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| 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.") | |