app.py

import streamlit as st
import pickle
import pandas as pd

# Load the trained models
rf_fullstk = pickle.load(open('rf_hacathon_fullstk.pkl', 'rb'))
rf_prodengg = pickle.load(open('rf_hacathon_prodengg.pkl', 'rb'))
rf_mkt = pickle.load(open('rf_hacathon_mkt.pkl', 'rb'))

# Define the function for prediction
def predict_placement(degree_p, internship, DSA, java, management, leadership, communication, sales, model):
    data = pd.DataFrame({
        'degree_p': degree_p,
        'internship': internship,
        'DSA': DSA,
        'java': java,
        'management': management,
        'leadership': leadership,
        'communication': communication,
        'sales': sales
    }, index=[0])
    prediction = model.predict(data)[0]
    probability = model.predict_proba(data)[0][1]
    return prediction, probability

# Create the Streamlit app
def main():
    st.title("Placement Prediction App")
    st.sidebar.title("Options")
    options = ["Full Stack Engineer", "Marketing", "Production Engineer"]
    job_role = st.sidebar.selectbox("Select Job Role", options)

    degree_p = st.slider("Degree Percentage", 0, 100, 50)
    internship = st.radio("Internship", ["Yes", "No"])
    DSA = st.radio("DSA Knowledge", [0, 1])
    java = st.radio("Java Knowledge", [0, 1])

    if job_role == "Full Stack Engineer":
        management = st.slider("Management Skills", 0, 5, 0)
        leadership = st.slider("Leadership Skills", 0, 5, 0)
        communication = st.slider("Communication Skills", 0, 5, 0)
        sales = st.slider("Sales Skills", 0, 5, 0)
        prediction, probability = predict_placement(degree_p, internship, DSA, java, management, leadership, communication, sales, rf_fullstk)
    elif job_role == "Marketing":
        management = st.slider("Management Skills", 0, 5, 0)
        leadership = st.slider("Leadership Skills", 0, 5, 0)
        DSA = st.slider("DSA Knowledge", 0, 5, 0)
        java = st.slider("Java Knowledge", 0, 5, 0)
        prediction, probability = predict_placement(degree_p, internship, DSA, java, management, leadership, communication, sales, rf_mkt)
    elif job_role == "Production Engineer":
        communication = st.slider("Communication Skills", 0, 5, 0)
        sales = st.slider("Sales Skills", 0, 5, 0)
        management = st.slider("Management Skills", 0, 5, 0)
        leadership = st.slider("Leadership Skills", 0, 5, 0)
        prediction, probability = predict_placement(degree_p, internship, DSA, java, management, leadership, communication, sales, rf_prodengg)

    if prediction == 1:
        st.success("Placed")
        st.success(f"You will be placed with a probability of {probability:.2f}")
    else:
        st.warning("Not Placed")

if __name__ == '__main__':
    main()