandas as pd
# st.markdown(
# """
# <style>
# body {
# background-color: #C9E4DE;
# }
# </style>
# """,
# unsafe_allow_html=True
# )
# try:
# model = joblib.load('model_campus')
# st.success("Model loaded successfully!")
# except Exception as e:
# st.error(f"Error loading model: {e}")
# st.stop()
# model = joblib.load(open('/Users/nishthapandey/Desktop/PlacementPrediction/model_campus_placement_rf.joblib','rb'))
def predict_placement(data):
# Preprocess the data
new_data = pd.DataFrame(data, index[0])
# Make prediction
prediction = model.predict(new_data)[0]
prob = model.predict_proba(new_data)[0][1]
return prediction, prob
def main():
st.header('Placement Prediciton App')
st.markdown('This app uses historical data to predict whether a student will be placed in a company based on their profile.')
gender = st.radio('Gender', ['Male', 'Female'])
ssc_p = st.number_input('Secondary School Percentage', min_value=0.0, max_value=100.0, value=50.0, step=0.1)
ssc_b = st.radio('Board of Education (SSC)', ['Central', 'Others'])
hsc_p = st.number_input('Higher Secondary Percentage', min_value=0.0, max_value=100.0, value=50.0, step=0.1)
hsc_b = st.radio('Board of Education (HSC)', ['Central', 'Others'])
degree_p = st.number_input('UG Percentage', min_value=0.0, max_value=100.0, value=50.0, step=0.1)
branch = st.selectbox('Branch of Study', ['CSE', 'ECE/EN', 'Others'])
workex = st.radio('Work Experience', ['Yes', 'No'])
certifications = st.number_input('Number of Certifications', min_value=0, max_value=10, value=0)
etest_p = st.number_input('Employability Test Score', min_value=0.0, max_value=100.0, value=50.0, step=0.1)
backlogs = st.number_input('Number of Backlogs', min_value=0, max_value=10, value=0)
if st.button('predict'):
new_data = {
'gender': 0 if gender == "Male" else 1,
'ssc_p': ssc_p,
'ssc_b': 1 if ssc_b == "Central" else 0,
'hsc_p': hsc_p,
'hsc_b': 1 if hsc_b == "Central" else 0,
'degree_p': degree_p,
'Branch': 2 if branch == "ECE/EN" else 1 if branch == "CSE" else 0,
'Workex': 1 if workex == "Yes" else 0,
'Certifications': certifications,
'etest_p': etest_p,
'Backlogs': backlogs,
}
prediction, probability = predict_placement(new_data)
st.write(f'Percentage of getting placed: {probability*100:.2f}%')
if __name__=='__main__':
main()
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