Real-Estate-Optimizer-swarm.py

import numpy as np
import pandas as pd
import requests
from abc import ABC, abstractmethod
import logging
import time
from dotenv import load_dotenv
import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BertModel, BertTokenizer
from llama_cpp import Llama

# Load environment variables
load_dotenv()

# Configure logging
logging.basicConfig(filename='real_estate_optimizer.log', level=logging.INFO,
                    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')

class Swarm(ABC):
    def __init__(self, name):
        self.name = name
        self.logger = logging.getLogger(name)

    @abstractmethod
    def run(self):
        pass

class LLMSwarm(Swarm):
    def __init__(self):
        super().__init__("LLM Swarm")
        self.llama_model = Llama(model_path=os.getenv('LLAMA_MODEL_PATH'))
        self.gpt_j_tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
        self.gpt_j_model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
        self.bert_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
        self.bert_model = BertModel.from_pretrained('bert-base-uncased')

    def run(self, input_text):
        self.logger.info("Running multi-model LLM analysis")
        
        # LLaMA analysis
        llama_output = self.llama_model(input_text, max_tokens=100)
        
        # GPT-J analysis
        gpt_j_input = self.gpt_j_tokenizer(input_text, return_tensors="pt")
        gpt_j_output = self.gpt_j_model.generate(**gpt_j_input, max_length=100)
        gpt_j_text = self.gpt_j_tokenizer.decode(gpt_j_output[0])
        
        # BERT analysis
        bert_input = self.bert_tokenizer(input_text, return_tensors="pt")
        bert_output = self.bert_model(**bert_input)
        
        return {
            "llama_analysis": llama_output,
            "gpt_j_analysis": gpt_j_text,
            "bert_embeddings": bert_output.last_hidden_state.mean(dim=1).detach().numpy()
        }

class DataAnalyticsSwarm(Swarm):
    def __init__(self):
        super().__init__("Data Analytics Swarm")
        self.data_points = 400

    def run(self):
        self.logger.info(f"Analyzing {self.data_points} data points")
        # Implement your data analytics logic here
        return {"analyzed_points": self.data_points}

class RealEstateAnalyticsSwarm(Swarm):
    def __init__(self):
        super().__init__("Real Estate Analytics Swarm")

    def run(self, data):
        self.logger.info("Performing specialized real estate analytics")
        # Implement your real estate analytics logic here
        return {"market_trends": "upward", "foreclosure_rate": 0.05}

class OptimizationSwarm(Swarm):
    def __init__(self):
        super().__init__("Optimization Swarm")
        self.particles = []

    def run(self):
        best_position, best_score = self.optimize_search(20, 100, 2)
        self.logger.info(f"Best position: {best_position}, Best score: {best_score}")
        return best_position

    def optimize_search(self, n_particles, n_iterations, dimensions):
        self.particles = [Particle(dimensions) for _ in range(n_particles)]
        global_best_position = np.copy(self.particles[0].position)
        global_best_score = float('inf')

        for _ in range(n_iterations):
            for particle in self.particles:
                score = self.objective_function(particle.position)
                if score < particle.best_score:
                    particle.best_score = score
                    particle.best_position = np.copy(particle.position)

                if score < global_best_score:
                    global_best_score = score
                    global_best_position = np.copy(particle.position)

            for particle in self.particles:
                particle.update_velocity(global_best_position)
                particle.update_position()

        return global_best_position, global_best_score

    def objective_function(self, position):
        location_score = position[0]
        affordability_score = 1 / (1 + np.exp(-position[1]))
        return location_score + affordability_score

class Particle:
    def __init__(self, dimensions):
        self.position = np.array([np.random.uniform(-10, 10) for _ in range(dimensions)])
        self.velocity = np.zeros(dimensions)
        self.best_position = np.copy(self.position)
        self.best_score = float('inf')

    def update_velocity(self, global_best_position, w=0.5, c1=1, c2=2):
        r1, r2 = np.random.random(2)
        cognitive_velocity = c1 * r1 * (self.best_position - self.position)
        social_velocity = c2 * r2 * (global_best_position - self.position)
        self.velocity = w * self.velocity + cognitive_velocity + social_velocity

    def update_position(self):
        self.position += self.velocity

class DataFetchSwarm(Swarm):
    def __init__(self):
        super().__init__("Data Fetch Swarm")
        self.api_key = os.getenv('API_KEY')
        self.base_url = "https://api.example.com/v1/properties"

    def run(self, params):
        return self.fetch_property_data(params)

    def fetch_property_data(self, params):
        headers = {'Authorization': f'Bearer {self.api_key}'}
        try:
            response = requests.get(self.base_url, headers=headers, params=params)
            response.raise_for_status()
            return response.json()
        except requests.RequestException as e:
            self.logger.error(f"Failed to fetch data: {e}")
            return None

class VisualizationSwarm(Swarm):
    def __init__(self):
        super().__init__("Visualization Swarm")

    def run(self, data):
        self.logger.info("Creating data visualizations")
        # Implement your visualization logic here
        return {"charts": ["trend_chart", "comparison_chart"]}

class RealEstateOptimizer:
    def __init__(self):
        self.llm_swarm = LLMSwarm()
        self.data_analytics_swarm = DataAnalyticsSwarm()
        self.real_estate_analytics_swarm = RealEstateAnalyticsSwarm()
        self.optimization_swarm = OptimizationSwarm()
        self.data_fetch_swarm = DataFetchSwarm()
        self.visualization_swarm = VisualizationSwarm()

    def run(self):
        logging.info("Starting Real Estate Optimizer")

        # Run data analytics
        analytics_results = self.data_analytics_swarm.run()

        # Perform real estate analytics
        re_analytics_results = self.real_estate_analytics_swarm.run(analytics_results)

        # LLM analysis
        llm_input = f"Analyze the real estate market trends: {re_analytics_results['market_trends']} with foreclosure rate: {re_analytics_results['foreclosure_rate']}"
        llm_results = self.llm_swarm.run(llm_input)

        # Optimize search parameters
        best_params = self.optimization_swarm.run()

        # Fetch property data
        params = {
            'location': best_params[0],
            'affordability_score': best_params[1],
            'property_type': 'multi-family',
            'income_level': 'low'
        }
        property_data = self.data_fetch_swarm.run(params)

        # Create visualizations
        if property_data:
            visualizations = self.visualization_swarm.run(property_data)
            logging.info(f"Created visualizations: {visualizations}")
        else:
            logging.warning("No property data available for visualization")

        logging.info("Real Estate Optimizer process completed")
        logging.info(f"LLM Analysis Results: {llm_results}")

if __name__ == "__main__":
    optimizer = RealEstateOptimizer()
    optimizer.run()