Improve Webapp with multi page

.github/workflows/deploy.yml

@@ -0,0 +1,40 @@
+
+name : Deploy Panel App
+
+concurrency: production
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+
+jobs:
+  deploy:
+    name: Deploy Panel App
+    runs-on: ubuntu-latest
+    environment: production
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v2
+
+      - name: Set up SSH
+        run: |
+          mkdir -p ~/.ssh/
+          echo "${{secrets.SSH_PRIVATE_KEY}}" > ~/.ssh/id_ed25519
+          chmod 600 ~/.ssh/id_ed25519
+          ssh-keyscan -t ed25519 -H ${{secrets.PROD_IP}} >> ~/.ssh/known_hosts
+
+      - name: Deploy to Server
+        run: |
+          ssh ubuntu@${{secrets.PROD_IP}} << 'EOF'
+          # Arrêter le serveur Panel
+          pkill -f 'streamlit run'
+          cd ${{vars.PROD_PATH}}
+          git pull
+          git lfs pull
+          nohup hatch run serve:prod </dev/null  >./log-prod.log 2>&1 &
+          EOF

pyproject.toml

@@ -28,6 +28,8 @@ dependencies = [
   "numpy>=1.25.0,<2.0.0",  # numpy 2.0.0 is released but not yet supported by all dependencies
   "pandas",
   "xarray",
+  "netCDF4",
+  "watchfiles",
   "scikit-learn",
   "matplotlib",
   "eccodes",
@@ -61,17 +63,18 @@ extra-dependencies = [
   "sphinx_design",
   "nbsphinx",
   "taipy",
+  "streamlit",
 ]
 [tool.hatch.envs.docs.scripts]
 build = "sphinx-build -b html doc/ doc/_build"
 
 [tool.hatch.envs.serve]
 extra-dependencies = [
-  "taipy",
+  "streamlit",
 ]
 
 [tool.hatch.envs.serve.scripts]
-prod = "python src/energy_forecast/dashboard/dashboard.py"
+prod = "streamlit run src/energy_forecast/dashboard/dashboard_streamlit.py"
 
 [tool.coverage.run]
 source_pkgs = ["energy_forecast", "tests"]

src/energy_forecast/__init__.py

@@ -1 +1,3 @@
+from pathlib import Path
+ROOT_DIR = Path(__file__).parent.parent.parent
 version = "0.1.0"

src/energy_forecast/dashboard/dashboard_streamlit.py

@@ -0,0 +1,19 @@
+import streamlit as st
+
+st.set_page_config(
+    page_title="ENR Forecast",
+    page_icon="🌞",
+)
+
+st.markdown("""# Welcome to Energy Forecast! 🌞
+This App is a demo of a simple energy forecast dashboard.
+
+It is divided into three sections:
+
+- <a href="weather" target = "_self">Weather Forecast</a>
+- <a href="power_generation" target = "_self">Energy Forecast</a>
+- <a href="consumption_prediction" target = "_self">Consumption Forecast</a>
+- <a href="prediction_tempo" target = "_self">Prediction Tempo</a>
+""", unsafe_allow_html=True)
+
+st.sidebar.success("Select a demo above.")

src/energy_forecast/dashboard/pages/1_weather.py

@@ -0,0 +1,43 @@
+from energy_forecast.meteo import ArpegeSimpleAPI
+from energy_forecast.energy import ECO2MixDownloader
+from energy_forecast.consumption_forecast import PredictionForecastAPI
+import pandas as pd
+import streamlit as st
+import altair as alt
+
+def compute_data_sun()->pd.Series:
+    sun_data = ArpegeSimpleAPI().region_sun()
+    data = sun_data.sum(axis=1).to_frame()
+    data.reset_index(inplace=True)
+    data.columns = ["time", "sun_flux"]
+    # data.name = "sun_flux"
+    return data
+
+def compute_data_wind()->pd.Series:
+    wind_data = ArpegeSimpleAPI().region_wind()
+    data = wind_data.sum(axis=1).to_frame()
+    data.reset_index(inplace=True)
+    data.columns = ["time", "wind_speed"]
+    return data
+
+st.title("Prévision meteo")
+
+wind_data = compute_data_wind()
+
+wind_barplot = alt.Chart(wind_data).mark_bar().encode(
+    y="wind_speed",
+    x="time"
+
+)
+st.altair_chart(wind_barplot,
+                use_container_width=True
+             )
+
+sun_data = compute_data_sun()
+sun_barplot = alt.Chart(sun_data).mark_bar().encode(
+    y="sun_flux",
+    x="time"
+)
+st.altair_chart(sun_barplot,
+                use_container_width=True
+             )

src/energy_forecast/dashboard/pages/2_power_generation.py

@@ -0,0 +1,91 @@
+from energy_forecast.energy import ECO2MixDownloader
+from energy_forecast.meteo import ArpegeSimpleAPI
+from energy_forecast.consumption_forecast import PredictionForecastAPI
+import pandas as pd
+import streamlit as st
+import altair as alt
+
+
+def compute_data_pv_power()->pd.Series:
+    sun_data = ArpegeSimpleAPI().region_sun()
+    sun_data.columns = [
+        c.replace(" ", "_").replace("'", "_").replace("-", "_").lower()
+        for c in sun_data.columns
+    ]
+    model_params = pd.read_csv("notebooks/datascience/sun_model_2_params.csv")
+    model_params.columns = ["region", "coef"]
+    model_params_ser = model_params.set_index("region").iloc[:, 0]
+    production = sun_data * model_params_ser
+    pv_power = production.sum(axis=1).to_frame() / 24
+    pv_power.reset_index(inplace=True)
+    pv_power.columns = ["time", "pv_power"]
+    return pv_power
+
+def compute_data_eolien()->pd.Series:
+    wind_data = ArpegeSimpleAPI().region_wind()
+    wind_data.columns = [
+        c.replace(" ", "_").replace("'", "_").replace("-", "_").lower()
+        for c in wind_data.columns
+    ]
+    model_params = pd.read_csv("notebooks/datascience/wind_model_2_params.csv")
+    model_params.columns = ["region", "coef"]
+    model_params_ser = model_params.set_index("region").iloc[:, 0]
+    production = wind_data * model_params_ser
+    eolien_power = production.sum(axis=1).to_frame() / 24
+    eolien_power.reset_index(inplace=True)
+    eolien_power.columns = ["time", "eolien_power"]
+    return eolien_power
+
+
+pv_power = compute_data_pv_power()
+eolien_power = compute_data_eolien()
+print(pv_power)
+
+def compute_energy():
+    r = ECO2MixDownloader(year=2024)
+    r.download()
+    energy =  r.read_file()
+
+    energy = energy[["Eolien", "Solaire"]]
+    energy = energy["2024-06-17":]
+    energy.index = energy.index.tz_localize("Europe/Paris").tz_convert("UTC").tz_localize(None)
+    energy = pd.concat([
+        energy,
+        pv_power.set_index("time").rename(columns={"pv_power": "PV Prediction"}),
+        eolien_power.set_index("time").rename(columns={"eolien_power": "Eolien Prediction"})
+    ], axis=1).reset_index()
+
+    return energy
+
+energy = compute_energy()
+# keep only full hours
+energy = energy[energy["time"].dt.minute == 0]
+
+st.title("Prévision de production ENR")
+
+
+wind_history = alt.Chart(energy).mark_line().encode(
+    y="Eolien",
+    x="time"
+)
+wind_prediction = alt.Chart(energy).mark_line(color="red").encode(
+    y="Eolien Prediction",
+    x="time"
+)
+wind_barplot = wind_prediction + wind_history
+st.altair_chart(wind_barplot,
+                use_container_width=True
+             )
+
+sun_history = alt.Chart(energy).mark_line().encode(
+    y="Solaire",
+    x="time"
+)
+sun_prediction = alt.Chart(energy).mark_line(color="red").encode(
+    y="PV Prediction",
+    x="time"
+)
+sun_barplot = sun_prediction + sun_history
+st.altair_chart(sun_barplot,
+                use_container_width=True
+             )

src/energy_forecast/dashboard/pages/3_consumption_prediction.py

@@ -0,0 +1,29 @@
+from energy_forecast.energy import ECO2MixDownloader
+from energy_forecast.meteo import ArpegeSimpleAPI
+from energy_forecast.consumption_forecast import PredictionForecastAPI
+from energy_forecast import ROOT_DIR
+import pandas as pd
+import streamlit as st
+import altair as alt
+
+
+env_file = ROOT_DIR / ".env"
+with env_file.open("r") as f:
+    secret = f.readline().strip().split("=", 1)[1]
+
+consumption_forecast_api = PredictionForecastAPI(secret=secret)
+consumption_forecast = consumption_forecast_api.get_weekly_forecast(
+    start_date=pd.Timestamp.now().date()
+).reset_index()
+
+st.title("Prévision de production ENR")
+
+
+
+consumption_chart = alt.Chart(consumption_forecast).mark_line().encode(
+    y="predicted_consumption",
+    x="time"
+)
+st.altair_chart(consumption_chart,
+                use_container_width=True
+             )

src/energy_forecast/dashboard/pages/4_prediction_tempo.py

@@ -0,0 +1,15 @@
+import streamlit as st
+import pandas as pd
+
+
+st.title("Prévision de production ENR")
+
+col1, col2, col3 = st.columns(3)
+
+today = pd.Timestamp.now().date()
+
+for offset, col in zip(range(3), [col1, col2, col3]):
+    text = f"Prévision pour le {today + pd.Timedelta(days=offset)}"
+    text += "\n\n <h1 style='color: white;'> Jour Bleu </h1>"
+    # an html blue square the size of the column with the text and H1 in white
+    col.markdown(f"<div style='background-color: blue ; padding: 10px ; border-radius: 10px ; color: white ; text-align: center'>{text}</div>", unsafe_allow_html=True)