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| title: EcomShoppingBuddy | |
| emoji: 😎 | |
| colorFrom: pink | |
| colorTo: red | |
| sdk: streamlit | |
| sdk_version: 1.27.2 | |
| app_file: app.py | |
| pinned: false | |
| # Shopping Buddy | |
| ## Overview: | |
| This project leverages [Falcon LLM](https://falconllm.tii.ae/), [OpenAI](openai.com) API, [Sentence Transformer](https://www.sbert.net/docs/installation.html#install-sentencetransformers), [Hugging Face Hub](https://huggingface.co/) and [Streamlit](https://docs.streamlit.io/) to build and deploy a chatbot shopping assistant application. It relies on a dataset of Amazon products to provide tailored product recommendations aligned with user's needs. The application uses [Langchain](https://www.langchain.com/) to integrate those diverse functionalities. Data preprocessing, embedding generation, and storage in Redis are also essential components of this project. | |
| More details about the project are provided in [this blog post](https://medium.com/@roumamedj/shopping-buddy-767458f752f9). | |
| You can test the application by visiting [Shopping Buddy](https://huggingface.co/spaces/RomyMy/EcomShoppingBuddy). | |
| ## Table of Contents | |
| - [Shopping Buddy](#shopping-buddy) | |
| * [Overview](#overview) | |
| * [Installation & Setup](#installation--setup) | |
| + [1. Clone the Repository](#1-clone-the-repository) | |
| + [2. Install Dependencies](#2-install-dependencies) | |
| + [3. Environment Variables](#3-environment-variables) | |
| + [4. Data Preprocessing](#4-data-preprocessing) | |
| * [Running the Application](#running-the-application) | |
| * [Contributing](#contributing) | |
| ## Installation & Setup | |
| ### 1. Clone the Repository | |
| ```bash | |
| git clone git@github.com:romaissaMe/shopping-buddy.git | |
| cd shopping-buddy | |
| ``` | |
| ### 2. Install Dependencies | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ### 3. Environment Variables | |
| Set up your environment variables. This project uses the `dotenv` library to manage environment variables. Create a `.env` file in the root directory: | |
| ```bash | |
| cp .env_example .env | |
| ``` | |
| and add the following variables: | |
| ```bash | |
| HUGGINGFACEHUB_API_TOKEN=your_huggingface_api_token | |
| OPENAI_API_KEY=your_openai_api_key | |
| REDIS_HOST=your_redis_host | |
| REDIS_PORT=your_redis_port | |
| REDIS_KEY=your_redis_key | |
| ``` | |
| ### 4. Data Preprocessing | |
| Before running the main application, preprocess and import your data into a database using: | |
| ```bash | |
| python preprocess.py | |
| ``` | |
| Download the data as csv file from [here](https://drive.google.com/file/d/1tHWB6u3yQCuAgOYc-DxtZ8Mru3uV5_lj/view) and name it 'product_data.csv'. | |
| This dataset comprises Amazon products information including item ID, item name, item keywords, product type... | |
| ## Running the Application | |
| Once the setup is complete, you can run the main application using: | |
| ```bash | |
| streamlit run app.py | |
| ``` | |
| This will launch the Streamlit application, and you can access the chatbot via the provided URL. | |
| <img | |
| src="./ShoppingBudddy.png" | |
| style="display: inline-block;margin: 0 auto ; max-width:400px"> | |
| ## Contributing | |
| If you're looking to contribute to this project, kindly follow the standard GitHub workflow: | |
| 1. Fork the repository. | |
| 2. Create a new branch for your feature or fix. | |
| 3. Commit your changes and open a pull request. | |
| 4. Ensure that your code adheres to the project's style and standards. | |