File size: 4,671 Bytes
b8fba85 | 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 | import gradio as gr
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import plotly.graph_objects as go
from plotly.subplots import make_subplots
def visualize_sensor_data(file):
"""Visualize IoT sensor data from uploaded CSV file"""
if file is None:
return generate_sample_chart(), "📊 Showing sample IoT sensor data"
try:
df = pd.read_csv(file.name)
# Create subplots
fig = make_subplots(
rows=2, cols=2,
subplot_titles=('Temperature (°C)', 'Humidity (%)', 'Vibration (Hz)', 'Power (W)')
)
# Add traces
if 'timestamp' in df.columns or 'time' in df.columns:
x_col = 'timestamp' if 'timestamp' in df.columns else 'time'
else:
x_col = df.columns[0]
if 'temperature' in df.columns:
fig.add_trace(go.Scatter(x=df[x_col], y=df['temperature'], name='Temperature', line=dict(color='#ff6b6b')), row=1, col=1)
if 'humidity' in df.columns:
fig.add_trace(go.Scatter(x=df[x_col], y=df['humidity'], name='Humidity', line=dict(color='#4ecdc4')), row=1, col=2)
if 'vibration' in df.columns:
fig.add_trace(go.Scatter(x=df[x_col], y=df['vibration'], name='Vibration', line=dict(color='#95e1d3')), row=2, col=1)
if 'power' in df.columns:
fig.add_trace(go.Scatter(x=df[x_col], y=df['power'], name='Power', line=dict(color='#f38181')), row=2, col=2)
fig.update_layout(height=600, showlegend=False, title_text="IoT Sensor Data Visualization")
summary = f"✅ Data loaded: {len(df)} records | Columns: {', '.join(df.columns.tolist())}"
return fig, summary
except Exception as e:
return generate_sample_chart(), f"⚠️ Error: {str(e)}. Showing sample data instead."
def generate_sample_chart():
"""Generate sample IoT sensor data for demonstration"""
timestamps = pd.date_range(start=datetime.now() - timedelta(hours=24), periods=100, freq='15min')
df = pd.DataFrame({
'timestamp': timestamps,
'temperature': 20 + np.random.randn(100) * 5,
'humidity': 60 + np.random.randn(100) * 10,
'vibration': 50 + np.random.randn(100) * 15,
'power': 100 + np.random.randn(100) * 20
})
fig = make_subplots(
rows=2, cols=2,
subplot_titles=('Temperature (°C)', 'Humidity (%)', 'Vibration (Hz)', 'Power (W)')
)
fig.add_trace(go.Scatter(x=df['timestamp'], y=df['temperature'], name='Temperature', line=dict(color='#ff6b6b')), row=1, col=1)
fig.add_trace(go.Scatter(x=df['timestamp'], y=df['humidity'], name='Humidity', line=dict(color='#4ecdc4')), row=1, col=2)
fig.add_trace(go.Scatter(x=df['timestamp'], y=df['vibration'], name='Vibration', line=dict(color='#95e1d3')), row=2, col=1)
fig.add_trace(go.Scatter(x=df['timestamp'], y=df['power'], name='Power', line=dict(color='#f38181')), row=2, col=2)
fig.update_layout(height=600, showlegend=False, title_text="Sample IoT Sensor Data (24 Hours)")
return fig
# Create Gradio interface
with gr.Blocks(title="IoT Sensor Visualizer - Anktechsol", theme=gr.themes.Soft()) as demo:
gr.Markdown("""
# 🌐 IoT Sensor Visualizer for Edge AI Projects
### by **Anktechsol** - AI + IoT Experts
Upload your IoT sensor CSV file to visualize temperature, humidity, vibration, and power data.
**No file?** We'll show you sample data!
**Expected CSV format:** `timestamp, temperature, humidity, vibration, power`
""")
with gr.Row():
with gr.Column(scale=1):
file_input = gr.File(label="📁 Upload CSV File", file_types=[".csv"])
visualize_btn = gr.Button("📊 Visualize Data", variant="primary", size="lg")
gr.Markdown("""---
### 🔗 Links
- [Anktechsol Website](https://anktechsol.com)
- [More AIoT Tools](https://huggingface.co/anktechsol)
""")
with gr.Column(scale=3):
plot_output = gr.Plot(label="Sensor Data Visualization")
status_output = gr.Textbox(label="Status", interactive=False)
# Event handler
visualize_btn.click(
fn=visualize_sensor_data,
inputs=[file_input],
outputs=[plot_output, status_output]
)
# Auto-load sample data on startup
demo.load(fn=lambda: (generate_sample_chart(), "📊 Sample data loaded. Upload your CSV to visualize your IoT data!"),
outputs=[plot_output, status_output])
if __name__ == "__main__":
demo.launch() |