initial commit

app.py

@@ -0,0 +1,52 @@
+import streamlit as st
+
+from components import summary_view
+import utils
+
+wandb_api_key = st.secrets["wandb"]
+
+st.set_page_config(page_title='Electricity Demand Dashboard', layout='wide')
+
+
+@st.cache_data(ttl=86400)
+def fetch_data():
+    return utils.get_wandb_data(
+        st.secrets['wandb']['entity'],
+        "enfobench-electricity-demand",
+        st.secrets["wandb"]['api_key'],
+        job_type="metrics",
+    )
+
+
+data = fetch_data()
+models = sorted(data['model'].unique().tolist())
+models_to_plot = set()
+model_groups: dict[str, list[str]] = {}
+
+
+for model in models:
+    group, model_name = model.split(".", maxsplit=1)
+    if group not in model_groups:
+        model_groups[group] = []
+    model_groups[group].append(model_name)
+
+
+with st.sidebar:
+    left, right = st.columns(2)  # Create two columns within the right column for side-by-side images
+    with left:
+        st.image("./images/ku_leuven_logo.png")   # Adjust the path and width as needed
+    with right:
+        st.image("./images/energyville_logo.png")
+
+    view = st.selectbox("View", ["Summary", "Raw Data"], index=0)
+
+    st.header("Models to include")
+    for model_group, models in model_groups.items():
+        st.text(model_group)
+        for model_name in models:
+            to_plot = st.checkbox(model_name, value=True)
+            if to_plot:
+                models_to_plot.add(f"{model_group}.{model_name}")
+
+if view == "Summary":
+    summary_view(data, models_to_plot)

components.py

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+import streamlit as st
+
+
+def summary_view(data, models_to_plot: set[str]):
+    st.title("Summary View")
+    st.write(data)
+    st.write(models_to_plot)
+
+    if st.button('Say Hello'):
+        st.balloons()

utils.py

@@ -0,0 +1,29 @@
+import pandas as pd
+import wandb
+
+
+def get_wandb_data(entity: str, project: str, api_key: str, job_type: str) -> pd.DataFrame:
+    api = wandb.Api(api_key=api_key)
+
+    # Project is specified by <entity/project-name>
+    filter_dict = {"jobType": job_type}
+    runs = api.runs(f"{entity}/{project}", filters=filter_dict)
+
+    summary_list, config_list, name_list = [], [], []
+    for run in runs:
+        # .summary contains the output keys/values for metrics like accuracy.
+        #  We call ._json_dict to omit large files
+        summary_list.append(run.summary._json_dict)
+
+        # .config contains the hyperparameters.
+        #  We remove special values that start with _.
+        config_list.append({k: v for k, v in run.config.items()})
+
+        # .name is the human-readable name of the run.
+        name_list.append(run.name)
+
+    summary_df = pd.json_normalize(summary_list, max_level=1)
+    config_df = pd.json_normalize(config_list, max_level=2)
+    runs_df = pd.concat([summary_df, config_df], axis=1)
+    runs_df.index = name_list
+    return runs_df