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3cbcce0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 | import streamlit as st
import pandas as pd
import joblib
import matplotlib.pyplot as plt
import seaborn as sns
import shap
import numpy as np
# -------------------------------
# Page Config & Theme
# -------------------------------
st.set_page_config(page_title="StudentPass", page_icon="🎓", layout="wide")
primary_color = "#4B79A1" # StudentPass branding
secondary_color = "#F4D35E"
st.markdown(f"""
<style>
[data-testid="stSidebar"] {{
background-color: {primary_color};
}}
.stButton>button {{
background-color: {secondary_color};
color: black;
}}
</style>
""", unsafe_allow_html=True)
# -------------------------------
# Load Model
# -------------------------------
@st.cache_data
def load_model():
return joblib.load("model/performance_pipeline.pkl")
model = load_model()
# -------------------------------
# Feature lists
# -------------------------------
categorical_features = ['school','sex','address','famsize','Pstatus','Mjob','Fjob','reason','guardian',
'schoolsup','famsup','paid','activities','nursery','higher','internet','romantic','dataset']
numeric_features = ['age','Medu','Fedu','traveltime','studytime','failures','famrel','freetime',
'goout','Dalc','Walc','health','absences','G1','G2','G3']
# -------------------------------
# Sidebar Navigation
# -------------------------------
st.sidebar.title("StudentPass")
app_mode = st.sidebar.selectbox("Choose the page:",
["Home", "Single Prediction", "Batch Prediction", "Statistics", "About"])
# -------------------------------
# Helper Functions
# -------------------------------
def get_dynamic_options(df, col):
if df[col].dtype == 'object':
return df[col].unique().tolist()
return None
# -------------------------------
# Home Page
# -------------------------------
if app_mode == "Home":
st.title("🎓 StudentPass")
st.markdown("""
Welcome to **StudentPass**, your student performance predictor.
**Features:**
- Predict student pass/fail outcome
- Single prediction or batch predictions via CSV
- View statistics and charts
- Understand model predictions with explanations
""")
# -------------------------------
# Single Prediction Page
# -------------------------------
elif app_mode == "Single Prediction":
st.title("Single Student Prediction")
sample_file = st.file_uploader("Upload CSV for dynamic categorical options (optional)", type="csv", key="single_sample")
sample_df = pd.read_csv(sample_file) if sample_file else None
with st.form("single_prediction_form"):
st.subheader("Categorical Features")
cat_inputs = {}
for col in categorical_features:
if sample_df is not None and col in sample_df.columns:
options = get_dynamic_options(sample_df, col)
if options is not None:
cat_inputs[col] = st.selectbox(col, options=options)
else:
cat_inputs[col] = st.text_input(col)
else:
# Default options for known categorical features
if col in ['schoolsup','famsup','paid','activities','nursery','higher','internet','romantic']:
cat_inputs[col] = st.selectbox(col, ["yes", "no"])
elif col == 'school':
cat_inputs[col] = st.selectbox(col, ["GP", "MS"])
elif col == 'address':
cat_inputs[col] = st.selectbox(col, ["U", "R"])
elif col == 'famsize':
cat_inputs[col] = st.selectbox(col, ["GT3", "LE3"])
elif col == 'Pstatus':
cat_inputs[col] = st.selectbox(col, ["T", "A"])
elif col in ['Mjob','Fjob']:
cat_inputs[col] = st.selectbox(col, ["teacher","health","services","at_home","other"])
elif col == 'reason':
cat_inputs[col] = st.selectbox(col, ["home","reputation","course","other"])
elif col == 'guardian':
cat_inputs[col] = st.selectbox(col, ["mother","father","other"])
else:
cat_inputs[col] = st.text_input(col)
st.subheader("Numeric Features")
num_inputs = {}
for col in numeric_features:
if sample_df is not None and col in sample_df.columns:
min_val = int(sample_df[col].min())
max_val = int(sample_df[col].max())
median_val = int(sample_df[col].median())
else:
min_val, max_val, median_val = 0, 100, 0
num_inputs[col] = st.number_input(col, min_value=min_val, max_value=max_val, value=median_val)
submitted = st.form_submit_button("Predict")
if submitted:
input_df = pd.DataFrame([{**cat_inputs, **num_inputs}])
if 'dataset' not in df.columns:
input_df['dataset'] = 'student_mat' # or any default value used during training
prediction = model.predict(input_df)[0]
st.success(f"Predicted Performance: **{prediction}**")
# -------------------------------
# SHAP Explanation
# -------------------------------
try:
explainer = shap.Explainer(model.predict, input_df)
shap_values = explainer(input_df)
st.subheader("Feature Importance (SHAP)")
shap.initjs()
shap.plots.bar(shap_values, show=False)
except Exception as e:
st.warning("SHAP explanation could not be generated. Model may not be compatible.")
# -------------------------------
# Batch Prediction Page
# -------------------------------
elif app_mode == "Batch Prediction":
st.title("Batch Prediction via CSV Upload")
st.markdown("Upload CSV file with student data to get predictions.")
uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
# Fallback to local CSV if no upload
if uploaded_file:
df = pd.read_csv(uploaded_file)
else:
try:
df = pd.read_csv("student_mat.csv")
st.info("No CSV uploaded. Using local student_mat.csv as default dataset.")
except FileNotFoundError:
df = None
st.warning("No CSV uploaded and local student_mat.csv not found. Please upload a CSV.")
# Proceed if df is available
if df is not None:
st.write("Data Preview:")
st.dataframe(df.head())
if st.button("Predict Batch"):
if 'dataset' not in df.columns:
df['dataset'] = 'student_mat' # or any default value used during training
preds = model.predict(df)
df["Prediction"] = preds
st.success("Predictions added to data")
st.dataframe(df.head())
# -------------------------------
# Interactive Charts
# -------------------------------
st.subheader("Prediction Counts")
pred_counts = df["Prediction"].value_counts()
fig, ax = plt.subplots()
sns.barplot(x=pred_counts.index, y=pred_counts.values, palette="coolwarm", ax=ax)
ax.set_ylabel("Count")
st.pyplot(fig)
st.subheader("Pass/Fail Pie Chart")
fig2, ax2 = plt.subplots()
ax2.pie(pred_counts.values, labels=pred_counts.index, autopct="%1.1f%%", colors=["#4B79A1","#F4D35E"])
st.pyplot(fig2)
# Download option
csv = df.to_csv(index=False).encode()
st.download_button("Download Predictions CSV", data=csv, file_name="predictions.csv", mime="text/csv")
# -------------------------------
# Statistics Page
# -------------------------------
elif app_mode == "Statistics":
st.title("Student Data Statistics")
st.markdown("Upload dataset to view charts and statistics.")
uploaded_file = st.file_uploader("Upload CSV for statistics", type="csv", key="stats")
if uploaded_file:
df = pd.read_csv(uploaded_file)
st.write(df.describe())
st.subheader("Correlation Heatmap")
fig, ax = plt.subplots(figsize=(10,8))
sns.heatmap(df.corr(), annot=True, cmap="coolwarm", ax=ax)
st.pyplot(fig)
st.subheader("Feature Distributions")
feature = st.selectbox("Select feature for histogram", numeric_features)
fig2, ax2 = plt.subplots()
sns.histplot(df[feature], kde=True, ax=ax2, color=secondary_color)
st.pyplot(fig2)
# -------------------------------
# About Page
# -------------------------------
elif app_mode == "About":
st.title("About StudentPass")
st.markdown("""
**StudentPass** is a web application built with Streamlit to predict student performance.
**Technologies used:**
- Python, Streamlit
- scikit-learn
- pandas, matplotlib, seaborn
- Joblib for model persistence
- SHAP for explainability
Developed to help educators and students understand performance trends and predictions.
""")
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