LLM_Engineering_OLD/week1/community-contributions/D2-property-rental-assistant

AI Property Rental Assistant

An intelligent property rental assistant Jupyter notebook that scrapes real estate listings from OnTheMarket and uses a local LLM (DeepSeek R1) to analyze and recommend properties based on user requirements.

Features

• Web Scraping: Automatically fetches property listings from OnTheMarket
• AI-Powered Analysis: Uses DeepSeek R1 model via Ollama for intelligent recommendations
• Personalized Recommendations: Filters and ranks properties based on:
  • Budget constraints
  • Number of bedrooms
  • Tenant type (student, family, professional)
  • Location preferences
• Clean Output: Returns formatted markdown with top 3-5 property recommendations
• Smart Filtering: Handles cases where no suitable properties are found with helpful suggestions

Prerequisites

• Python 3.7+
• Ollama installed and running locally
• DeepSeek R1 14B model pulled in Ollama

Installation

1. Clone the repository

git clone <your-repo-url>
cd property-rental-assistant

2. Install required Python packages

pip install requests beautifulsoup4 ollama ipython jupyter

3. Install and setup Ollama

# Install Ollama (macOS/Linux)
curl -fsSL https://ollama.ai/install.sh | sh

# For Windows, download from: https://ollama.ai/download

4. Pull the DeepSeek R1 model

ollama pull deepseek-r1:14b

5. Start Ollama server

ollama serve

Usage

Running the Notebook

1. Start Jupyter Notebook

jupyter notebook

2. Open the notebook Navigate to property_rental_assistant.ipynb in the Jupyter interface

3. Run all cells Click Cell → Run All or use Shift + Enter to run cells individually

Customizing Search Parameters

Modify the user_needs variable in the notebook:

user_needs = "I'm a student looking for a 2-bedroom house in Durham under £2,000/month"

Other examples:

• "Family of 4 looking for 3-bedroom house with garden in Durham, budget £2,500/month"
• "Professional couple seeking modern 1-bed apartment near city center, max £1,500/month"
• "Student group needs 4-bedroom house near Durham University, £600/month per person"

Changing the Property Website

Update the website_url variable in the notebook:

website_url = "https://www.onthemarket.com/to-rent/property/durham/"

Architecture

┌─────────────────┐     ┌──────────────┐     ┌─────────────┐
│   OnTheMarket   │────▶│  Web Scraper │────▶│   Ollama    │
│     Website     │     │  (BeautifulSoup)│    │ (DeepSeek R1)│
└─────────────────┘     └──────────────┘     └─────────────┘
                                                      │
                                                      ▼
                              ┌─────────────────────────────────┐
                              │   AI-Generated Recommendations  │
                              │   • Top 5 matching properties   │
                              │   • Filtered by requirements    │
                              │   • Markdown formatted output   │
                              └─────────────────────────────────┘

Project Structure

property-rental-assistant/
│
├── property_rental_assistant.ipynb  # Main Jupyter notebook
└── README.md                         # This file

🔧 Configuration

Ollama API Settings

OLLAMA_API = "http://localhost:11434/api/chat"  # Default Ollama endpoint
MODEL = "deepseek-r1:14b"                        # Model to use

Web Scraping Settings

headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
}
timeout = 10  # Request timeout in seconds

Content Limits

website.text[:4000]  # Truncate content to 4000 chars for token limits

How It Works

1. Web Scraping: The Website class fetches and parses HTML content from the property listing URL
2. Content Cleaning: Removes scripts, styles, and images to extract clean text
3. Prompt Engineering: Combines system prompt with user requirements and scraped data
4. LLM Analysis: Sends the prompt to DeepSeek R1 via Ollama API
5. Recommendation Generation: The AI analyzes listings and returns top matches in markdown format

🛠️ Troubleshooting

Ollama Connection Error

Error communicating with Ollama: [Errno 111] Connection refused

Solution: Ensure Ollama is running with ollama serve

Model Not Found

Error: model 'deepseek-r1:14b' not found

Solution: Pull the model with ollama pull deepseek-r1:14b

Web Scraping Blocked

Error fetching website: 403 Forbidden

Solution: The website may be blocking automated requests. Try:

• Updating the User-Agent string
• Adding delays between requests
• Using a proxy or VPN

Insufficient Property Data

If recommendations are poor quality, the scraper may not be capturing listing details properly. Check:

• The website structure hasn't changed
• The content truncation limit (4000 chars) isn't too restrictive

Future Enhancements

• Support multiple property websites (Rightmove, Zoopla, SpareRoom)
• Interactive CLI for dynamic user input
• Property image analysis
• Save search history and favorite properties
• Email notifications for new matching properties
• Price trend analysis
• Commute time calculations to specified locations
• Multi-language support
• Web interface with Flask/FastAPI
• Docker containerization

Acknowledgments

• Ollama for local LLM hosting
• DeepSeek for the R1 model
• BeautifulSoup for web scraping
• OnTheMarket for property data