app.py

import gradio as gr
import pandas as pd
import random
from transformers import AutoTokenizer, AutoModelForCausalLM
import plotly.graph_objects as go

# Load GPT-2 model from Hugging Face
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForCausalLM.from_pretrained("gpt2")

# 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  # Return savings for ROI calculation
    )

# Gamification: Leaderboard and Badges
leaderboard = {"User1": 30, "User2": 25, "User3": 20}

def update_leaderboard(user_name, reduction_percentage):
    leaderboard[user_name] = leaderboard.get(user_name, 0) + reduction_percentage
    sorted_leaderboard = sorted(leaderboard.items(), key=lambda x: x[1], reverse=True)
    leaderboard_text = "\n".join([f"{i+1}. {user}: {points} points" for i, (user, points) in enumerate(sorted_leaderboard)])
    badge = "Gold" if reduction_percentage > 30 else "Silver" if reduction_percentage > 20 else "Bronze"
    return leaderboard_text, f"Badge earned: {badge}"

# IoT-based Smart Device Integration (Simulated)
def fetch_smart_device_data():
    data = {
        "AC": random.randint(350, 500),
        "Lighting": random.randint(100, 200),
        "Refrigerator": random.randint(180, 250),
    }
    return "\n".join([f"{k}: {v} kWh" for k, v in data.items()])

# Customized Energy Visualization: Interactive Bar Chart
def visualize_energy_data(appliances):
    df = pd.DataFrame(appliances.items(), columns=["Appliance", "Energy (kWh)"])
    fig = go.Figure(data=[go.Bar(
        x=df["Appliance"],
        y=df["Energy (kWh)"],
        marker=dict(color=['#FF6347' if x > 300 else '#32CD32' for x in df["Energy (kWh)"]])  # High usage appliances in red, low in green
    )])
    fig.update_layout(
        title="Appliance-wise Energy Usage",
        xaxis_title="Appliance",
        yaxis_title="Energy (kWh)",
        hovermode='x unified',
    )
    return fig

# ROI Calculator
def calculate_roi(initial_investment, savings):
    try:
        roi = (savings / initial_investment) * 100
    except ZeroDivisionError:
        roi = 0.0
    return f"Your ROI is {roi:.2f}% based on an initial investment of {initial_investment} PKR and savings of {savings:.2f} PKR."

# Chatbot Interface
def chatbot_interface(home_size, location, energy_data, language, user_name, reduction_percentage, initial_investment):
    energy_analysis, tips, weather_tips, alerts, carbon_output, ai_recommendation, savings = analyze_energy_data(energy_data, location, language)
    appliances = {
        appliance: float(kwh.strip(" kWh"))
        for appliance, kwh in [line.split(":") for line in energy_data.strip().split("\n")]
    }
    energy_chart = visualize_energy_data(appliances)
    leaderboard_text, badge = update_leaderboard(user_name, reduction_percentage)
    roi_output = calculate_roi(initial_investment, savings)
    return energy_analysis, tips, weather_tips, alerts, carbon_output, ai_recommendation, energy_chart, leaderboard_text, badge, roi_output

# Build UI with Gradio
def build_ui():
    with gr.Blocks() as demo:
        with gr.Row():
            home_size = gr.Slider(minimum=500, maximum=5000, step=100, label="Home Size (sq ft)", value=1200)
            location = gr.Textbox(label="Location (City)", placeholder="Enter your city...", info="Specify the city to get weather-based tips.")
            energy_data = gr.Textbox(
                label="Energy Data (Appliance: kWh)",
                placeholder="e.g., AC: 500 kWh\nLighting: 120 kWh\nRefrigerator: 150 kWh",
                lines=5,
                info="Enter your appliances and their energy usage."
            )
            language = gr.Radio(choices=["English", "Urdu"], label="Language")

        with gr.Row():
            user_name = gr.Textbox(label="Your Name", placeholder="Enter your name...", info="Provide your name to track performance.")
            reduction_percentage = gr.Slider(minimum=0, maximum=50, step=1, label="Energy Reduction (%)", value=10)
            initial_investment = gr.Number(label="Initial Investment (PKR)", value=10000)

        fetch_data_button = gr.Button("Fetch Smart Device Data")
        fetch_data_output = gr.Textbox(label="Smart Device Data", interactive=False)

        fetch_data_button.click(fetch_smart_device_data, inputs=[], outputs=fetch_data_output)

        submit_button = gr.Button("Analyze", variant="primary", elem_id="submit-button")

        with gr.Row():
            energy_output = gr.Textbox(label="Energy Consumption Analysis", interactive=False)
            tips_output = gr.Textbox(label="Optimization Tips", interactive=False)
            weather_output = gr.Textbox(label="Weather-specific Tips", interactive=False)
            alerts_output = gr.Textbox(label="Alerts", interactive=False)
            ai_recommendations = gr.Textbox(label="AI Recommendations", interactive=False)

        with gr.Row():
            carbon_output = gr.Textbox(label="Carbon Footprint", interactive=False)
            energy_chart = gr.Plot(label="Energy Visualization")

        with gr.Row():
            leaderboard_output = gr.Textbox(label="Leaderboard", interactive=False)
            badge_output = gr.Textbox(label="Your Badge", interactive=False)

        with gr.Row():
            roi_output = gr.Textbox(label="Return on Investment (ROI)", interactive=False)

        submit_button.click(
            chatbot_interface,
            inputs=[home_size, location, energy_data, language, user_name, reduction_percentage, initial_investment],
            outputs=[energy_output, tips_output, weather_output, alerts_output, carbon_output, ai_recommendations, energy_chart, leaderboard_output, badge_output, roi_output]
        )

    return demo

demo = build_ui()
demo.launch()