Create app.py

app.py

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+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()