Create app.py

app.py

@@ -0,0 +1,177 @@
+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()