Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,167 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import plotly.express as px
+from io import BytesIO
+import base64
+
+# 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:
+    if uploaded_file.name.endswith(".xlsx"):
+        df = pd.read_excel(uploaded_file)
+    else:
+        df = pd.read_csv(uploaded_file)
+
+    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")
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+else:
+    st.warning("Upload the merged dataset to start the analysis.")