Upload app.py

app.py

@@ -0,0 +1,133 @@
+import os
+import time
+import pandas as pd
+import numpy as np
+import joblib
+import requests
+import streamlit as st
+from streamlit_autorefresh import st_autorefresh
+
+# Auto-refresh every 5 seconds
+st_autorefresh(interval=5000, key="refresh")
+
+# Load model
+@st.cache_resource
+def load_model():
+    return joblib.load("rf_model.pkl")
+
+model = load_model()
+
+# Supabase config
+SUPABASE_URL = os.environ["SUPABASE_URL"]
+SUPABASE_KEY = os.environ["SUPABASE_KEY"]
+TABLE = "smart_meter_readings_1year"
+
+# Initialize session state
+if "row_index" not in st.session_state:
+    st.session_state.row_index = 0
+if "history" not in st.session_state:
+    st.session_state.history = pd.DataFrame()
+
+# Fetch all data
+@st.cache_data
+def fetch_all_data():
+    url = f"{SUPABASE_URL}/rest/v1/{TABLE}?select=*&order=timestamp.asc"
+    headers = {
+        "apikey": SUPABASE_KEY,
+        "Authorization": f"Bearer {SUPABASE_KEY}"
+    }
+    r = requests.get(url, headers=headers)
+    if r.ok:
+        return pd.DataFrame(r.json())
+    else:
+        st.error(f"❌ Error fetching data: {r.status_code}")
+        return pd.DataFrame()
+
+df_all = fetch_all_data()
+
+# Debug sidebar
+st.sidebar.title("🛠 Debug Info")
+st.sidebar.write("Row index:", st.session_state.row_index)
+st.sidebar.write("Total rows:", len(df_all))
+if not df_all.empty and st.session_state.row_index < len(df_all):
+    st.sidebar.write("Next row:", df_all.iloc[st.session_state.row_index].to_dict())
+
+# Get next row
+def get_next_row():
+    if st.session_state.row_index < len(df_all):
+        row = df_all.iloc[[st.session_state.row_index]]
+        st.session_state.row_index += 1
+        return row
+    return pd.DataFrame()
+
+# Feature engineering
+def engineer(df):
+    # Handle timestamp
+    if pd.api.types.is_numeric_dtype(df["timestamp"]):
+        df["datetime"] = pd.to_datetime(df["timestamp"], unit="s")
+    else:
+        df["datetime"] = pd.to_datetime(df["timestamp"])
+
+    df["hour_of_day"] = df["datetime"].dt.hour
+    df["lag_30min"] = df["power_consumption_kwh"].shift(1)
+    df["lag_1h"] = df["power_consumption_kwh"].shift(2)
+    df["rolling_avg_1h"] = df["power_consumption_kwh"].rolling(2).mean()
+    df["rolling_avg_2h"] = df["power_consumption_kwh"].rolling(4).mean()
+    df["is_weekend"] = df["datetime"].dt.weekday >= 5
+    df["hour_sin"] = np.sin(2 * np.pi * df["hour_of_day"] / 24)
+    df["hour_cos"] = np.cos(2 * np.pi * df["hour_of_day"] / 24)
+
+    # One-hot encode property_type and region
+    df = pd.get_dummies(df, columns=["property_type", "region"], drop_first=False)
+
+    # Ensure all expected features exist
+    expected_features = [
+        'lag_30min', 'lag_1h',
+        'rolling_avg_1h', 'rolling_avg_2h',
+        'hour_of_day', 'is_weekend',
+        'hour_sin', 'hour_cos',
+        'temperature_c', 'ev_owner', 'solar_installed',
+        'property_type_commercial', 'property_type_residential',
+        'region_north', 'region_south', 'region_east', 'region_west'
+    ]
+
+    for col in expected_features:
+        if col not in df.columns:
+            df[col] = 0
+
+    return df
+
+# UI layout
+st.title("⚡ Gridflux: Live Smart-Meter Forecast")
+
+placeholder_chart = st.empty()
+placeholder_metric = st.empty()
+
+new_row = get_next_row()
+
+if not new_row.empty:
+    st.session_state.history = pd.concat([st.session_state.history, new_row], ignore_index=True)
+    df_feat = engineer(st.session_state.history).dropna()
+
+    if not df_feat.empty:
+        latest_input = df_feat.iloc[[-1]][[
+            'lag_30min', 'lag_1h',
+            'rolling_avg_1h', 'rolling_avg_2h',
+            'hour_of_day', 'is_weekend',
+            'hour_sin', 'hour_cos',
+            'temperature_c', 'ev_owner', 'solar_installed',
+            'property_type_commercial', 'property_type_residential',
+            'region_north', 'region_south', 'region_east', 'region_west'
+        ]]
+
+        prediction = model.predict(latest_input)[0]
+
+        # Show outputs
+        chart_df = st.session_state.history.copy()
+        chart_df["datetime"] = pd.to_datetime(chart_df["timestamp"])
+        chart_df.set_index("datetime", inplace=True)
+
+        placeholder_chart.line_chart(chart_df["power_consumption_kwh"])
+        placeholder_metric.metric("🔮 Predicted Power Usage (kWh)", f"{prediction:.3f}")
+else:
+    st.success("✅ All data processed.")