# 2050_Materials_Models

## Overview

This repository, titled `2050_materials_models`, is a part of the 2050-materials platform. It contains various machine learning models developed to enhance the platform's capabilities.

## Models in this Repository

Currently, the repository hosts two models:

1. `prediction_model_material.pth`: A model for predicting the best match from a list of material types.
2. `prediction_model_product.pth`: A model used to predict the best match from a range of product types.

These models are built to streamline the process of identifying and categorizing different materials and products used in construction, assisting in making more environmentally informed choices.

## How to Use

### Requirements

The primary requirements include:

- PyTorch
- Joblib (if applicable)

### Loading the Models

You can load these models using PyTorch. Here's a quick snippet on how to do it:

```python
import torch

# Load the material prediction model
material_model_path = 'path/to/prediction_model_material.pth'
material_model = torch.load(material_model_path)

# Load the product prediction model
product_model_path = 'path/to/prediction_model_product.pth'
product_model = torch.load(product_model_path)

# Example of using the models for prediction