Add D2-property-rental-assistant
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# AI Property Rental Assistant
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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.
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## Features
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- **Web Scraping**: Automatically fetches property listings from OnTheMarket
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- **AI-Powered Analysis**: Uses DeepSeek R1 model via Ollama for intelligent recommendations
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- **Personalized Recommendations**: Filters and ranks properties based on:
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- Budget constraints
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- Number of bedrooms
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- Tenant type (student, family, professional)
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- Location preferences
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- **Clean Output**: Returns formatted markdown with top 3-5 property recommendations
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- **Smart Filtering**: Handles cases where no suitable properties are found with helpful suggestions
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## Prerequisites
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- Python 3.7+
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- Ollama installed and running locally
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- DeepSeek R1 14B model pulled in Ollama
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## Installation
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1. **Clone the repository**
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```bash
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git clone <your-repo-url>
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cd property-rental-assistant
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```
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2. **Install required Python packages**
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```bash
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pip install requests beautifulsoup4 ollama ipython jupyter
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```
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3. **Install and setup Ollama**
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```bash
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# Install Ollama (macOS/Linux)
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curl -fsSL https://ollama.ai/install.sh | sh
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# For Windows, download from: https://ollama.ai/download
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```
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4. **Pull the DeepSeek R1 model**
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```bash
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ollama pull deepseek-r1:14b
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```
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5. **Start Ollama server**
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```bash
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ollama serve
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```
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## Usage
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### Running the Notebook
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1. **Start Jupyter Notebook**
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```bash
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jupyter notebook
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```
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2. **Open the notebook**
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Navigate to `property_rental_assistant.ipynb` in the Jupyter interface
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3. **Run all cells**
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Click `Cell` → `Run All` or use `Shift + Enter` to run cells individually
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### Customizing Search Parameters
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Modify the `user_needs` variable in the notebook:
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```python
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user_needs = "I'm a student looking for a 2-bedroom house in Durham under £2,000/month"
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```
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Other examples:
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- `"Family of 4 looking for 3-bedroom house with garden in Durham, budget £2,500/month"`
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- `"Professional couple seeking modern 1-bed apartment near city center, max £1,500/month"`
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- `"Student group needs 4-bedroom house near Durham University, £600/month per person"`
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### Changing the Property Website
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Update the `website_url` variable in the notebook:
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```python
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website_url = "https://www.onthemarket.com/to-rent/property/durham/"
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```
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## Architecture
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```
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┌─────────────────┐ ┌──────────────┐ ┌─────────────┐
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│ OnTheMarket │────▶│ Web Scraper │────▶│ Ollama │
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│ Website │ │ (BeautifulSoup)│ │ (DeepSeek R1)│
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└─────────────────┘ └──────────────┘ └─────────────┘
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│
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▼
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┌─────────────────────────────────┐
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│ AI-Generated Recommendations │
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│ • Top 5 matching properties │
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│ • Filtered by requirements │
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│ • Markdown formatted output │
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└─────────────────────────────────┘
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```
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## Project Structure
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```
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property-rental-assistant/
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│
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├── property_rental_assistant.ipynb # Main Jupyter notebook
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└── README.md # This file
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```
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## 🔧 Configuration
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### Ollama API Settings
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```python
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OLLAMA_API = "http://localhost:11434/api/chat" # Default Ollama endpoint
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MODEL = "deepseek-r1:14b" # Model to use
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```
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### Web Scraping Settings
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```python
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
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}
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timeout = 10 # Request timeout in seconds
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```
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### Content Limits
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```python
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website.text[:4000] # Truncate content to 4000 chars for token limits
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```
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## How It Works
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1. **Web Scraping**: The `Website` class fetches and parses HTML content from the property listing URL
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2. **Content Cleaning**: Removes scripts, styles, and images to extract clean text
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3. **Prompt Engineering**: Combines system prompt with user requirements and scraped data
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4. **LLM Analysis**: Sends the prompt to DeepSeek R1 via Ollama API
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5. **Recommendation Generation**: The AI analyzes listings and returns top matches in markdown format
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## 🛠️ Troubleshooting
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### Ollama Connection Error
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```
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Error communicating with Ollama: [Errno 111] Connection refused
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```
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**Solution**: Ensure Ollama is running with `ollama serve`
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### Model Not Found
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```
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Error: model 'deepseek-r1:14b' not found
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```
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**Solution**: Pull the model with `ollama pull deepseek-r1:14b`
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### Web Scraping Blocked
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```
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Error fetching website: 403 Forbidden
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```
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**Solution**: The website may be blocking automated requests. Try:
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- Updating the User-Agent string
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- Adding delays between requests
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- Using a proxy or VPN
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### Insufficient Property Data
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If recommendations are poor quality, the scraper may not be capturing listing details properly. Check:
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- The website structure hasn't changed
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- The content truncation limit (4000 chars) isn't too restrictive
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## Future Enhancements
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- [ ] Support multiple property websites (Rightmove, Zoopla, SpareRoom)
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- [ ] Interactive CLI for dynamic user input
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- [ ] Property image analysis
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- [ ] Save search history and favorite properties
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- [ ] Email notifications for new matching properties
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- [ ] Price trend analysis
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- [ ] Commute time calculations to specified locations
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- [ ] Multi-language support
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- [ ] Web interface with Flask/FastAPI
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- [ ] Docker containerization
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## Acknowledgments
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- [Ollama](https://ollama.ai/) for local LLM hosting
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- [DeepSeek](https://www.deepseek.com/) for the R1 model
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- [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/) for web scraping
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- [OnTheMarket](https://www.onthemarket.com/) for property data
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