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import gradio as gr
import pandas as pd
import random
from transformers import AutoTokenizer, AutoModelForCausalLM
import plotly.graph_objects as go
# Attempt to import PyTorch
try:
import torch
except ImportError:
torch = None
# Load GPT-2 model if PyTorch is available
if torch:
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForCausalLM.from_pretrained("gpt2")
else:
tokenizer, model = None, None
print("PyTorch not available. GPT-2 model will not be loaded.")
# Analyze energy data and provide consumption details, recommendations, and weather tips
def analyze_energy_data(energy_data, location, language):
appliances = {}
total_kwh = 0
peak_hours_rate = 20
off_peak_hours_rate = 12
benchmarks = {"AC": 400, "Refrigerator": 200, "Lighting": 150, "Fan": 100}
alerts = []
try:
for line in energy_data.strip().split("\n"):
appliance, kwh = line.split(":")
kwh_value = float(kwh.strip().split(" ")[0])
appliances[appliance.strip()] = kwh_value
total_kwh += kwh_value
if appliance.strip() in benchmarks and kwh_value > benchmarks[appliance.strip()]:
alert_message = (
f"Your {appliance.strip()} usage exceeds the limit by "
f"{kwh_value - benchmarks[appliance.strip()]:.2f} kWh."
)
alerts.append(alert_message)
except Exception:
return (
"Error: Enter data in the correct format (e.g., AC: 500 kWh).",
"",
"",
"",
"",
"",
0.0
)
total_bill = total_kwh * peak_hours_rate
optimized_bill = sum(
appliances[app] * (off_peak_hours_rate if app in ["AC", "Refrigerator"] else peak_hours_rate)
for app in appliances
)
savings = total_bill - optimized_bill
carbon_emissions = total_kwh * 0.707 # Approx kg of CO2 per kWh
weather_tips = (
f"Considering high temperatures in {location}, keep windows closed during peak heat hours to optimize cooling."
if "Lahore" in location
else "Check local weather to optimize energy usage."
)
return (
f"Your current bill is PKR {total_bill:.2f}, potentially saving PKR {savings:.2f}.",
"\n".join([f"{appliance}: {random.choice(['Use during off-peak hours.', 'Turn off when not in use.'])}" for appliance in appliances]),
weather_tips,
"\n".join(alerts),
f"Your carbon footprint: {carbon_emissions:.2f} kg of CO2. Consider using renewable energy.",
f"AI Recommendation: Optimize usage of AC and lighting based on peak hours to reduce costs and emissions.",
savings
)
# Build the Gradio UI
def build_ui():
with gr.Blocks() as demo:
gr.Markdown("## Energy Consumption Analyzer")
with gr.Row():
energy_data = gr.Textbox(
label="Enter Energy Data",
placeholder="e.g., AC: 500 kWh\nRefrigerator: 300 kWh",
lines=5
)
location = gr.Textbox(label="Enter Location", placeholder="e.g., Lahore")
language = gr.Dropdown(
label="Select Language",
choices=["English", "Urdu"],
value="English"
)
with gr.Row():
analyze_button = gr.Button("Analyze")
with gr.Row():
output_bill = gr.Text(label="Estimated Bill")
output_recommendations = gr.Text(label="Recommendations")
with gr.Row():
output_weather_tips = gr.Text(label="Weather Tips")
output_alerts = gr.Text(label="Alerts")
output_carbon_footprint = gr.Text(label="Carbon Footprint")
output_ai_recommendation = gr.Text(label="AI Recommendations")
analyze_button.click(
analyze_energy_data,
inputs=[energy_data, location, language],
outputs=[
output_bill,
output_recommendations,
output_weather_tips,
output_alerts,
output_carbon_footprint,
output_ai_recommendation
]
)
return demo
# Launch the Gradio app
demo = build_ui()
demo.launch()
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