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5c948de 448a580 5de0e74 448a580 b983e61 448a580 5de0e74 e64ed76 5de0e74 e64ed76 5de0e74 e64ed76 5de0e74 448a580 5c948de 448a580 5de0e74 | 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 | import streamlit as st
import pandas as pd
import plotly.express as px
from io import BytesIO
import base64
import os
import os
os.environ["STREAMLIT_RUNTIME_DIR"] = "/tmp"
os.environ["STREAMLIT_CACHE_DIR"] = "/tmp"
# PAGE CONFIGURATION
st.set_page_config(page_title="Telangana Minorities Residential Educational Institutions Society", layout="wide")
# Custom CSS for UI
st.markdown("""
<style>
.big-font {font-size:20px !important; font-weight:600;}
.kpi-box {background-color:#f9f9f9; padding:15px; border-radius:10px; text-align:center; border: 1px solid #ddd;}
</style>
""", unsafe_allow_html=True)
# DATA UPLOAD
st.title("Telangana Minorities Residential Educational Institutions Society Analysis Dashboard")
uploaded_file = st.file_uploader("Upload your dataset (Excel or CSV)", type=["xlsx", "csv"])
if uploaded_file:
# ✅ Save uploaded file to /tmp to avoid permission error
save_path = os.path.join("/tmp", uploaded_file.name)
with open(save_path, "wb") as f:
f.write(uploaded_file.getbuffer())
# Read dataset
if save_path.endswith(".xlsx"):
df = pd.read_excel(save_path)
else:
df = pd.read_csv(save_path)
st.success("Dataset uploaded successfully!")
# Standardize column names
df.columns = df.columns.str.strip().str.lower().str.replace(" ", "_")
df.fillna(0, inplace=True)
# DYNAMIC COLUMN DETECTION
def find_column(keyword):
for col in df.columns:
if keyword in col:
return col
return None
columns = {
"v_minority": {
"sanctioned": find_column("vth_class_minority_sanction"),
"admitted": find_column("vth_class_minority_admitted"),
"vacancy": find_column("vth_class_minority_vacancies"),
"attendance": find_column("total_school_attendance"),
},
"v_non_minority": {
"sanctioned": find_column("vth_class_non_minority_sanction"),
"admitted": find_column("vth_class_non_minority_admitted"),
"vacancy": find_column("vth_class_non_minority_vacancies"),
"attendance": find_column("total_school_attendance"),
},
"inter_minority": {
"sanctioned": find_column("1st_year_minority_sanction"),
"admitted": find_column("1st_year_minority_admitted"),
"vacancy": find_column("1st_year_minority_vacancies"),
"attendance": find_column("total_intermediate_attendance"),
},
"inter_non_minority": {
"sanctioned": find_column("1st_year_non_minority_sanction"),
"admitted": find_column("1st_year_non_minority_admitted"),
"vacancy": find_column("1st_year_non_minority_vacancies"),
"attendance": find_column("total_intermediate_attendance"),
},
"absentees": find_column("total_absentees"),
}
# FILTERS
st.sidebar.header("Filters")
level_filter = st.sidebar.radio("Select Level", ["V (School)", "Inter 1st Year"])
category_filter = st.sidebar.radio("Select Category", ["Minority", "Non-Minority"])
districts = st.sidebar.multiselect("Select District(s)", options=df["district"].unique(), default=df["district"].unique())
search_college = st.sidebar.text_input("Search College Name (Optional)")
df = df[df["district"].isin(districts)]
if search_college:
df = df[df["college_name"].str.contains(search_college, case=False, na=False)]
# Map columns dynamically
if level_filter == "V (School)":
key = "v_minority" if category_filter == "Minority" else "v_non_minority"
else:
key = "inter_minority" if category_filter == "Minority" else "inter_non_minority"
sanctioned_col = columns[key]["sanctioned"]
admitted_col = columns[key]["admitted"]
vacant_col = columns[key]["vacancy"]
attendance_col = columns[key]["attendance"]
absentees_col = columns["absentees"]
# KPI CALCULATIONS
total_sanctioned = df[sanctioned_col].sum()
total_admitted = df[admitted_col].sum()
total_vacant = df[vacant_col].sum()
total_attendance = df[attendance_col].sum()
total_absentees = df[absentees_col].sum()
attendance_pct = round((total_attendance / (total_attendance + total_absentees)) * 100, 2) if (total_attendance + total_absentees) > 0 else 0
# KPI DISPLAY
col1, col2, col3, col4, col5, col6 = st.columns(6)
col1.markdown(f"<div class='kpi-box'>🏫 <br><span class='big-font'>{total_sanctioned:,}</span><br>Total Sanctioned</div>", unsafe_allow_html=True)
col2.markdown(f"<div class='kpi-box'>🎓 <br><span class='big-font'>{total_admitted:,}</span><br>Total Admitted</div>", unsafe_allow_html=True)
col3.markdown(f"<div class='kpi-box'>📌 <br><span class='big-font'>{total_vacant:,}</span><br>Total Vacant</div>", unsafe_allow_html=True)
col4.markdown(f"<div class='kpi-box'>✅ <br><span class='big-font'>{total_attendance:,}</span><br>Total Attendance</div>", unsafe_allow_html=True)
col5.markdown(f"<div class='kpi-box'>❌ <br><span class='big-font'>{total_absentees:,}</span><br>Total Absentees</div>", unsafe_allow_html=True)
col6.markdown(f"<div class='kpi-box'>📈 <br><span class='big-font'>{attendance_pct}%</span><br>Attendance %</div>", unsafe_allow_html=True)
st.markdown("---")
# VISUALIZATIONS
df["admission_rate"] = (df[admitted_col] / df[sanctioned_col]) * 100
df["vacancy_rate"] = (df[vacant_col] / df[sanctioned_col]) * 100
df["attendance_%"] = (df[attendance_col] / (df[attendance_col] + df[absentees_col])) * 100
# 1. Vacancy Rate vs Admission Rate (Bubble)
st.plotly_chart(px.scatter(df, x="admission_rate", y="vacancy_rate", size="admission_rate",
color=attendance_col, hover_name="college_name",
title="Vacancy Rate vs Admission Rate"), use_container_width=True)
# 2. Attendance Impact on Vacancies
st.plotly_chart(px.scatter(df, x=attendance_col, y="vacancy_rate", title="Attendance Impact on Vacancy Rate", trendline="ols"), use_container_width=True)
# 3. Top 10 Districts by Absenteeism
absentee_summary = df.groupby("district")[absentees_col].sum().reset_index().sort_values(absentees_col, ascending=False).head(10)
st.plotly_chart(px.bar(absentee_summary, x="district", y=absentees_col, title="Top 10 Districts by Absenteeism"), use_container_width=True)
# 4. Vacancy Rate Distribution
st.plotly_chart(px.box(df, y="vacancy_rate", x="district", title="Vacancy Rate Distribution by District"), use_container_width=True)
# 5. Sanction vs Admission
stacked = df.groupby("district")[[sanctioned_col, admitted_col]].sum().reset_index()
stacked = stacked.melt(id_vars="district", value_vars=[sanctioned_col, admitted_col], var_name="Type", value_name="Seats")
st.plotly_chart(px.bar(stacked, x="district", y="Seats", color="Type", title="Sanctioned vs Admitted Seats"), use_container_width=True)
# 6. Top & Bottom 5 Institutes
top_5 = df.nlargest(5, "attendance_%")[["college_name", "attendance_%"]]
bottom_5 = df.nsmallest(5, "attendance_%")[["college_name", "attendance_%"]]
col1, col2 = st.columns(2)
with col1:
st.subheader("Top 5 Institutes (Attendance %)")
st.dataframe(top_5)
with col2:
st.subheader("Bottom 5 Institutes (Attendance %)")
st.dataframe(bottom_5)
# DRILL-DOWN: SELECT DISTRICT
selected_district = st.selectbox("Search for a District for Detailed Institute View", df["district"].unique())
drill_df = df[df["district"] == selected_district][["college_name", sanctioned_col, admitted_col, vacant_col, attendance_col, absentees_col, "attendance_%"]]
st.dataframe(drill_df)
# DOWNLOAD DATA
def download_excel(dataframe):
output = BytesIO()
with pd.ExcelWriter(output, engine="xlsxwriter") as writer:
dataframe.to_excel(writer, index=False, sheet_name="Report")
return output.getvalue()
st.download_button("📥 Download Excel Report", data=download_excel(df), file_name="education_dashboard_report.xlsx")
else:
st.warning("Upload the merged dataset to start the analysis.")
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