Setup MVC and basic userdashboard with dummy data.

streamlit_src/admin_dashboard.py

@@ -1,14 +0,0 @@
-import streamlit as st
-
-def ask_for_admin_password():
-    password = st.text_input("Enter admin password", type="password")
-    if password == "admin":
-        return True
-    else:
-        return False
-
-def start_admin_dashboard():
-    if ask_for_admin_password():
-        st.title("Admin Dashboard")
-    else:
-        st.error("Invalid password. Please try again.")

streamlit_src/app.py

@@ -1,4 +1,4 @@
-import streamlit as st
-from user_dashboard import *
-
-show_user_dashboard()
+from controllers.home_controller import HomeController
+    
+if __name__ == "__main__":
+    HomeController()

streamlit_src/controllers/admin_controller.py

@@ -0,0 +1,29 @@
+from models.air_quality_model import AirQualityModel
+from views.admin_view import AdminView
+
+class AdminController:
+    def __init__(self):
+        self.model = AirQualityModel()
+        self.view = AdminView()
+
+    def show_dashboard(self):
+        return 
+        # Get today's data and predictions
+        today_data = self.model.get_today_data()
+        next_three_days = self.model.next_three_day_predictions()
+
+        # WHO Guidelines
+        who_guidelines = {
+            "Pollutant": ["NO2 (µg/m³)", "O3 (µg/m³)"],
+            "WHO Guideline": [self.model.WHO_NO2_LEVEL, self.model.WHO_O3_LEVEL]
+        }
+
+        # Display current data and predictions
+        self.view.show_current_data(today_data, who_guidelines)
+        self.view.display_predictions(next_three_days)
+
+        # Compare to WHO guidelines
+        self.view.compare_to_who(today_data, self.model.WHO_NO2_LEVEL, self.model.WHO_O3_LEVEL)
+        
+    def welcome_back(self):
+        self.view.welcome_back()

streamlit_src/controllers/home_controller.py

@@ -0,0 +1,44 @@
+import streamlit as st
+import time
+from controllers.user_controller import UserController
+from controllers.admin_controller import AdminController
+from views.home_view import HomeView
+import hashlib
+
+class HomeController:
+    __ADMIN_PASSWORD_HASH = '240be518fabd2724ddb6f04eeb1da5967448d7e831c08c8fa822809f74c720a9'
+
+    def __init__(self):
+        self.home_view = HomeView()
+        self.user_controller = UserController()
+        self.admin_controller = AdminController()
+        self._run()
+        
+    def _hash_password(self, password):
+        """Hash the input password using SHA-256."""
+        return hashlib.sha256(password.encode()).hexdigest()
+
+    def _run(self):
+        if "is_admin_logged_in" not in st.session_state:
+            st.session_state.is_admin_logged_in = False
+
+        # Show the sidebar switch
+        switch = self.home_view.show_dashboard_switch()
+
+        if switch == "User Dashboard":
+            self.user_controller.show_dashboard()
+
+        elif switch == "Admin Dashboard":
+            # Check if the admin is already logged in
+            if st.session_state.is_admin_logged_in:
+                self.admin_controller.welcome_back()
+                self.admin_controller.show_dashboard()
+            else:
+                password = self.home_view.prompt_admin_password()
+
+                if self._hash_password(password) == self.__ADMIN_PASSWORD_HASH:
+                    st.session_state.is_admin_logged_in = True
+                    self.admin_controller.show_dashboard()
+                else:
+                    time.sleep(5)
+                    self.user_controller.show_dashboard()

streamlit_src/controllers/user_controller.py

@@ -0,0 +1,26 @@
+from models.air_quality_model import AirQualityModel
+from views.user_view import UserView
+
+class UserController:
+    def __init__(self):
+        self.model = AirQualityModel()
+        self.view = UserView()
+
+    def show_dashboard(self):
+        
+        # Get today's data and predictions
+        today_data = self.model.get_today_data()
+        next_three_days = self.model.next_three_day_predictions()
+
+        # WHO Guidelines
+        who_guidelines = {
+            "Pollutant": ["NO2 (µg/m³)", "O3 (µg/m³)"],
+            "WHO Guideline": [self.model.WHO_NO2_LEVEL, self.model.WHO_O3_LEVEL]
+        }
+
+        # Display current data and predictions
+        self.view.show_current_data(today_data, who_guidelines)
+        self.view.display_predictions(next_three_days)
+
+        # Compare to WHO guidelines
+        self.view.compare_to_who(today_data, self.model.WHO_NO2_LEVEL, self.model.WHO_O3_LEVEL)

streamlit_src/model_utils.py

@@ -1,36 +0,0 @@
-import os
-import sys
-import pandas as pd
-import joblib
-
-project_root = os.path.dirname(__file__)
-parent_dir = os.path.dirname(project_root)
-sys.path.append(parent_dir)
-from air_quality_forecast.prediction import PredictorModels
-
-def predictions_xgb_from_datapath(data_path):
-    updated_data_path = os.path.join(parent_dir, data_path) #"data", "inference", "x_test.csv"
-    data = pd.read_csv(updated_data_path)
-    return predictions_xgb_from_data(data)
-    
-def load_normalizer():
-    saved_models_path = os.path.join(parent_dir, "saved_models")
-    normalizer = joblib.load(os.path.join(saved_models_path, "normalizer.joblib"))
-    return normalizer
-
-def predictions_xgb_from_data(data, normalized=False):
-    
-    if data.columns[0] == "date":
-        data.set_index("date", inplace=True)
-        
-    
-    if not normalized:    normalizer = load_normalizer()
-        data = normalizer.transform(data)
-    
-    predictor = Predic
-        els()
-    return predictor.xgb_predictions(data)
-
-
-if __name__ == "__main__":
-    print(predictions_xgb_from_datapath("data/inference/x_test.csv"))

streamlit_src/models/air_quality_model.py

@@ -0,0 +1,25 @@
+import pandas as pd
+import numpy as np
+
+class AirQualityModel():
+    def __init__(self) -> None:
+        
+        self.WHO_NO2_LEVEL = 25
+        self.WHO_O3_LEVEL = 100
+        pass
+    
+    def get_today_data(self):
+        today_data = {
+            "NO2 (µg/m³)": np.random.uniform(20, 60),  # Simulated data
+            "O3 (µg/m³)": np.random.uniform(50, 120)   # Simulated data
+        }
+        return today_data
+
+        # Function to retrieve predictions for the next three days (replace with actual model predictions)
+    def next_three_day_predictions(self):
+        next_three_days = pd.DataFrame({
+            "Day": ["Day 1", "Day 2", "Day 3"],
+            "NO2 (µg/m³)": np.random.uniform(20, 60, 3),
+            "O3 (µg/m³)": np.random.uniform(50, 120, 3)
+        })
+        return next_three_days

streamlit_src/user_dashboard.py

@@ -1,110 +0,0 @@
-import streamlit as st
-import pandas as pd
-from datetime import date
-import numpy as np
-
-# Constants for WHO guideline levels
-WHO_NO2_LEVEL = 25
-WHO_O3_LEVEL = 100
-
-# Main function to display user dashboard
-def show_user_dashboard():
-    # Set layout and sidebar
-    set_layout()
-    set_sidebar()
-    
-    # Main content layout
-    set_columns()
-
-# Function to set the layout of the app
-def set_layout():
-    st.set_page_config(page_title="Air Quality Monitoring Dashboard", layout="wide")
-    st.sidebar.title("Air Quality Monitoring")
-
-# Function to retrieve current air quality data (replace with actual predictions)
-def get_today_data():
-    today_data = {
-        "NO2 (µg/m³)": np.random.uniform(20, 60),  # Simulated data
-        "O3 (µg/m³)": np.random.uniform(50, 120)   # Simulated data
-    }
-    return today_data
-
-# Function to retrieve predictions for the next three days (replace with actual model predictions)
-def next_three_day_predictions():
-    next_three_days = pd.DataFrame({
-        "Day": ["Day 1", "Day 2", "Day 3"],
-        "NO2 (µg/m³)": np.random.uniform(20, 60, 3),
-        "O3 (µg/m³)": np.random.uniform(50, 120, 3)
-    })
-    return next_three_days
-
-# Function to compare current data to WHO guidelines
-def compare_to_who(today_data):
-    if today_data["NO2 (µg/m³)"] > WHO_NO2_LEVEL:
-        st.sidebar.error("⚠️ NO2 levels are above WHO guidelines!")
-    else:
-        st.sidebar.success("✅ NO2 levels are within safe limits.")
-
-    if today_data["O3 (µg/m³)"] > WHO_O3_LEVEL:
-        st.sidebar.error("⚠️ O3 levels are above WHO guidelines!")
-    else:
-        st.sidebar.success("✅ O3 levels are within safe limits.")
-
-# Sidebar setup to display today's data and WHO guidelines
-def set_sidebar():
-    today_data = get_today_data()
-    st.sidebar.markdown(f"Today's Date: **{date.today().strftime('%B %d, %Y')}**")
-    
-    # Current Pollutant Concentrations
-    st.sidebar.markdown("### Current Pollutant Concentrations")
-    today_data_df = pd.DataFrame([today_data])  # Create a DataFrame without index
-    st.sidebar.dataframe(today_data_df.style.hide(axis="index"))  # Hide the index
-    
-    # WHO guideline values
-    who_guidelines = {
-        "Pollutant": ["NO2 (µg/m³)", "O3 (µg/m³)"],
-        "WHO Guideline": [WHO_NO2_LEVEL, WHO_O3_LEVEL]
-    }
-    st.sidebar.markdown("### WHO Guidelines")
-    who_guidelines_df = pd.DataFrame(who_guidelines)
-    st.sidebar.dataframe(who_guidelines_df.style.hide(axis="index"))  # Hide the index
-    
-    # WHO guideline alerts
-    compare_to_who(today_data)
-
-# Function to handle the admin dashboard button (placeholder for now)
-def start_admin_dashboard():
-    st.info("Navigating to admin dashboard...")
-
-# Set right column to include navigation to admin dashboard
-def set_right_column(column):
-    with column:
-        st.button(label="Admin Dashboard",
-                  help="Navigate to the admin dashboard.",
-                  on_click=start_admin_dashboard,
-                  icon="🔧")
-
-# Middle column for future use (e.g., prediction graphs)
-def set_middle_column(column):
-    with column:
-        next_three_days = next_three_day_predictions()
-        st.markdown("### Predictions for the Next 3 Days")
-        st.line_chart(next_three_days.set_index("Day"))
-
-        st.markdown("### About Air Pollution")
-        st.write("""
-        **Air pollution** is harmful to human health and the environment. NO2 and O3 are key pollutants monitored
-        to assess air quality. The World Health Organization (WHO) provides guidelines on safe levels of these
-        pollutants to minimize health risks.
-        """)
-
-# Left column for any additional content (e.g., awareness information)
-def set_left_column(column):
-    pass
-
-# Function to set the column layout for the main dashboard
-def set_columns():
-    col = st.columns((1, 3, 1), gap="medium")
-    set_right_column(col[2])
-    set_middle_column(col[1])
-    set_left_column(col[0])

streamlit_src/views/admin_view.py

@@ -0,0 +1,41 @@
+# views/user_view.py
+import streamlit as st
+import pandas as pd
+from datetime import date
+import time
+
+class AdminView:
+    def show_current_data(self, today_data, who_guidelines):
+        return 
+        st.sidebar.markdown(f"ADMIN Today's Date: **{date.today().strftime('%B %d, %Y')}**")
+        st.sidebar.markdown("### Current Pollutant Concentrations")
+        today_data_df = pd.DataFrame([today_data])
+        st.sidebar.dataframe(today_data_df.style.hide(axis="index"))
+        
+        st.sidebar.markdown("### WHO Guidelines")
+        who_guidelines_df = pd.DataFrame(who_guidelines)
+        st.sidebar.dataframe(who_guidelines_df.style.hide(axis="index"))
+
+    def display_predictions(self, next_three_days):
+        return  
+        st.markdown("### Predictions for the Next 3 Days")
+        st.line_chart(next_three_days.set_index("Day"))
+
+    def compare_to_who(self, today_data, no2_level, o3_level):
+        return  
+        if today_data["NO2 (µg/m³)"] > no2_level:
+            st.sidebar.error("⚠️ NO2 levels are above WHO guidelines!")
+        else:
+            st.sidebar.success("✅ NO2 levels are within safe limits.")
+
+        if today_data["O3 (µg/m³)"] > o3_level:
+            st.sidebar.error("⚠️ O3 levels are above WHO guidelines!")
+        else:
+            st.sidebar.success("✅ O3 levels are within safe limits.")
+            
+    def welcome_back(self):
+        st.empty()
+        placeholder = st.empty()
+        placeholder.success("Welcome back!")
+        time.sleep(2)
+        placeholder.empty()

streamlit_src/views/home_view.py

@@ -0,0 +1,18 @@
+import streamlit as st
+
+class HomeView:
+    
+    def __init__(self):
+        self.set_layout()
+    
+    def set_layout(self):
+        st.set_page_config(page_title="Air Quality Monitoring Dashboard", layout="wide")
+        st.sidebar.title("Air Quality Monitoring")
+        
+    def show_dashboard_switch(self):
+        page = st.sidebar.radio("Go to", ["User Dashboard", "Admin Dashboard"])
+        return page
+    
+    def prompt_admin_password(self):
+        st.sidebar.markdown("### Admin Access Required")
+        return st.sidebar.text_input("Enter Admin Password:", type="password")

streamlit_src/views/user_view.py

@@ -0,0 +1,30 @@
+# views/user_view.py
+import streamlit as st
+import pandas as pd
+from datetime import date
+
+class UserView:
+    def show_current_data(self, today_data, who_guidelines):
+        st.sidebar.markdown(f"Today's Date: **{date.today().strftime('%B %d, %Y')}**")
+        st.sidebar.markdown("### Current Pollutant Concentrations")
+        today_data_df = pd.DataFrame([today_data])
+        st.sidebar.dataframe(today_data_df.style.hide(axis="index"))
+        
+        st.sidebar.markdown("### WHO Guidelines")
+        who_guidelines_df = pd.DataFrame(who_guidelines)
+        st.sidebar.dataframe(who_guidelines_df.style.hide(axis="index"))
+
+    def display_predictions(self, next_three_days):
+        st.markdown("### Predictions for the Next 3 Days")
+        st.line_chart(next_three_days.set_index("Day"))
+
+    def compare_to_who(self, today_data, no2_level, o3_level):
+        if today_data["NO2 (µg/m³)"] > no2_level:
+            st.sidebar.error("⚠️ NO2 levels are above WHO guidelines!")
+        else:
+            st.sidebar.success("✅ NO2 levels are within safe limits.")
+
+        if today_data["O3 (µg/m³)"] > o3_level:
+            st.sidebar.error("⚠️ O3 levels are above WHO guidelines!")
+        else:
+            st.sidebar.success("✅ O3 levels are within safe limits.")