import numpy as np
import pandas as pd
import plotly.graph_objects as go
import streamlit as st
from sklearn.metrics import mean_squared_error
from src.data_api_calls import get_combined_data
USERNAME = "admin"
PASSWORD = "password"
st.title("Admin Panel")
# Use session state to remember login state
if "login_success" not in st.session_state:
st.session_state.login_success = False
# Login Form
if not st.session_state.login_success:
with st.form("login_form"):
st.write("Please login to access the admin dashboard:")
username = st.text_input("Username")
password = st.text_input("Password", type="password")
login_button = st.form_submit_button("Login")
if login_button:
if username == USERNAME and password == PASSWORD:
st.session_state.login_success = True
st.success("Login successful!")
else:
st.error("Invalid username or password.")
else:
# Fetching the combined data
table_data = get_combined_data()
# Check for missing values
missing_values = table_data.isnull()
# Display the main data table
st.subheader("Data used for the prediction")
# Display message based on whether data is complete
if missing_values.values.any():
# Warning message if there are missing values
st.markdown(
"Warning: Some data is missing!
",
unsafe_allow_html=True,
)
# Identify columns with missing values
missing_columns = table_data.columns[missing_values.any()].tolist()
# Identify rows (dates) with missing values
missing_rows = table_data[missing_values.any(axis=1)]["Date"].tolist()
# Display additional information about missing columns and rows
if missing_columns:
st.markdown(f"**Columns with missing data:** {', '.join(missing_columns)}")
if missing_rows:
st.markdown(
f"**Rows with missing data (dates):** {', '.join(missing_rows)}"
)
else:
# Success message if no data is missing
st.markdown(
"All data is complete!
",
unsafe_allow_html=True,
)
st.dataframe(table_data)
# Actual data vs 1,2,3 days ahead predictions
actual_data = pd.read_csv("pollution_data.csv")
prediction_data = pd.read_csv("predictions_history.csv")
col1, col2 = st.columns(2)
with col1:
pollutant = st.radio("Select a pollutant", ("O3", "NO2"))
with col2:
days_ahead = st.radio("Select days ahead for prediction", (1, 2, 3))
predictions = prediction_data[prediction_data["pollutant"] == pollutant]
actual = actual_data[["date", pollutant]].rename(
columns={pollutant: "actual_value"}
)
predictions_filtered = predictions[
predictions["date_predicted"]
== (
pd.to_datetime(predictions["date"]) - pd.Timedelta(days=days_ahead)
).dt.strftime("%Y-%m-%d")
]
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=actual["date"],
y=actual["actual_value"],
mode="lines+markers",
name="Ground Truth",
line=dict(color="green", width=3),
)
)
fig.add_trace(
go.Scatter(
x=predictions_filtered["date"],
y=predictions_filtered["prediction_value"],
mode="lines+markers",
name=f"Prediction {days_ahead} day(s) ahead",
line=dict(dash="dash", color="orange", width=3),
)
)
fig.update_layout(
title=f"{pollutant} Predictions vs Actual Values",
xaxis_title="Date",
yaxis_title=f"{pollutant} Concentration",
legend=dict(x=0, y=1),
yaxis=dict(range=[0, 60]),
template="plotly_white",
xaxis=dict(
title="Date",
type="date",
tickmode="array",
tickvals=predictions["date"],
tickformat="%d-%b",
tickangle=-45,
tickcolor="gray",
),
)
st.plotly_chart(fig)
# Evaluation Function
def evaluate_predictions_all_days(actual, predictions):
rmse_values_all = {"O3": [], "NO2": []}
smape_values_all = {"O3": [], "NO2": []}
for pollutant in ["O3", "NO2"]:
predictions_pollutant = predictions[predictions["pollutant"] == pollutant]
actual_pollutant = actual_data[["date", pollutant]].rename(
columns={pollutant: "actual_value"}
)
# Calculate RMSE and SMAPE for each day (1st, 2nd, and 3rd)
for i in range(1, 4):
predictions_filtered = predictions_pollutant[
predictions_pollutant["date_predicted"]
== (
pd.to_datetime(predictions_pollutant["date"])
- pd.Timedelta(days=i)
).dt.strftime("%Y-%m-%d")
]
actual_filtered = actual_pollutant[
actual_pollutant["date"].isin(predictions_filtered["date"])
]
merged = pd.merge(
actual_filtered,
predictions_filtered,
left_on="date",
right_on="date",
)
if not merged.empty:
actual_values = merged["actual_value"].values
prediction_values = merged["prediction_value"].values
rmse = np.sqrt(mean_squared_error(actual_values, prediction_values))
rmse_values_all[pollutant].append(rmse)
smape = (
100
/ len(actual_values)
* np.sum(
2
* np.abs(prediction_values - actual_values)
/ (np.abs(actual_values) + np.abs(prediction_values))
)
)
smape_values_all[pollutant].append(smape)
# Plot RMSE and SMAPE for both pollutants
fig_rmse = go.Figure()
for day in range(3):
fig_rmse.add_trace(
go.Bar(
x=["O3", "NO2"],
y=[rmse_values_all["O3"][day], rmse_values_all["NO2"][day]],
name=f"Day {day + 1}",
)
)
fig_rmse.update_layout(
title="RMSE for Predictions Over 3 Days",
yaxis_title="RMSE",
xaxis_title="Pollutant",
barmode="group",
)
st.plotly_chart(fig_rmse)
fig_smape = go.Figure()
for day in range(3):
fig_smape.add_trace(
go.Bar(
x=["O3", "NO2"],
y=[smape_values_all["O3"][day], smape_values_all["NO2"][day]],
name=f"Day {day + 1}",
)
)
fig_smape.update_layout(
title="SMAPE for Predictions Over 3 Days",
yaxis_title="SMAPE (%)",
xaxis_title="Pollutant",
barmode="group",
)
st.plotly_chart(fig_smape)
# Calculate total current SMAPE and RMSE
total_O3_smape = sum(smape_values_all["O3"]) / len(smape_values_all)
total_NO2_smape = sum(smape_values_all["NO2"]) / len(smape_values_all)
total_O3_rmse = sum(rmse_values_all["O3"]) / len(rmse_values_all)
total_NO2_rmse = sum(rmse_values_all["NO2"]) / len(rmse_values_all)
# Display metrics table
metrics_data = {
"Metric": [
"Current NO2 SMAPE (%)",
"Current NO2 RMSE (µg/m3)",
"Current O3 SMAPE (%)",
"Current O3 RMSE (µg/m3)",
],
"Value": [total_NO2_smape, total_NO2_rmse, total_O3_smape, total_O3_rmse],
}
metrics_df = pd.DataFrame(metrics_data)
st.table(metrics_df)
evaluate_predictions_all_days(actual_data, prediction_data)