upload app.py

app.py

@@ -0,0 +1,76 @@
+import streamlit as st 
+import joblib
+import pandas as pd
+#st.title('Placement Prediction app')
+# st.markdown("""
+#     <style>
+#     .title {
+#         font-size: 50px;
+#         font-weight: bold;
+#         color: #4CAF50;
+#         text-align: center;
+#         font-family: 'Courier New', Courier, monospace;
+#     }
+#     </style>
+#     """, unsafe_allow_html=True)
+
+
+# st.title('Placement Prediction App')
+# st.subheader('Predicting student placement outcomes using machine learning')
+# st.markdown('This app uses historical data to predict whether a student will be placed in a company based on their profile.')
+
+try:
+    model = joblib.load('/Users/nishthapandey/Desktop/PlacementPrediction/model_campus_placement_rf.joblib')
+    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)
+
+    # 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')
+
+    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 Percentage', 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()
+