adding actual data to all places in the st app

streamlit_src/controllers/user_controller.py

@@ -1,13 +1,20 @@
+import streamlit as st
+import numpy as np
 from models.air_quality_model import AirQualityModel
 from views.user_view import UserView
-import numpy as np
 
 
 class UserController:
     def __init__(self):
         self.model = AirQualityModel()
         self.view = UserView()
-        self.is_first_run = True
+
+        # Ensure session state for first run and quiz question
+        if "is_first_run" not in st.session_state:
+            st.session_state.is_first_run = True
+
+        if "question_choice" not in st.session_state:
+            st.session_state.question_choice = np.random.randint(0, 5)
 
     def show_dashboard(self):
         # Get today's data and predictions
@@ -22,11 +29,18 @@ class UserController:
 
         # Display current data and predictions
         self.view.show_current_data(today_data, who_guidelines)
-        if self.is_first_run:
-            question_choice = np.random.randint(0, 5)
+
+        # Use session state to avoid resetting the quiz question after each interaction
+        if st.session_state.is_first_run:
+            st.session_state.question_choice = np.random.randint(0, 5)
+            st.session_state.is_first_run = False
+
+        # Raise awareness and display the quiz with the saved question number
         self.view.raise_awareness_and_quiz(
-            today_data, who_guidelines, question_nr=question_choice
+            today_data, who_guidelines, question_nr=st.session_state.question_choice
         )
+
+        # Plot selection
         plot_type = self.view.view_option_selection()
         if plot_type == "Line Plot":
             self.view.display_predictions_lineplot(next_three_days, who_guidelines)
@@ -40,5 +54,3 @@ class UserController:
 
         # Print sources
         self.view.print_sources()
-
-        self.is_first_run = False

streamlit_src/models/air_quality_model.py

@@ -1,18 +1,17 @@
 import pandas as pd
-import numpy as np
 import os
 
 PREDICTION_PATH = os.path.join(
     os.path.dirname(os.path.dirname(os.path.dirname(__file__))),
     "data",
-    "inference",
-    "current_prediction_data.csv",
+    "model_predictions",
+    "prediction_data.csv",
 )
 
 PAST_DATA_PATH = os.path.join(
     os.path.dirname(os.path.dirname(os.path.dirname(__file__))),
     "data",
-    "inference",
+    "model_predictions",
     "last_three_days.csv",
 )
 
@@ -22,13 +21,6 @@ class AirQualityModel:
         self.WHO_NO2_LEVEL = 25
         self.WHO_O3_LEVEL = 100
 
-    def get_today_data_fake(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
-
     def get_today_data(self):
         """
         Returns the air quality data for today from the past 3 days data.
@@ -41,26 +33,29 @@ class AirQualityModel:
             A pandas Series with the air quality data for today.
         """
         last_three_days = self.get_last_three_days()
-        today = last_three_days.iloc[1]
+        today = last_three_days.iloc[0]
         return today
 
     def next_three_day_predictions(self):
         # Load data from the specified path (assuming it's a CSV file)
         data = pd.read_csv(PREDICTION_PATH)
 
+        # correct data is todays data row
+        data = data[data["date"] == pd.Timestamp.now().strftime("%Y-%m-%d")]
+
         # Create a DataFrame with the relevant information for the next three days
         next_three_days = pd.DataFrame(
             {
                 "Day": ["Day 1", "Day 2", "Day 3"],
                 "NO2 (µg/m³)": [
-                    data["NO2"].values[0],
                     data["NO2 + day 1"].values[0],
                     data["NO2 + day 2"].values[0],
+                    data["NO2 + day 3"].values[0],
                 ],
                 "O3 (µg/m³)": [
-                    data["O3"].values[0],
                     data["O3 + day 1"].values[0],
                     data["O3 + day 2"].values[0],
+                    data["O3 + day 3"].values[0],
                 ],
             }
         )