app.py

import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
from sklearn.linear_model import LinearRegression
from datetime import datetime, timedelta
import streamlit as st
from crewai import Agent, Task, Crew
import os
import random

# Set up your OpenAI API key (ensure you handle your API keys securely in production)
os.environ["OPENAI_API_KEY"] = "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"

# Define your agents and tasks as per your setup

# Environmental Analyst Agent
environmental_agent = Agent(
    role="Environmental Analyst",
    goal="Analyze and propose environmentally sustainable designs.",
    backstory="An experienced environmentalist with a strong track record in developing sustainable urban plans that reduce environmental impact and promote eco-friendly living.",
    tool=["EcoImpactAnalysisTool", "RenewableEnergySimulator"]
)

# Human Traffic Carbon Footprint Analyst Agent
human_traffic_carbon_footprint_agent = Agent(
    role="Human Traffic Carbon Footprint Analyst",
    goal="Assess and mitigate the carbon footprint resulting from human traffic around buildings.",
    backstory="Dedicated to reducing urban carbon emissions, with expertise in traffic pattern analysis and sustainable transportation solutions.",
    tool=[
        "TrafficSimulationTool",
        "CarbonEmissionCalculator",
        "SustainableTransportPlanner"
    ]
)

# Define tasks for each agent
assess_sustainability_task = Task(
    description="Assess environmental sustainability of urban design.",
    agent=environmental_agent
)

mitigate_traffic_carbon_footprint_task = Task(
    description="Mitigate carbon footprint from human traffic.",
    agent=human_traffic_carbon_footprint_agent
)

# Streamlit app interface
st.title('Smart City Sustainability Analysis')

# Predefined surprising city design concepts
design_concepts = [
    "A city where all buildings are floating on water to adapt to rising sea levels.",
    "An underground city designed to use geothermal energy and protect inhabitants from extreme weather conditions.",
    "A vertical city with skyscrapers connected by sky bridges and public transportation via drones.",
    "A city built within a gigantic biodome that regulates climate and ensures a perfect environment.",
    "A fully mobile city with buildings on wheels, allowing it to move to different areas as seasons change."
]

def generate_city_design():
    # Randomly choose a surprising design concept
    return random.choice(design_concepts)


# User inputs for the analysis
design_input = st.text_area("Enter your urban design concept:")

if st.button('Analyze Design'):
    if design_input:
        # Assemble the crew with the defined tasks
        sustainability_crew = Crew(
            agents=[environmental_agent, human_traffic_carbon_footprint_agent],
            tasks=[assess_sustainability_task, mitigate_traffic_carbon_footprint_task],
            verbose=True
        )

        # Run the crew to analyze the design
        result = sustainability_crew.kickoff()

        # Display the analysis results (this is a placeholder, adjust based on your actual result structure)
        st.subheader('Analysis Results')
        st.write(result)  # Make sure to format the result in a user-friendly way
    else:
        st.write("Please enter a design concept to analyze.")   

# Define the Sensor class
class Sensor:
    def __init__(self, location):
        self.location = location
        self.carbon_levels = []

    def detect_carbon(self, carbon_emission):
        self.carbon_levels.append(carbon_emission)

    def get_average_carbon(self):
        return sum(self.carbon_levels) / len(self.carbon_levels) if self.carbon_levels else 0

# Define the AIAgent class
class AIAgent:
    def analyze_data(self, sensor):
        average_carbon = sensor.get_average_carbon()
        return average_carbon, self.make_decision(average_carbon)

    def make_decision(self, average_carbon):
        if average_carbon > 50:
            return "Implement carbon reduction measures."
        else:
            return "Maintain current city operations."

# Define the SmartCity class
class SmartCity:
    def __init__(self, sensors, agent):
        self.sensors = sensors
        self.agent = agent

    def simulate_human_traffic(self):
        for sensor in self.sensors:
            carbon_emission = random.uniform(10, 100)
            sensor.detect_carbon(carbon_emission)

    def run_simulation(self):
        self.simulate_human_traffic()
        results = {}
        for sensor in self.sensors:
            avg_carbon, decision = self.agent.analyze_data(sensor)
            results[sensor.location] = (avg_carbon, decision)
        return results

# Streamlit app starts here
st.title('Smart City Carbon Emission Simulation')

# Number of sensors input
number_of_sensors = st.sidebar.number_input('Number of Sensors', min_value=1, value=3)

# Create sensors and an AI agent
sensors = [Sensor(location=f"Location {i+1}") for i in range(number_of_sensors)]
agent = AIAgent()

# Initialize the Smart City
smart_city = SmartCity(sensors, agent)

if st.button('Run Daily Simulation'):
    results = smart_city.run_simulation()
    for location, (avg_carbon, decision) in results.items():
        st.write(f"**{location}** - Average Carbon Emission: {avg_carbon:.2f}")
        st.write(f"Decision: {decision}")
else:
    st.write("Click the button to run the simulation for a day.")

def get_sensor_data(traffic_density):
    # This function should be replaced with real sensor data acquisition logic
    return {'traffic_density': traffic_density}

# Placeholder function to simulate analyzing sensor data
def analyze_sensor_data(data):
    # This function should be replaced with real data analysis logic
    return {'groundedness': 0.9, 'hallucination': 0.1}

# Placeholder function to simulate running simulations
def run_simulation(parameters):
    # This function should be replaced with real simulation logic
    return {'simulation_result': 'Reduced traffic congestion by 30%'}

# Placeholder function to simulate creating eBooks
def create_ebook(ideas):
    # This function should be replaced with real eBook creation logic
    return 'eBook with innovative urban planning ideas generated.'

# Streamlit application
st.title('Smart City Design Tool')

# Sensor data simulation inputs
st.header('Sensor Data Input')
traffic_density = st.slider('Select Traffic Density', min_value=0, max_value=100, value=50)

# When the user clicks the 'Analyze Data' button, run the analysis and display the results
if st.button('Analyze Data'):
    # Simulate getting sensor data based on inputs
    sensor_data = get_sensor_data(traffic_density)
    
    # Execute the crew to analyze the design and generate solutions
    analysis_results = analyze_sensor_data(sensor_data)
    hin = analysis_results['groundedness'] * analysis_results['hallucination']
    simulation_parameters = {'traffic_density': sensor_data['traffic_density']}
    simulation_result = run_simulation(simulation_parameters)
    new_ideas = ['Urban Green Spaces', 'Renewable Energy Sources']
    ebook = create_ebook(new_ideas)
    
    # Display the results
    st.write(f"HIN: {hin}")
    st.write(f"Simulation Result: {simulation_result['simulation_result']}")
    st.write(f"eBook Content: {ebook}")



# Simulate sensor data
def simulate_sensor_data(sensor_type, num_entries=100):
    timestamps = [datetime.now() - timedelta(minutes=15 * i) for i in range(num_entries)]
    if sensor_type == "temperature":
        data = np.random.normal(loc=25, scale=5, size=num_entries)  # Average temp with some variation
    elif sensor_type == "humidity":
        data = np.random.uniform(low=30, high=70, size=num_entries)  # Random humidity levels
    elif sensor_type == "energy":
        data = np.random.uniform(low=100, high=500, size=num_entries)  # Energy usage in kWh
    elif sensor_type == "water":
        data = np.random.uniform(low=100, high=1000, size=num_entries)  # Water usage in gallons
    else:
        data = np.random.rand(num_entries)  # Generic data for other sensors

    return pd.DataFrame({"Timestamp": timestamps, "Value": data})

# AI analysis (simple linear regression for demonstration)
def ai_analysis(df):
    df["Timestamp_ordinal"] = df["Timestamp"].map(datetime.toordinal)
    X = df[["Timestamp_ordinal"]]
    y = df["Value"]
    model = LinearRegression()
    model.fit(X, y)
    return model.predict(X)

# Main app function
def main():
    st.title("Custom Green City Builder")

    # Sidebar for sensor selection
    sensor_type = st.sidebar.selectbox("Select Sensor Type", ["temperature", "humidity", "energy", "water", "other"])

    # Simulate and display sensor data
    data = simulate_sensor_data(sensor_type)
    st.write(f"Simulated Data for {sensor_type.capitalize()} Sensor")
    st.line_chart(data.set_index("Timestamp"))

    # AI analysis and prediction
    if st.button("Run AI Analysis"):
        predictions = ai_analysis(data)
        data["Predictions"] = predictions
        fig = px.line(data, x="Timestamp", y=["Value", "Predictions"], labels={"value": "Sensor Readings", "variable": "Type"})
        st.plotly_chart(fig)

if __name__ == "__main__":
    main()