app.py

import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import datetime
import time

# ==========================================
# 1. CONFIGURATION & MOCK DATA GENERATION
# ==========================================
st.set_page_config(
    page_title="WDE Accountability Dashboard",
    page_icon="🤠",
    layout="wide"
)

# Simulated Wyoming Districts
WY_DISTRICTS = [
    "Laramie County SD #1",
    "Natrona County SD #1",
    "Sheridan County SD #2",
    "Teton County SD #1",
    "Albany County SD #1"
]

# Simulated "Nightly Update" Data Generator
@st.cache_data(ttl=3600)  # Cache mimics the static nature of nightly builds
def load_data(simulation_date):
    """
    Simulates fetching data from a Data Warehouse updated nightly.
    """
    data = []
    
    # Generate mock data
    for dist in WY_DISTRICTS:
        # Create 3 schools per district
        for school_level in ['Elementary', 'Middle', 'High']:
            school_name = f"{dist.split(' ')[0]} {school_level}"
            
            # Generate 50 students per school for the prototype
            for i in range(50):
                student_id = f"WY-{np.random.randint(100000, 999999)}"
                
                # Simulate WY-TOPP Scores (Scale 200-800)
                math_score = np.random.normal(500, 50) + (20 if "Teton" in dist else 0)
                ela_score = np.random.normal(510, 45)
                attendance_rate = np.clip(np.random.normal(92, 5), 50, 100)
                
                data.append({
                    "District": dist,
                    "School": school_name,
                    "Student_ID": student_id,
                    "Grade_Level": np.random.choice([3, 4, 5, 6, 7, 8, 9, 10, 11]),
                    "WY_TOPP_Math": int(math_score),
                    "WY_TOPP_ELA": int(ela_score),
                    "Attendance_Pct": round(attendance_rate, 1),
                    "At_Risk": "Yes" if attendance_rate < 85 or math_score < 450 else "No",
                    "Last_Updated": simulation_date
                })
    
    return pd.DataFrame(data)

# ==========================================
# 2. AUTHENTICATION & SECURITY (RBAC)
# ==========================================
# In production, connect this to SSO / LDAP / Active Directory
USERS = {
    "admin": {"password": "password123", "role": "State_Admin", "access": "All"},
    "laramie_supt": {"password": "wyo", "role": "District_Admin", "access": "Laramie County SD #1"},
    "teton_principal": {"password": "mountains", "role": "School_Admin", "access": "Teton Elementary"},
}

def login():
    st.markdown("## 🔒 WDE Secure Login")
    
    if "authenticated" not in st.session_state:
        st.session_state["authenticated"] = False

    if not st.session_state["authenticated"]:
        username = st.text_input("Username")
        password = st.text_input("Password", type="password")
        
        if st.button("Login"):
            if username in USERS and USERS[username]["password"] == password:
                st.session_state["authenticated"] = True
                st.session_state["user"] = username
                st.session_state["role"] = USERS[username]["role"]
                st.session_state["access"] = USERS[username]["access"]
                st.rerun()
            else:
                st.error("Invalid credentials")
    else:
        return True
    return False

def logout():
    st.session_state["authenticated"] = False
    st.rerun()

# ==========================================
# 3. DASHBOARD LOGIC
# ==========================================
def main_dashboard():
    # --- Sidebar ---
    st.sidebar.title("Navigation")
    st.sidebar.write(f"Logged in as: **{st.session_state['user']}**")
    st.sidebar.write(f"Role: *{st.session_state['role']}*")
    
    if st.sidebar.button("Logout"):
        logout()
        
    st.sidebar.markdown("---")
    
    # Simulate Nightly Update Info
    today = datetime.date.today()
    st.sidebar.info(f"📅 Data Current As Of: \n{today} 03:00 AM MST")
    
    # --- Data Loading ---
    df = load_data(today)
    
    # --- Security Filter (Row Level Security) ---
    # Filter data based on user role before it hits the UI
    if st.session_state["access"] != "All":
        if st.session_state["role"] == "District_Admin":
            df = df[df["District"] == st.session_state["access"]]
        elif st.session_state["role"] == "School_Admin":
            df = df[df["School"] == st.session_state["access"]]

    # --- Page Content ---
    st.title("🏔️ Wyoming Accountability & Analytics")
    
    # Top Level Metrics
    col1, col2, col3, col4 = st.columns(4)
    col1.metric("Total Students", f"{len(df)}")
    col2.metric("Avg Math Score", f"{int(df['WY_TOPP_Math'].mean())}")
    col3.metric("Avg Attendance", f"{df['Attendance_Pct'].mean():.1f}%")
    
    at_risk_count = len(df[df["At_Risk"] == "Yes"])
    col4.metric("At-Risk Students", f"{at_risk_count}", delta="-Action Required", delta_color="inverse")

    st.markdown("---")

    # --- Visualizations ---
    
    # 1. Drill Down Controls
    st.subheader("📊 Performance Analytics")
    
    view_type = st.radio("Analyze by:", ["District", "School", "Grade_Level"], horizontal=True)
    
    # Group data based on selection
    agg_df = df.groupby(view_type)[["WY_TOPP_Math", "WY_TOPP_ELA", "Attendance_Pct"]].mean().reset_index()
    
    # Chart
    fig = px.bar(
        agg_df, 
        x=view_type, 
        y=["WY_TOPP_Math", "WY_TOPP_ELA"], 
        barmode='group',
        title=f"Average WY-TOPP Scores by {view_type}",
        color_discrete_sequence=["#F2A900", "#53565A"] # Wyoming Colors (approx)
    )
    st.plotly_chart(fig, use_container_width=True)

    # 2. Risk Analysis
    c1, c2 = st.columns(2)
    
    with c1:
        st.subheader("Attendance vs. Performance")
        fig2 = px.scatter(
            df, 
            x="Attendance_Pct", 
            y="WY_TOPP_Math", 
            color="At_Risk",
            hover_data=["Student_ID", "School"],
            title="Correlation: Attendance vs Math Scores"
        )
        st.plotly_chart(fig2, use_container_width=True)
        
    with c2:
        st.subheader("At-Risk Distribution")
        risk_dist = df['At_Risk'].value_counts()
        fig3 = px.pie(values=risk_dist, names=risk_dist.index, hole=0.4, color_discrete_sequence=['#2ecc71', '#e74c3c'])
        st.plotly_chart(fig3, use_container_width=True)

    # --- Data Grid (Secure View) ---
    st.subheader("📂 Student Data Details")
    
    # Search / Filter
    text_search = st.text_input("Search Student ID", "")
    grade_filter = st.multiselect("Filter by Grade", sorted(df["Grade_Level"].unique()))
    
    filtered_df = df.copy()
    if text_search:
        filtered_df = filtered_df[filtered_df["Student_ID"].str.contains(text_search)]
    if grade_filter:
        filtered_df = filtered_df[filtered_df["Grade_Level"].isin(grade_filter)]
        
    # Styling the dataframe (Highlighting low attendance)
    st.dataframe(
        filtered_df.style.map(lambda x: 'color: red; font-weight: bold' if isinstance(x, (int, float)) and x < 85 else '', subset=['Attendance_Pct']),
        use_container_width=True,
        hide_index=True
    )
    
    # Export Button (Audit Log Placeholder)
    if st.button("📥 Export Report to CSV"):
        st.toast("Export started... Logged in Audit Trail.", icon="✅")
        # In production, this would trigger a download and write to an SQL audit log

# ==========================================
# 4. APP ENTRY POINT
# ==========================================
if __name__ == "__main__":
    if login():
        main_dashboard()