Create app.py

app.py

@@ -0,0 +1,215 @@
+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()