updating user dashboard with major quality of life changes

streamlit_src/controllers/admin_controller.py

@@ -1,5 +1,6 @@
 from models.air_quality_model import AirQualityModel
 from views.admin_view import AdminView
+import streamlit as st
 
 
 class AdminController:
@@ -8,25 +9,33 @@ class AdminController:
         self.view = AdminView()
 
     def show_dashboard(self):
-        return
-        # Get today's data and predictions
+        # Fetch today's data and the next three days' predictions from the model
         today_data = self.model.get_today_data()
         next_three_days = self.model.next_three_day_predictions()
+        #model_metrics = self.model.get_model_metrics()
 
-        # WHO Guidelines
+        # Define WHO guidelines for pollutant levels
         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)
+        # Define layout: main content on the left and additional information on the right
+        col_main, col_right = st.columns([0.7, 0.3])
 
-        # Compare to WHO guidelines
-        self.view.compare_to_who(
-            today_data, self.model.WHO_NO2_LEVEL, self.model.WHO_O3_LEVEL
-        )
+        # Main content in the left column
+        with col_main:
+            # Display current data and future predictions
+            self.view.show_current_data(today_data, who_guidelines)
+
+            # Display predictions line plot
+
+            self.view.display_predictions_lineplot(next_three_days, who_guidelines)
+
+        # Right column content for additional details or actions
+        with col_right:
+            st.markdown("### Model Performance")
+            #self.view.show_model_performance(model_metrics)
 
     def welcome_back(self):
         self.view.welcome_back()

streamlit_src/controllers/user_controller.py

@@ -20,6 +20,7 @@ class UserController:
 
         # Display current data and predictions
         self.view.show_current_data(today_data, who_guidelines)
+        self.view.raise_awareness_and_quiz(today_data, who_guidelines)
         plot_type = self.view.view_option_selection()
         if plot_type == "Line Plot":
             self.view.display_predictions_lineplot(next_three_days, who_guidelines)
@@ -30,3 +31,6 @@ class UserController:
         self.view.compare_to_who(
             today_data, self.model.WHO_NO2_LEVEL, self.model.WHO_O3_LEVEL
         )
+
+        # Print sources
+        self.view.print_sources()

streamlit_src/facts.json

@@ -0,0 +1,12 @@
+{
+    "facts": [
+      "Air pollution is now recognized as the biggest environmental threat to human health, responsible for millions of deaths and lost years of healthy life each year.",
+      "Air pollution significantly contributes to noncommunicable diseases (NCDs), such as heart disease, lung cancer, and respiratory diseases.",
+      "Despite improvements in air quality in high-income countries, air pollution has worsened in many low- and middle-income countries due to rapid urbanization and economic development.",
+      "The health burden from air pollution also brings a significant economic toll due to increased healthcare costs and lost productivity.",
+      "Since 1987, the WHO has been providing health-based air quality guidelines, influencing pollution control policies worldwide.",
+      "In 2015, the World Health Assembly called for stronger global efforts to protect people from air pollution.",
+      "Over the past 15 years, there has been a marked increase in scientific evidence showing the harmful effects of air pollution."
+    ]
+  }
+  

streamlit_src/question.json

@@ -0,0 +1,55 @@
+{
+    "quiz": [
+      {
+        "question": "What is the biggest environmental threat to human health?",
+        "options": [
+          "Climate change",
+          "Deforestation",
+          "Air pollution",
+          "Water pollution"
+        ],
+        "answer": "Air pollution"
+      },
+      {
+        "question": "Which disease is NOT significantly contributed to by air pollution?",
+        "options": [
+          "Lung cancer",
+          "Heart disease",
+          "Diabetes",
+          "Respiratory diseases"
+        ],
+        "answer": "Diabetes"
+      },
+      {
+        "question": "Since what year has the WHO been providing health-based air quality guidelines?",
+        "options": [
+          "1980",
+          "1987",
+          "1995",
+          "2000"
+        ],
+        "answer": "1987"
+      },
+      {
+        "question": "What global health event in 2015 called for stronger efforts against air pollution?",
+        "options": [
+          "The Paris Agreement",
+          "The World Health Assembly",
+          "The United Nations Climate Summit",
+          "The Earth Summit"
+        ],
+        "answer": "The World Health Assembly"
+      },
+      {
+        "question": "What is a major economic impact of air pollution?",
+        "options": [
+          "Increased healthcare costs",
+          "Increased tourism",
+          "Higher employment rates",
+          "Lower energy costs"
+        ],
+        "answer": "Increased healthcare costs"
+      }
+    ]
+  }
+  

streamlit_src/views/admin_view.py

@@ -1,40 +1,75 @@
 # views/user_view.py
 import streamlit as st
 import pandas as pd
-from datetime import date
+from datetime import date, datetime, timedelta
 import time
+import plotly.graph_objects as go
 
 
 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(f"Today's Date: **{datetime.now().strftime('%B %d, %Y')}**")
+
+        # Display today's pollutant data along with WHO guidelines
+        merged_data = {
+            "Pollutant": ["NO2 (µg/m³)", "O3 (µg/m³)"],
+            "Current Concentration": [
+                today_data["NO2 (µg/m³)"],
+                today_data["O3 (µg/m³)"],
+            ],
+            "WHO Guideline": [
+                who_guidelines["WHO Guideline"][0],
+                who_guidelines["WHO Guideline"][1],
+            ],
+        }
+        merged_data_df = pd.DataFrame(merged_data)
+        st.sidebar.markdown("### Current Pollutant Concentrations and WHO Guidelines")
+        st.sidebar.dataframe(merged_data_df, hide_index=True)
+
+    def show_model_performance(self, model_metrics):
+        st.markdown("### Model Performance Metrics")
+        for model, metrics in model_metrics.items():
+            st.markdown(f"**{model}**")
+            st.write(f"RMSE: {metrics['RMSE']}, MAE: {metrics['MAE']}")
+
+    def display_predictions_lineplot(self, next_three_days, who_guidelines):
+        tomorrow, day_after_tomorrow, two_days_after_tomorrow = self.get_next_three_days_dates()
+
+        # Update the dataframe with actual dates
+        next_three_days["Date"] = [tomorrow, day_after_tomorrow, two_days_after_tomorrow]
+
+        # Create line plot for the predictions
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(x=next_three_days["Date"], y=next_three_days["NO2 (µg/m³)"], mode="lines+markers", name="NO2"))
+        fig.add_trace(go.Scatter(x=next_three_days["Date"], y=next_three_days["O3 (µg/m³)"], mode="lines+markers", name="O3"))
+
+        # Add WHO guidelines as reference lines
+        fig.add_hline(y=who_guidelines["WHO Guideline"][0], line_dash="dot", annotation_text="WHO NO2 Guideline")
+        fig.add_hline(y=who_guidelines["WHO Guideline"][1], line_dash="dot", annotation_text="WHO O3 Guideline")
+
+        # Update layout
+        fig.update_layout(
+            title="Pollutant Predictions for Next 3 Days",
+            xaxis_title="Date",
+            yaxis_title="Concentration (µg/m³)",
+            hovermode="x unified"
         )
-        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.")
+
+        st.plotly_chart(fig)
+
+    def display_model_comparisons(self, actual, predicted):
+        st.markdown("### Model Prediction Comparisons")
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(x=actual, y=predicted, mode="markers", name="Predictions vs Actual"))
+        fig.update_layout(title="Actual vs Predicted Pollutant Levels", xaxis_title="Actual", yaxis_title="Predicted")
+        st.plotly_chart(fig)
+
+    def get_next_three_days_dates(self):
+        today = datetime.now()
+        tomorrow = today + timedelta(days=1)
+        day_after_tomorrow = today + timedelta(days=2)
+        two_days_after_tomorrow = today + timedelta(days=3)
+        return tomorrow, day_after_tomorrow, two_days_after_tomorrow
 
     def welcome_back(self):
         st.empty()

streamlit_src/views/user_view.py

@@ -3,6 +3,13 @@ import streamlit as st
 import pandas as pd
 from datetime import date, datetime, timedelta
 import plotly.graph_objects as go
+import json
+import random
+import sys
+import os
+
+FACTS_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "facts.json")
+QUESTIONS_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "question.json")
 
 
 class UserView:
@@ -29,6 +36,11 @@ class UserView:
         st.sidebar.dataframe(merged_data_df, hide_index=True)
 
     def get_next_three_days_dates(self):
+        """
+        Returns the next three days' dates in datetime format as a tuple.
+        
+        :return: tuple of three datetime objects, representing tomorrow, day after tomorrow, and two days after tomorrow.
+        """
         today = datetime.now()  # Get the current date and time
         tomorrow = today + timedelta(days=1)
         day_after_tomorrow = today + timedelta(days=2)
@@ -36,8 +48,6 @@ class UserView:
         return tomorrow, day_after_tomorrow, two_days_after_tomorrow
 
     def display_predictions_lineplot(self, next_three_days, who_guidelines):
-        st.markdown("### Predictions for the Next 3 Days")
-
         tomorrow, day_after_tomorrow, two_days_after_tomorrow = (
             self.get_next_three_days_dates()
         )
@@ -105,15 +115,19 @@ class UserView:
         # Display the plot in Streamlit
         st.plotly_chart(fig)
 
-    def get_no2_color(self, value, who_limit):
-        # Define the gradient points
-        if value <= who_limit:
-            # Blue -> Purple gradient
-            return f"rgba({int(0 + (255 * value / who_limit))}, 0, 255, 1)"  # Gradient from blue to purple
+    def get_color(self, value, who_limit):
+        half_who_limit = who_limit / 2
+
+        if value <= half_who_limit:
+            # Green -> Yellow gradient
+            return f"rgba({int(255 * value / half_who_limit)}, 255, 0, 1)"  # Gradient from green to yellow
+        elif value <= who_limit:
+            # Yellow -> Red gradient
+            excess_value = value - half_who_limit
+            return f"rgba(255, {int(255 - (255 * excess_value / half_who_limit))}, 0, 1)"  # Gradient from yellow to red
         else:
-            # Purple -> Red gradient
-            excess_value = min(who_limit, value - who_limit)
-            return f"rgba(255, 0, {int(255 - (255 * excess_value / who_limit))}, 1)"  # Gradient from purple to red
+            # Beyond the WHO limit, fully red
+            return "rgba(255, 0, 0, 1)"  # Fully red
 
     def display_predictions_gaugeplot(self, next_three_days, who_guidelines):
         st.markdown("### Predictions for the Next 3 Days")
@@ -140,7 +154,7 @@ class UserView:
             with col1:
                 # Get color based on NO2 value
                 no2_value = next_three_days["NO2 (µg/m³)"][i]
-                no2_color = self.get_no2_color(
+                no2_color = self.get_color(
                     no2_value, who_guidelines["WHO Guideline"][0]
                 )
                 fig_no2 = go.Figure(
@@ -152,7 +166,7 @@ class UserView:
                             "axis": {
                                 "range": [
                                     0,
-                                    max(who_guidelines["WHO Guideline"][0], 100),
+                                    2*who_guidelines["WHO Guideline"][0],
                                 ]
                             },
                             "bar": {"color": no2_color},
@@ -167,6 +181,10 @@ class UserView:
 
             # O3 Gauge
             with col2:
+                o3_value = next_three_days["O3 (µg/m³)"][i]
+                o3_color = self.get_color(
+                    o3_value, who_guidelines["WHO Guideline"][1]
+                    )
                 fig_o3 = go.Figure(
                     go.Indicator(
                         mode="gauge+number",
@@ -176,10 +194,10 @@ class UserView:
                             "axis": {
                                 "range": [
                                     0,
-                                    max(who_guidelines["WHO Guideline"][1], 150),
+                                    1.5*who_guidelines["WHO Guideline"][1],
                                 ]
                             },
-                            "bar": {"color": "lightblue"},
+                            "bar": {"color": o3_color},
                         },
                         domain={"x": [0, 1], "y": [0, 1]},  # Controls size
                     )
@@ -190,8 +208,9 @@ class UserView:
                 st.plotly_chart(fig_o3)
 
     def view_option_selection(self) -> str:
+        st.markdown("### Visualizing Air Quality Predictions")
         plot_type = st.selectbox(
-            "Choose Visualization Type", ("Line Plot", "Gauge Plot")
+            "", ("Line Plot", "Gauge Plot")
         )
         return plot_type
 
@@ -206,18 +225,96 @@ class UserView:
         else:
             st.sidebar.success("✅ O3 levels are within safe limits.")
 
-    def raise_awareness(self):
-        st.sidebar.markdown("""
-        **Air pollution** is a serious concern that affects the environment and public health.
-        High levels of pollutants, such as ozone (O₃) and nitrogen dioxide (NO₂), can lead to
-        respiratory problems, aggravate pre-existing conditions like asthma, and contribute to
-        cardiovascular diseases. On particularly bad days, vulnerable groups such as children,
-        the elderly, and those with respiratory issues are at even greater risk.
-        """)
-
-        st.sidebar.markdown("""
-        **Key Pollutants:**
-        - **Ozone (O₃):** Formed by chemical reactions in the atmosphere, particularly on sunny days.
-        - **Nitrogen Dioxide (NO₂):** Mostly emitted from vehicles and industrial activities, this can cause
-        irritation of the respiratory system.
-        """)
+    def raise_awareness(self, today_data, who_guidelines):
+        st.markdown("### Air Quality Awareness")
+
+        # Load facts from the JSON file
+        with open(FACTS_PATH, 'r') as f:
+            facts = json.load(f)["facts"]
+
+        # Randomly select a fact from the list
+        random_fact = random.choice(facts)
+
+        # Create expandable sections for key pollutant information
+        with st.expander("🌍 What is Air Pollution?"):
+            st.write("""
+            **Air pollution** is a serious concern that affects the environment and public health.
+            High levels of pollutants, such as ozone (O₃) and nitrogen dioxide (NO₂), can lead to
+            respiratory problems, aggravate pre-existing conditions like asthma, and contribute to
+            cardiovascular diseases.
+            """)
+        
+        with st.expander("⚠️ Why O₃ and NO₂ Matter"):
+            st.write("""
+            **Ozone (O₃):** Formed by chemical reactions in the atmosphere, particularly on sunny days.
+            High levels can cause chest pain, coughing, throat irritation, and airway inflammation.
+            
+            **Nitrogen Dioxide (NO₂):** Mostly emitted from vehicles and industrial activities, this can cause
+            irritation of the respiratory system and decrease lung function, especially during long-term exposure.
+            """)
+
+        self.add_spaces(num_lines=3)
+
+        # Display the random fact for user awareness
+        st.markdown("### Did You Know?")
+        st.info(random_fact)  # Display the random fact in an info box
+
+        self.add_spaces(num_lines=3)
+
+        # Show real-time suggestions for high pollution days
+        st.markdown("### Health Recommendations Based on Current Levels")
+        if today_data["NO2 (µg/m³)"] > who_guidelines["WHO Guideline"][0] or today_data["O3 (µg/m³)"] > who_guidelines["WHO Guideline"][1]:
+            st.error("🚨 High pollution levels today. Avoid outdoor activities if possible, especially for vulnerable groups.")
+        else:
+            st.success("✅ Air quality is within safe limits today. Enjoy your outdoor activities!")
+        
+        self.add_spaces(num_lines=3)
+
+    def print_sources(self):
+        # Provide user links to external resources or reports
+        st.markdown("### Learn More")
+        st.markdown("[WHO Air Quality Guidelines](https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health)")
+        st.markdown("[Air Pollution Facts](https://www.un.org/sustainabledevelopment/air-pollution/)")
+
+    def quiz(self):
+        with open(QUESTIONS_PATH, 'r') as f:
+            quiz_data = json.load(f)
+        # Access the quiz questions
+        questions = quiz_data['quiz']
+        random_question = random.choice(questions)
+
+        # Add a simple quiz to engage the user
+        st.markdown("### Quick Quiz: How Much Do You Know About Air Pollution?")
+        with st.form(key="quiz_form"):
+            # Display the first question and options
+            st.write(random_question['question'])
+            options = random_question['options']
+            answer = st.radio("Choose an option:", options)
+            submitted = st.form_submit_button("Submit Answer")
+
+            if submitted:
+                if answer == random_question['answer']:
+                    st.success("Correct!")
+                else:
+                    st.error("Incorrect. The correct answer is: " + random_question['answer'])
+
+    def raise_awareness_and_quiz(self, today_data, who_guidelines):
+        # Create two columns: main column for awareness and right column for the quiz
+        col_main, col_right = st.columns([0.7, 0.3], gap="large")  # 70% for awareness, 30% for quiz
+
+        # Left column: Raise awareness
+        with col_main:
+            self.raise_awareness(today_data, who_guidelines)
+
+        # Right column: Quiz
+        with col_right:
+            self.quiz()
+    
+    def add_spaces(self, num_lines=1):
+        """Add vertical space between sections by adding empty lines.
+
+        Args:
+            num_lines (int): Number of blank lines to add. Default is 1.
+        """
+        for _ in range(num_lines):
+            st.write("")  # This adds a blank line to create space