Merge pull request #2 from khurshiduktamov/arima-model-update

App interface and CI/CD pipeline update

.github/workflows/daily_fetch.yaml

@@ -23,7 +23,7 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install pandas statsmodels joblib requests
+        pip install pandas statsmodels joblib requests plotly
 
     - name: Fetch weather data
       run: |

app/main.py

@@ -1,15 +1,124 @@
 import streamlit as st
 import pandas as pd
+import json
+import plotly.express as px
+
+# Set the page title
+st.set_page_config(page_title="AQI Predictions")
 
 # Load predictions
 predictions_path = 'data/processed/predictions.csv'
 predictions = pd.read_csv(predictions_path)
 
+# Load weather data
+weather_data_path = 'data/raw/weather_data.json'
+with open(weather_data_path) as f:
+    weather_data = json.load(f)
+
+# Extract the most recent weather details
+latest_weather_entry = weather_data['data'][-1]['data']
+aqi = latest_weather_entry['aqi']
+temp = latest_weather_entry['iaqi']['t']['v']
+humidity = latest_weather_entry['iaqi']['h']['v']
+pressure = latest_weather_entry['iaqi']['p']['v']
+wind_speed = latest_weather_entry['iaqi']['w']['v']
+wind_gust = latest_weather_entry['iaqi']['wg']['v']
+dew_point = latest_weather_entry['iaqi']['dew']['v']
+city_name = latest_weather_entry['city']['name']
+update_time = latest_weather_entry['time']['iso']
+
 # Streamlit app
-st.title('Weather Prediction App')
+st.title('Air Quality Prediction App')
+
+# Display current weather
+st.header(f"Current Weather in {city_name}")
+st.write(f"Last updated: {update_time}")
 
-st.write('## PM2.5 Predictions for the Next 5 Days')
+# Display weather metrics in columns with icons
+col1, col2, col3 = st.columns(3)
+with col1:
+    st.metric("AQI", aqi, help="Air Quality Index")
+    st.image("https://img.icons8.com/color/48/000000/air-quality.png", width=30)
+with col2:
+    st.metric("Temperature (°C)", temp)
+    st.image("https://img.icons8.com/color/48/000000/temperature.png", width=30)
+with col3:
+    st.metric("Humidity (%)", humidity)
+    st.image("https://img.icons8.com/color/48/000000/humidity.png", width=30)
+
+col4, col5, col6 = st.columns(3)
+with col4:
+    st.metric("Pressure (hPa)", pressure)
+    st.image("https://img.icons8.com/color/48/000000/pressure.png", width=30)
+with col5:
+    st.metric("Wind Speed (m/s)", wind_speed)
+    st.image("https://img.icons8.com/color/48/000000/wind.png", width=30)
+with col6:
+    st.metric("Wind Gust (m/s)", wind_gust)
+    st.image("https://cdn-icons-png.flaticon.com/512/740/740832.png", width=30)
+
+col7, _ = st.columns(2)
+with col7:
+    st.metric("Dew Point (°C)", dew_point)
+    st.image("https://img.icons8.com/color/48/000000/dew-point.png", width=30)
+
+# Display PM2.5 predictions
+st.header('PM2.5 Predictions for the Next 5 Days')
 st.write(predictions)
 
-# Plot predictions
-st.line_chart(predictions.set_index('date')['predicted_pm25'])
+# Plot predictions with Plotly for better visuals
+fig = px.line(predictions, x='date', y='pm25', title='PM2.5 Predictions', labels={'date': 'Date', 'pm25': 'PM2.5 Level'})
+fig.update_layout(title_x=0.5, title_font=dict(size=24), xaxis_title_font=dict(size=18), yaxis_title_font=dict(size=18))
+st.plotly_chart(fig)
+
+# Add AQI information table
+st.header('Air Quality Index (AQI) Information')
+aqi_info = {
+    "AQI": ["0 - 50", "51 - 100", "101 - 150", "151 - 200", "201 - 300", "300+"],
+    "Air Pollution Level": ["Good", "Moderate", "Unhealthy for Sensitive Groups", "Unhealthy", "Very Unhealthy", "Hazardous"],
+    "Health Implications": [
+        "Air quality is considered satisfactory, and air pollution poses little or no risk.",
+        "Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution.",
+        "Members of sensitive groups may experience health effects. The general public is not likely to be affected.",
+        "Everyone may begin to experience health effects; members of sensitive groups may experience more serious health effects.",
+        "Health warnings of emergency conditions. The entire population is more likely to be affected.",
+        "Health alert: everyone may experience more serious health effects."
+    ],
+    "Cautionary Statement (for PM2.5)": [
+        "None",
+        "Active children and adults, and people with respiratory disease, such as asthma, should limit prolonged outdoor exertion.",
+        "Active children and adults, and people with respiratory disease, such as asthma, should limit prolonged outdoor exertion.",
+        "Active children and adults, and people with respiratory disease, such as asthma, should avoid prolonged outdoor exertion; everyone else, especially children, should limit prolonged outdoor exertion.",
+        "Active children and adults, and people with respiratory disease, such as asthma, should avoid all outdoor exertion; everyone else, especially children, should limit outdoor exertion.",
+        "Everyone should avoid all outdoor exertion."
+    ]
+}
+
+# Create a DataFrame for AQI information
+aqi_df = pd.DataFrame(aqi_info)
+
+# Define colors based on AQI levels
+colors = {
+    "Good": "#009966",
+    "Moderate": "#FFDE33",
+    "Unhealthy for Sensitive Groups": "#FF9933",
+    "Unhealthy": "#CC0033",
+    "Very Unhealthy": "#660099",
+    "Hazardous": "#7E0023"
+}
+
+# Apply colors to the AQI levels
+def color_row(row):
+    return [f'background-color: {colors[row["Air Pollution Level"]]}']*len(row)
+
+st.dataframe(aqi_df.style.apply(color_row, axis=1))
+
+
+# Show a map of the city location
+st.map(pd.DataFrame([[latest_weather_entry['city']['geo'][0], latest_weather_entry['city']['geo'][1]]], columns=['lat', 'lon']))
+
+
+# Adding footer for attributions
+st.write("### Data Attributions")
+for attribution in latest_weather_entry['attributions']:
+    st.markdown(f"[{attribution['name']}]({attribution['url']})")