Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import joblib
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.tree import DecisionTreeClassifier
+import time
+import numpy as np
+
+import streamlit as st
+
+# Using Markdown with custom styles to center the title and add style
+st.markdown("""
+    <style>
+    .title {
+        font-size: 40px;
+        font-weight: bold;
+        color: #FF4B4B;
+        text-align: center;
+        margin-bottom: -20px;  # Adjusts the spacing below the title
+    }
+    </style>
+    <div class="title">🎓 STUDENT DROPOUT PREDICTION APP 🎓</div>
+    """, unsafe_allow_html=True)
+
+# Display a banner image
+st.image("banner.webp", use_column_width=True)
+
+# Main page description
+st.markdown("""
+This app predicts the likelihood of a student dropping out 🚪.
+Enter the student's details on the left sidebar to see the prediction result.
+The prediction helps in identifying students at risk early, allowing for timely intervention to improve retention rates.
+""")
+
+import joblib
+
+# Load the models
+decision_tree = joblib.load('age_aware_models/decision_tree_model.joblib')
+logistic_regression = joblib.load('age_aware_models/logistic_regression_model.joblib')
+random_forest = joblib.load('age_aware_models/random_forest_model.joblib')
+
+# Define a dictionary of models with their names, actual models, and types
+models = {
+    'Decision Tree': {'model': decision_tree, 'type': 'Decision Tree'},
+    'Logistic Regression': {'model': logistic_regression, 'type': 'Logistic Regression'},
+    'Random Forest': {'model': random_forest, 'type': 'Random Forest'}
+}
+
+with st.sidebar:
+    # Streamlit UI to select a model
+    # Add some design to the header
+    st.write("<h2 style='color: #ff5733; text-align: center;'>Select Model</h2>", unsafe_allow_html=True)
+
+    st.header('')
+    # Ensure that this is defined before you try to use `model_name`
+    model_name = st.selectbox('Choose a model', list(models.keys()))
+
+    # Retrieve the selected model and its type from the dictionary after it's been defined
+    model = models[model_name]['model']
+    model_type = models[model_name]['type']
+
+    # Additional Streamlit code to display selected model and type or other UI elements
+    st.write(f"You have selected: {model_name}")
+
+
+# Load trained model
+@st.cache_resource
+#def load_model():
+#    return LogisticRegression()  # Load your trained model here
+
+def preprocess_input(input_data, original_feature_names):
+    # Create a DataFrame from the input data
+    input_df = pd.DataFrame(input_data, index=[0])
+
+    # Ensure the DataFrame has the correct column structure
+    input_df = input_df.reindex(columns=original_feature_names, fill_value=0)
+
+    return input_df
+
+original_feature_names = ['Marital_Status', 'Application_Mode', 'Application_Order', 'Course',
+       'Attendance', 'Previous_Qualification', 'Nationality',
+       'Mother_Qualification', 'Father_Qualification', 'Mother_Occupation',
+       'Father_Occupation', 'Displaced', 'Special_Needs', 'Debtor',
+       'Fees_UpToDate', 'Gender', 'Scholarship_Holder', 'Age', 'International',
+       '1st_Sem_Credits', '1st_Sem_Enrolled', '1st_Sem_Evaluations',
+       '1st_Sem_Approved', '1st_Sem_Grade', '1st_Sem_No_Evaluations',
+       '2nd_Sem_Credits', '2nd_Sem_Enrolled', '2nd_Sem_Evaluations',
+       '2nd_Sem_Approved', '2nd_Sem_Grade', '2nd_Sem_No_Evaluations',
+       'Unemployment_Rate', 'Inflation_Rate', 'GDP']
+
+def map_and_select(label, mapping_or_value, min_value=None, max_value=None, step=None):
+    if isinstance(mapping_or_value, dict):
+        # Invert the mapping dictionary
+        inverted_mapping = {v: k for k, v in mapping_or_value.items()}
+
+        # Display the selectbox
+        selected_option = st.sidebar.selectbox(label, options=list(inverted_mapping.keys()))
+
+        # Retrieve numerical value based on the selected option
+        selected_value = inverted_mapping[selected_option]
+        st.sidebar.write(f"{label} Value:", selected_value)
+        return selected_value
+    else:
+        # Determine the type of input and display accordingly
+        if isinstance(mapping_or_value, float):
+            # Handle as a slider for float values
+            selected_value = st.sidebar.slider(label, min_value=min_value, max_value=max_value, value=mapping_or_value, step=step)
+        elif isinstance(mapping_or_value, int):
+            # Handle as a number input for int values
+            selected_value = st.sidebar.number_input(label, min_value=min_value, max_value=max_value, value=mapping_or_value, step=step)
+        else:
+            # Display as a text input for non-numeric values
+            selected_value = st.sidebar.text_input(label, value=str(mapping_or_value))
+
+        st.sidebar.write(f"{label}:", selected_value)
+        return selected_value
+
+def predict_dropout(input_data, model, model_type):
+    # Initialize variable to ensure it has a value in all code paths
+    dropout_prediction = None
+
+    # Check the model type to decide on the prediction method
+    if model_type == "Logistic Regression" or model_type == "Decision Tree" or model_type == "Random Forest":
+        # Use model.predict for predictions
+        dropout_prediction = model.predict(input_data)
+    else:
+        raise ValueError("Unsupported model type.")
+
+    return dropout_prediction
+
+def map_dropout_prediction(prediction):
+    if prediction == 1:
+        return "Dropout", "🎓", "The model predicts that the student is likely to dropout."
+    else:
+        return "Not Dropout", "👩‍🎓", "The model predicts that the student is not likely to dropout."
+
+marital_mapping = {
+    1: 'Single',
+    2: 'Married',
+    3: 'Widower',
+    4: 'Divorced',
+    5: 'Facto union',
+    6: 'Legally separated'
+}
+
+
+# Add some design to the header
+st.sidebar.write("<h2 style='color: #ff5733; text-align: center;'>Enter Student Details</h2>", unsafe_allow_html=True)
+
+
+# Use the map_and_select function to handle mapping and selection for Marital Status
+marital_status = map_and_select('Marital Status', marital_mapping)
+
+application_mode_mapping = {
+    1: '1st phase—general contingent',
+    2: 'Ordinance No. 612/93',
+    3: '1st phase—special contingent (Azores Island)',
+    4: 'Holders of other higher courses',
+    5: 'Ordinance No. 854-B/99',
+    6: 'International student (bachelor)',
+    7: '1st phase—special contingent (Madeira Island)',
+    8: '2nd phase—general contingent',
+    9: '3rd phase—general contingent',
+    10: 'Ordinance No. 533-A/99, item b2) (Different Plan)',
+    11: 'Ordinance No. 533-A/99, item b3 (Other Institution)',
+    12: 'Over 23 years old',
+    13: 'Transfer',
+    14: 'Change in course',
+    15: 'Technological specialization diploma holders',
+    16: 'Change in institution/course',
+    17: 'Short cycle diploma holders',
+    18: 'Change in institution/course (International)'
+}
+
+
+application_mode = map_and_select('Application Mode', application_mode_mapping)
+
+
+#application_mode = st.sidebar.selectbox('Application Mode', options=range(1, 10))  # Assuming modes 1 through 9
+#st.sidebar.write("application Mode:", application_mode)
+
+application_order_mapping = {
+    1: 'First',
+    2: 'Second',
+    3: 'Third',
+    4: 'Fourth',
+    5: 'Fifth',
+    6: 'Sixth',
+    9: 'Ninth',
+    0: 'Zero'
+}
+
+application_order = map_and_select('Application Order', application_order_mapping)
+
+#application_order = st.sidebar.number_input('Application Order', min_value=0, max_value=10, value=1)
+#st.sidebar.write("application Order:", application_order)
+
+courses_mapping = {
+    1: 'Biofuel Production Technologies',
+    2: 'Animation and Multimedia Design',
+    3: 'Social Service (evening attendance)',
+    4: 'Agronomy',
+    5: 'Communication Design',
+    6: 'Veterinary Nursing',
+    7: 'Informatics Engineering',
+    8: 'Equiniculture',
+    9: 'Management',
+    10: 'Social Service',
+    11: 'Tourism',
+    12: 'Nursing',
+    13: 'Oral Hygiene',
+    14: 'Advertising and Marketing Management',
+    15: 'Journalism and Communication',
+    16: 'Basic Education',
+    17: 'Management (evening attendance)'
+}
+
+# Use the map_and_select function to handle mapping and selection for Courses
+course = map_and_select('Course', courses_mapping)
+
+#course = st.sidebar.selectbox('Course', options=range(1, 100))  # Update range based on actual course codes
+#st.sidebar.write("course:", course)
+
+attendance_mapping = {
+    1: 'Daytime',
+    2: 'Evening'
+}
+
+# Use the map_and_select function to handle mapping and selection for Daytime/Evening Attendance
+daytime_evening_attendance = map_and_select('Daytime/Evening Attendance', attendance_mapping)
+
+#daytime_evening_attendance = st.sidebar.radio('Daytime/Evening Attendance', options=[1, 2], format_func=lambda x: 'Daytime' if x == 1 else 'Evening')
+#st.sidebar.write("Daytime Evening Attendance:", daytime_evening_attendance)
+
+previous_qualification_mapping = {
+    1: 'Secondary education',
+    2: 'Higher education—bachelor’s degree',
+    3: 'Higher education—degree',
+    4: 'Higher education—master’s degree',
+    5: 'Higher education—doctorate',
+    6: 'Frequency of higher education',
+    7: '12th year of schooling—not completed',
+    8: '11th year of schooling—not completed',
+    9: 'Other—11th year of schooling',
+    10: '10th year of schooling',
+    11: '10th year of schooling—not completed',
+    12: 'Basic education 3rd cycle (9th/10th/11th year) or equivalent',
+    13: 'Basic education 2nd cycle (6th/7th/8th year) or equivalent',
+    14: 'Technological specialization course',
+    15: 'Higher education—degree (1st cycle)',
+    16: 'Professional higher technical course',
+    17: 'Higher education—master’s degree (2nd cycle)'
+}
+
+# Use the map_and_select function to handle mapping and selection for Previous Qualification
+previous_qualification = map_and_select('Previous Qualification', previous_qualification_mapping)
+
+## Output the selected value using st.write
+#st.sidebar.write("Previous Qualification:", selected_previous_qualification_label)
+
+#previous_qualification = st.sidebar.selectbox('Previous Qualification', options=range(1, 20))  # Update range based on actual qualifications
+#st.sidebar.write("Previous Qualification:", previous_qualification)
+
+nationality_mapping = {
+    1: 'Portuguese', 2: 'German', 3: 'Spanish', 4: 'Italian', 5: 'Dutch', 6: 'English',
+    7: 'Lithuanian', 8: 'Angolan', 9: 'Cape Verdean', 10: 'Guinean', 11: 'Mozambican',
+    12: 'Santomean', 13: 'Turkish', 14: 'Brazilian', 15: 'Romanian', 16: 'Moldova (Republic of)',
+    17: 'Mexican', 18: 'Ukrainian', 19: 'Russian', 20: 'Cuban', 21: 'Colombian'
+}
+
+nationality = map_and_select('Nationality', nationality_mapping)
+
+#nationality = st.sidebar.selectbox('Nationality', options=range(1, 200))  # Update range based on actual nationality codes
+#st.sidebar.write("nationality:", nationality)
+
+qualification_mapping = {
+    1: 'Secondary Education',
+    2: 'Higher Education - Undergraduate',
+    3: 'Higher Education - Undergraduate',
+    4: 'Higher Education - Graduate',
+    5: 'Higher Education - Graduate',
+    6: 'Higher Education - Undergraduate',
+    7: 'Primary Education',
+    8: 'Primary Education',
+    9: 'Primary Education',
+    10: 'Secondary Education',
+    11: 'Secondary Education',
+    12: 'Secondary Education',
+    13: 'Secondary Education',
+    14: 'Secondary Education',
+    15: 'Secondary Education',
+    16: 'Vocational/Technical',
+    17: 'Secondary Education',
+    18: 'Primary Education',
+    19: 'Secondary Education',
+    20: 'Primary Education',
+    21: 'Primary Education',
+    22: 'Secondary Education',
+    23: 'Secondary Education',
+    24: 'Unknown',
+    25: 'Primary Education',
+    26: 'Primary Education',
+    27: 'Primary Education',
+    28: 'Primary Education',
+    29: 'Vocational/Technical',
+    30: 'Higher Education - Undergraduate',
+    31: 'Higher Education - Undergraduate',
+    32: 'Higher Education - Undergraduate',
+    33: 'Higher Education - Graduate',
+    34: 'Higher Education - Graduate'
+}
+
+mother_qualification = map_and_select('Mother\'s Qualification', qualification_mapping)
+
+#mother_qualification = st.sidebar.selectbox('Mother\'s Qualification', options=range(1, 20))
+#st.sidebar.write("Mother Qualification:", mother_qualification)
+
+father_qualification = map_and_select('Father\'s Qualification', qualification_mapping)
+
+#father_qualification = st.sidebar.selectbox('Father\'s Qualification', options=range(1, 20))
+#st.sidebar.write("Father Qualification:", father_qualification)
+
+occupation_mapping = {
+    1: 'Student',
+    2: 'Representatives of the Legislative Power and Executive Bodies, Directors, Directors and Executive Managers',
+    3: 'Specialists in Intellectual and Scientific Activities',
+    4: 'Intermediate Level Technicians and Professions',
+    5: 'Administrative staff',
+    6: 'Personal Services, Security and Safety Workers, and Sellers',
+    7: 'Farmers and Skilled Workers in Agriculture, Fisheries, and Forestry',
+    8: 'Skilled Workers in Industry, Construction, and Craftsmen',
+    9: 'Installation and Machine Operators and Assembly Workers',
+    10: 'Unskilled Workers',
+    11: 'Armed Forces Professions',
+    12: 'Other Situation',
+    13: '(blank)',
+    14: 'Armed Forces Officers',
+    15: 'Armed Forces Sergeants',
+    16: 'Other Armed Forces personnel',
+    17: 'Directors of administrative and commercial services',
+    18: 'Hotel, catering, trade, and other services directors',
+    19: 'Specialists in the physical sciences, mathematics, engineering, and related techniques',
+    20: 'Health professionals',
+    21: 'Teachers',
+    22: 'Specialists in finance, accounting, administrative organization, and public and commercial relations',
+    23: 'Intermediate level science and engineering technicians and professions',
+    24: 'Technicians and professionals of intermediate level of health',
+    25: 'Intermediate level technicians from legal, social, sports, cultural, and similar services',
+    26: 'Information and communication technology technicians',
+    27: 'Office workers, secretaries in general, and data processing operators',
+    28: 'Data, accounting, statistical, financial services, and registry-related operators',
+    29: 'Other administrative support staff',
+    30: 'Personal service workers',
+    31: 'Sellers',
+    32: 'Personal care workers and the like',
+    33: 'Protection and security services personnel',
+    34: 'Market-oriented farmers and skilled agricultural and animal production workers',
+    35: 'Farmers, livestock keepers, fishermen, hunters and gatherers, and subsistence',
+    36: 'Skilled construction workers and the like, except electricians',
+    37: 'Skilled workers in metallurgy, metalworking, and similar',
+    38: 'Skilled workers in electricity and electronics',
+    39: 'Workers in food processing, woodworking, and clothing and other industries and crafts',
+    40: 'Fixed plant and machine operators',
+    41: 'Assembly workers',
+    42: 'Vehicle drivers and mobile equipment operators',
+    43: 'Unskilled workers in agriculture, animal production, and fisheries and forestry',
+    44: 'Unskilled workers in extractive industry, construction, manufacturing, and transport',
+    45: 'Meal preparation assistants',
+    46: 'Street vendors (except food) and street service providers'
+}
+
+mother_occupation = map_and_select('Mother\'s Occupation', occupation_mapping)
+
+#mother_occupation = st.sidebar.selectbox('Mother\'s Occupation', options=range(1, 50))  # Update range based on actual occupations
+#st.sidebar.write("Mother Occupation:", mother_occupation)
+
+father_occupation = map_and_select('Father\'s Occupation', occupation_mapping)
+
+#father_occupation = st.sidebar.selectbox('Father\'s Occupation', options=range(1, 50))
+#st.sidebar.write("Father Occupation:", father_occupation)
+
+
+displaced_mapping = {
+    1: 'Yes',
+    0: 'No'
+}
+
+displaced = map_and_select('Displaced', displaced_mapping)
+
+#displaced = st.sidebar.radio('Displaced', options=[0, 1], format_func=lambda x: 'No' if x == 0 else 'Yes')
+#st.sidebar.write("Displaced:", displaced)
+
+educational_special_needs_mapping = {
+    1: 'Yes',
+    0: 'No'
+}
+
+debtor_mapping = {
+    1: 'Yes',
+    0: 'No'
+}
+
+educational_special_needs = map_and_select('Educational Special Needs', educational_special_needs_mapping)
+#st.sidebar.write("Educational Special Needs:", educational_special_needs_mapping[educational_special_needs])
+
+debtor = map_and_select('Debtor', debtor_mapping)
+#st.sidebar.write("Debtor:", debtor_mapping[debtor])
+
+
+#educational_special_needs = st.sidebar.radio('Educational Special Needs', options=[0, 1], format_func=lambda x: 'No' if x == 0 else 'Yes')
+#st.sidebar.write("Educational Special Needs:", educational_special_needs)
+
+#debtor = st.sidebar.radio('Debtor', options=[0, 1], format_func=lambda x: 'No' if x == 0 else 'Yes')
+#st.sidebar.write("Debtor:", debtor)
+
+# Example usage for single input
+
+tuition_fees_up_to_date = map_and_select('Tuition Fees Up to Date', 5000, min_value=0, max_value=10000)
+
+
+#tuition_fees_up_to_date = st.sidebar.number_input('Tuition Fees Up to Date', min_value=0, max_value=10000, value=5000)
+#st.sidebar.write("tuition_fees_up_to_date:", tuition_fees_up_to_date)
+
+# Gender replacement
+gender_mapping = {
+    1: 'male',
+    0: 'female'
+}
+
+gender = map_and_select('Gender', gender_mapping)
+
+#gender = st.sidebar.radio('Gender', options=[1, 2], format_func=lambda x: 'Male' if x == 1 else 'Female')
+#st.sidebar.write("gender:", gender)
+
+scholarship_mapping = {
+    1: 'Yes',
+    0: 'No'
+}
+
+scholarship_holder = map_and_select('Scholarship Holder', scholarship_mapping)
+#st.sidebar.write("Scholarship holder:", scholarship_mapping[scholarship_holder])
+
+#scholarship_holder = st.sidebar.radio('Scholarship Holder', options=[0, 1], format_func=lambda x: 'No' if x == 0 else 'Yes')
+#st.sidebar.write("Scholarship holder:", scholarship_holder)
+
+# Example usage for single input
+age_at_enrollment = map_and_select('Age at Enrollment', 16, min_value=6, max_value=18)
+
+#age_at_enrollment = st.sidebar.number_input('Age at Enrollment', min_value=16, max_value=60, value=18)
+#st.sidebar.write("Age at Enrollment:", age_at_enrollment)
+
+international_mapping = {
+    1: 'Yes',
+    0: 'No'
+}
+
+international = map_and_select('International', international_mapping)
+#st.sidebar.write("International:", international_mapping[international])
+
+#international = st.sidebar.radio('International', options=[0, 1], format_func=lambda x: 'No' if x == 0 else 'Yes')
+#st.sidebar.write("International:", international)
+
+
+unemployment_rate = map_and_select('Unemployment Rate', 10.8, min_value=0.0, max_value=100.0 )
+#st.sidebar.write("unemployment_rate:", unemployment_rate)
+
+
+#unemployment_rate = st.sidebar.slider('Unemployment Rate', min_value=0.0, max_value=100.0, value=10.8)
+#st.sidebar.write("Unemployment Rate:", unemployment_rate)
+
+inflation_rate = map_and_select('Inflation Rate', 1.4, min_value=-10.0, max_value=30.0)
+#st.sidebar.write("inflation_rate:", inflation_rate)
+
+# Use map_and_select for the inflation_rate
+#inflation_rate = map_and_select(1, 'Inflation Rate', 1.4)
+#st.sidebar.write("Inflation Rate:", inflation_rate)
+
+#inflation_rate = st.sidebar.slider('Inflation Rate', min_value=-10.0, max_value=30.0, value=1.4)
+#st.sidebar.write("Inflation Rate:", inflation_rate)
+
+#gdp = st.sidebar.number_input('GDP', min_value=0.0, max_value=100.0, value=1.74)
+#st.sidebar.write("GDP:", gdp)
+
+# Use map_and_select for the inflation_rate
+gdp = map_and_select('GDP', 1.74, min_value=0.0, max_value=100.0)
+#st.sidebar.write("Inflation Rate:", gdp)
+
+#st.header('Curricular Units 1st Semester')
+#credited_1st_sem = st.sidebar.number_input('Credited Units 1st Semester', min_value=0, step=1)
+#st.sidebar.write("Credited Units 1st Semester:", credited_1st_sem)
+#enrolled_1st_sem = st.sidebar.number_input('Enrolled Units 1st Semester', min_value=0, step=1)
+#st.sidebar.write("Enrolled Units 1st Semester:", enrolled_1st_sem)
+#evaluations_1st_sem = st.sidebar.number_input('Evaluations 1st Semester', min_value=0, step=1)
+#st.sidebar.write("Evaluations 1st Semester:", evaluations_1st_sem)
+#approved_1st_sem = st.sidebar.number_input('Approved Units 1st Semester', min_value=0, step=1)
+#st.sidebar.write("Approved Units 1st Semester:", approved_1st_sem)
+#grade_1st_sem = st.sidebar.number_input('Grade 1st Semester', min_value=0.0, max_value=10.0, step=0.1)
+#st.sidebar.write("Grade 1st Semester:", grade_1st_sem)
+#without_evaluations_1st_sem = st.sidebar.number_input('Units without Evaluations 1st Semester', min_value=0, step=1)
+#st.sidebar.write("GDP:", without_evaluations_1st_sem)
+
+# Use map_and_select for the various inputs
+credited_1st_sem = map_and_select('Credited Units 1st Semester', 0, min_value=0, step=1)
+#st.sidebar.write("Credited Units 1st Semester:", credited_1st_sem)
+
+enrolled_1st_sem = map_and_select('Enrolled Units 1st Semester', 0, min_value=0, step=1)
+#st.sidebar.write("Enrolled Units 1st Semester:", enrolled_1st_sem)
+
+evaluations_1st_sem = map_and_select('Evaluations 1st Semester', 0, min_value=0, step=1)
+#st.sidebar.write("Evaluations 1st Semester:", evaluations_1st_sem)
+
+approved_1st_sem = map_and_select('Approved Units 1st Semester', 0, min_value=0, step=1)
+#st.sidebar.write("Approved Units 1st Semester:", approved_1st_sem)
+
+grade_1st_sem = map_and_select('Grade 1st Semester', 0.0, min_value=0.0, max_value=10.0, step=0.1)
+#st.sidebar.write("Grade 1st Semester:", grade_1st_sem)
+
+without_evaluations_1st_sem = map_and_select('Units without Evaluations 1st Semester', 0, min_value=0, step=1)
+#st.sidebar.write("Units without Evaluations 1st Semester:", without_evaluations_1st_sem)
+
+
+#st.sidebar.header('Curricular Units 2nd Semester')
+#credited_2nd_sem = st.sidebar.number_input('Credited Units 2nd Semester', min_value=0, step=1)
+#st.sidebar.write("Credited Units 2nd Semester:", credited_2nd_sem)
+#enrolled_2nd_sem = st.sidebar.number_input('Enrolled Units 2nd Semester', min_value=0, step=1)
+#st.sidebar.write("Enrolled Units 2nd Semester:", enrolled_2nd_sem)
+#evaluations_2nd_sem = st.sidebar.number_input('Evaluations 2nd Semester', min_value=0, step=1)
+#st.sidebar.write("Evaluations 2nd Semester:", evaluations_2nd_sem)
+#approved_2nd_sem = st.sidebar.number_input('Approved Units 2nd Semester', min_value=0, step=1)
+#st.sidebar.write("Approved Units 2nd Semester:", approved_2nd_sem)
+#grade_2nd_sem = st.sidebar.number_input('Grade 2nd Semester', min_value=0.0, max_value=10.0, step=0.1)
+#st.sidebar.write("Grade 2nd Semester:", grade_2nd_sem)
+#without_evaluations_2nd_sem = st.sidebar.number_input('Units without Evaluations 2nd Semester', min_value=0, step=1)
+#st.sidebar.write("Units without Evaluations 2nd Semester:", without_evaluations_2nd_sem)
+
+# Use map_and_select for the various inputs for the 2nd semester
+credited_2nd_sem = map_and_select('Credited Units 2nd Semester', 0, min_value=0, step=1)
+#st.sidebar.write("Credited Units 2nd Semester:", credited_2nd_sem)
+
+enrolled_2nd_sem = map_and_select('Enrolled Units 2nd Semester', 0, min_value=0, step=1)
+#st.sidebar.write("Enrolled Units 2nd Semester:", enrolled_2nd_sem)
+
+evaluations_2nd_sem = map_and_select('Evaluations 2nd Semester', 0, min_value=0, step=1)
+#st.sidebar.write("Evaluations 2nd Semester:", evaluations_2nd_sem)
+
+approved_2nd_sem = map_and_select('Approved Units 2nd Semester', 0, min_value=0, step=1)
+#st.sidebar.write("Approved Units 2nd Semester:", approved_2nd_sem)
+
+grade_2nd_sem = map_and_select('Grade 2nd Semester', 0.0, min_value=0.0, max_value=10.0, step=0.1)
+#st.sidebar.write("Grade 2nd Semester:", grade_2nd_sem)
+
+without_evaluations_2nd_sem = map_and_select('Units without Evaluations 2nd Semester', 0, min_value=0, step=1)
+#st.sidebar.write("Units without Evaluations 2nd Semester:", without_evaluations_2nd_sem)
+
+input_data = {
+'Marital_Status': marital_status,
+'Application_Mode' : application_mode,
+'Application_Order': application_order,
+'Course': course,
+'Attendance': daytime_evening_attendance,
+'Previous_Qualification': previous_qualification,
+'Nationality': nationality,
+'Mother_Qualification': mother_qualification,
+'Father_Qualification': father_qualification,
+'Mother_Occupation': mother_occupation,
+'Father_Occupation': father_occupation,
+'Displaced': displaced,
+'Special_Needs': educational_special_needs,
+'Debtor': debtor,
+'Fees_UpToDate':tuition_fees_up_to_date,
+'Gender': gender,
+'Scholarship_Holder': scholarship_holder,
+'Age': age_at_enrollment,
+'International': international,
+'1st_Sem_Credits': credited_1st_sem,
+'1st_Sem_Enrolled': enrolled_1st_sem,
+'1st_Sem_Evaluations': evaluations_1st_sem,
+'1st_Sem_Approved': approved_1st_sem,
+'1st_Sem_Grade': grade_1st_sem,
+'1st_Sem_No_Evaluations': without_evaluations_1st_sem,
+'2nd_Sem_Credits': credited_2nd_sem,
+'2nd_Sem_Enrolled': enrolled_2nd_sem,
+'2nd_Sem_Evaluations': evaluations_2nd_sem,
+'2nd_Sem_Approved': approved_2nd_sem,
+'2nd_Sem_Grade': grade_2nd_sem,
+'2nd_Sem_No_Evaluations': without_evaluations_2nd_sem,
+'Unemployment_Rate': unemployment_rate,
+'Inflation_Rate': inflation_rate,
+'GDP':gdp
+}
+
+if st.sidebar.button('Predict Dropout'):
+    try:
+        with st.spinner("Predicting..."):
+            # Simulate a long-running prediction process
+            progress_bar = st.progress(0)
+            for i in range(5):  # Simulate progress
+                time.sleep(0.1)  # Sleep for a short period to simulate work
+                progress_bar.progress((i + 1) * 20)
+
+            # Convert input dictionary to a 2D array
+            input_array = np.array(list(input_data.values())).reshape(1, -1)
+
+            # Perform prediction
+            dropout_label = predict_dropout(input_array, model, model_type)
+            dropout_label, emoji, explanation = map_dropout_prediction(dropout_label)
+
+            # Display the prediction result
+            st.success("Prediction complete!")
+            st.write(f"Prediction: {dropout_label} {emoji}")
+            st.write(explanation)
+
+            # Display images
+            if dropout_label == "Dropout":
+                st.image("dropout_image.webp", caption="Image representing a dropout student", use_column_width=True)
+            else:
+                st.image("not_dropout_image.webp", caption="Image representing a non-dropout student", use_column_width=True)
+
+    except Exception as e:
+        st.error(f"An error occurred: {str(e)}")