app file

app.py

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+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()