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Sleeping
File size: 4,121 Bytes
a933b83 351e759 a933b83 | 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 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | import gradio as gr
import pandas as pd
import json
import argparse
import time
import os
# Read demo data received from AWS IoT MQTT Broker
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
LOG_FILE = os.path.join(CURRENT_DIR, "mqtt_received_demo_data.json")
def load_data():
try:
if not os.path.exists(LOG_FILE):
return pd.DataFrame(columns=["timestamp", "energy_kW"])
with open(LOG_FILE, "r") as file:
data = json.load(file)
df = pd.DataFrame(data)
df['energy_kW'] = df['energy_kW'].astype(float)
df['timestamp'] = pd.to_datetime(df['timestamp'], format='%d/%m/%Y')
return df.head(10) # Show first 10 readings
except json.JSONDecodeError:
print("Error decoding JSON from log file.")
return pd.DataFrame(columns=["timestamp", "energy_kW"])
except Exception as e:
print(f"Error loading data: {e}")
return pd.DataFrame(columns=["timestamp", "energy_kW"])
def update_data(df):
while True:
time.sleep(1)
new_data = load_data() # Load only new or changed data
df.update(value=new_data)
yield
def generate_dashboard(plot_type, show_power, show_timestamp):
df = load_data()
if df.empty:
return df, gr.update(visible=False), gr.update(visible=False)
# Filter columns based on checkboxes
columns_to_show = []
if show_timestamp:
columns_to_show.append("timestamp")
if show_power:
columns_to_show.append("energy_kW")
df_filtered = df[columns_to_show]
table_update = gr.update(value=df_filtered, visible=(plot_type == "Table"))
line_plot_update = gr.update(
value=df_filtered,
x="timestamp" if show_timestamp else None,
y="energy_kW" if show_power else None,
title="Energy Consumption Over Time",
tooltip=columns_to_show,
visible=(plot_type == "Line Plot"),
height="50vh"
)
bar_plot_update = gr.update(
value=df_filtered,
x="timestamp" if show_timestamp else "energy_kW",
y="energy_kW" if show_power else None,
title="Energy Consumption Distribution",
tooltip=columns_to_show,
visible=(plot_type == "Histogram"),
height="50vh"
)
return table_update, line_plot_update, bar_plot_update
custom_css = """
@media (min-width: 768px) {
.plot-container {
height: 500px !important;
}
}
@media (max-width: 767px) {
.plot-container {
height: 50vh !important;
}
}
"""
with gr.Blocks(css=custom_css) as app:
gr.Markdown("<h1 style='text-align: center;'>⚡ SmartMeterSim Dashboard ⚡</h1>")
gr.Markdown("<h3 style='text-align: center;'>📈 Optimize your energy footprint instantly with real-time usage data.</h3>")
with gr.Row():
with gr.Column(scale=2): # Sidebar (20% width)
gr.Markdown("## 💡 Measurements")
measure_power = gr.Checkbox(label="Power", value=True)
measure_timestamp = gr.Checkbox(label="Timestamp", value=True)
gr.Markdown("## 📊 Graphs")
plot_type = gr.Dropdown(["Table", "Line Plot", "Histogram"], value="Table", label="Select Visualization")
refresh_btn = gr.Button("🔄 Refresh Data")
with gr.Column(scale=8): # Main content (80% width)
df_display = gr.Dataframe(value=load_data(), label="Energy Data Table", interactive=False)
app.load(lambda: update_data(df_display))
line_plot = gr.LinePlot(visible=False, height="50vh", elem_classes="plot-container")
bar_plot = gr.BarPlot(visible=False, height="50vh", elem_classes="plot-container")
refresh_btn.click(
fn=generate_dashboard,
inputs=[plot_type, measure_power, measure_timestamp],
outputs=[df_display, line_plot, bar_plot]
)
plot_type.change(
fn=generate_dashboard,
inputs=[plot_type, measure_power, measure_timestamp],
outputs=[df_display, line_plot, bar_plot]
)
app.queue()
app.launch()
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