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Create app.py
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app.py
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import streamlit as st
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import pickle
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import pandas as pd
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# Load the trained models
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rf_fullstk = pickle.load(open('rf_hacathon_fullstk.pkl', 'rb'))
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rf_prodengg = pickle.load(open('rf_hacathon_prodengg.pkl', 'rb'))
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rf_mkt = pickle.load(open('rf_hacathon_mkt.pkl', 'rb'))
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# Define the function for prediction
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def predict_placement(degree_p, internship, DSA, java, management, leadership, communication, sales, model):
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data = pd.DataFrame({
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'degree_p': degree_p,
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'internship': internship,
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'DSA': DSA,
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'java': java,
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'management': management,
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'leadership': leadership,
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'communication': communication,
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'sales': sales
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}, index=[0])
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prediction = model.predict(data)[0]
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probability = model.predict_proba(data)[0][1]
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return prediction, probability
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# Create the Streamlit app
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def main():
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st.title("Placement Prediction App")
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st.sidebar.title("Options")
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options = ["Full Stack Engineer", "Marketing", "Production Engineer"]
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job_role = st.sidebar.selectbox("Select Job Role", options)
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degree_p = st.slider("Degree Percentage", 0, 100, 50)
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internship = st.radio("Internship", ["Yes", "No"])
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DSA = st.radio("DSA Knowledge", [0, 1])
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java = st.radio("Java Knowledge", [0, 1])
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if job_role == "Full Stack Engineer":
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management = st.slider("Management Skills", 0, 5, 0)
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leadership = st.slider("Leadership Skills", 0, 5, 0)
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communication = st.slider("Communication Skills", 0, 5, 0)
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sales = st.slider("Sales Skills", 0, 5, 0)
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prediction, probability = predict_placement(degree_p, internship, DSA, java, management, leadership, communication, sales, rf_fullstk)
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elif job_role == "Marketing":
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management = st.slider("Management Skills", 0, 5, 0)
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leadership = st.slider("Leadership Skills", 0, 5, 0)
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DSA = st.slider("DSA Knowledge", 0, 5, 0)
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java = st.slider("Java Knowledge", 0, 5, 0)
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prediction, probability = predict_placement(degree_p, internship, DSA, java, management, leadership, communication, sales, rf_mkt)
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elif job_role == "Production Engineer":
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communication = st.slider("Communication Skills", 0, 5, 0)
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sales = st.slider("Sales Skills", 0, 5, 0)
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management = st.slider("Management Skills", 0, 5, 0)
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leadership = st.slider("Leadership Skills", 0, 5, 0)
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prediction, probability = predict_placement(degree_p, internship, DSA, java, management, leadership, communication, sales, rf_prodengg)
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if prediction == 1:
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st.success("Placed")
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st.success(f"You will be placed with a probability of {probability:.2f}")
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else:
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st.warning("Not Placed")
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if __name__ == '__main__':
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main()
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