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46d0ccd cb70105 46d0ccd cb70105 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | import streamlit as st
import pandas as pd
import joblib
import numpy as np
from tensorflow.keras.models import load_model
MODEL_PATH = 'src/lstm_energy_prediction.h5'
SCALER_PATH = 'src/scaler_energy.joblib'
FEATURES = ['Global_active_power', 'Global_reactive_power', 'Voltage',
'Global_intensity', 'Sub_metering_2', 'Sub_metering_1', 'Sub_metering_3']
@st.cache_resource
def load_assets():
try:
# Load the LSTM model
model = load_model(MODEL_PATH)
# Load the scaler
scaler = joblib.load(SCALER_PATH)
return model, scaler
except Exception as e:
st.error(f"Error loading assets. Ensure '{MODEL_PATH}' (H5 model) and '{SCALER_PATH}' (Scaler) are uploaded. Error: {e}")
return None, None
def prepare_input_and_predict(model, scaler, input_data):
# 1. Convert input data to a numpy array for scaling
raw_input = np.array([input_data[f] for f in FEATURES]).reshape(1, -1)
# 2. Scale the input data
scaled_input = scaler.transform(raw_input)
# 3. Reshape for LSTM: [Samples, Time Steps, Features] = [1, 1, 7]
lstm_input = scaled_input[:, :len(FEATURES)].reshape(1, 1, len(FEATURES))
# 4. Predict the scaled output (var1(t) = Global_active_power at time t)
scaled_prediction = model.predict(lstm_input)
# 5. Inverse Transform the prediction
dummy_inverse = np.zeros(raw_input.shape)
dummy_inverse[0, 0] = scaled_prediction[0, 0]
actual_prediction = scaler.inverse_transform(dummy_inverse)[0, 0]
return actual_prediction
# --- Streamlit Interface ---
st.set_page_config(page_title="Energy Prediction App", layout="centered")
st.title("⚡ Hourly Energy Consumption Prediction (LSTM)")
st.markdown("Enter the consumption values from the last hour to predict the next hour's **Global Active Power**.")
model, scaler = load_assets()
if model is not None and scaler is not None:
st.sidebar.header("Last Hour's Consumption Data")
# --- INPUT WIDGETS (Matching the 7 features) ---
# These input keys MUST match the FEATURES list.
global_active_power = st.sidebar.number_input("Global Active Power:", value=0.5, step=0.01)
global_reactive_power = st.sidebar.number_input("Global Reactive Power:", value=0.08, step=0.01)
voltage = st.sidebar.number_input("Voltage:", value=240.0, step=0.1)
global_intensity = st.sidebar.number_input("Global Intensity:", value=2.5, step=0.1)
sub_metering_2 = st.sidebar.number_input("Sub Metering 2:", value=0.0, step=0.1)
sub_metering_1 = st.sidebar.number_input("Sub Metering 1:", value=0.0, step=0.1)
sub_metering_3 = st.sidebar.number_input("Sub Metering 3:", value=17.0, step=0.1)
# Collect inputs
input_data = {
'Global_active_power': global_active_power,
'Global_reactive_power': global_reactive_power,
'Voltage': voltage,
'Global_intensity': global_intensity,
'Sub_metering_2': sub_metering_2,
'Sub_metering_1': sub_metering_1,
'Sub_metering_3': sub_metering_3
}
if st.button("Predict Next Hour's Power"):
with st.spinner('Predicting...'):
predicted_power = prepare_input_and_predict(model, scaler, input_data)
if predicted_power is not None:
st.success("Prediction Successful!")
st.markdown(f"### Predicted Next Hour's Global Active Power:")
st.markdown(f"**{predicted_power:,.2f} kW**")
st.info("Note: Prediction is based on the complex feature transformation (lag features) used during model training.") |