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README.md CHANGED
@@ -8,11 +8,86 @@ sdk_version: 1.27.2
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  app_file: app.py
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  pinned: false
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  ---
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- **Description:**
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- An ***e-commerce chatBot*** which goes through the Amazon dataset products and suggests the most suitable goods according to the user needs.
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- By utilizing the power of product embeddings and large language models exploiting Langchain and Redis technologies alongside the open source sentence-transformer embedding model and Falcon LLM, this chatbot acts as a real salesperson, can understand the client's request and efficiently search for relevant product recommendations based on the user description and present them in an engaging and informative manner.
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- **link to download the Amazon product dataset** : https://drive.google.com/file/d/1tHWB6u3yQCuAgOYc-DxtZ8Mru3uV5_lj/view
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  <img
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- src="./display.png"
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  style="display: inline-block;margin: 0 auto ; max-width:400px">
 
 
 
 
 
 
 
 
 
 
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  app_file: app.py
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  pinned: false
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  ---
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+ # Shopping Buddy
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+
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+ ## Overview:
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+
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+ 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 that processes Amazon products dataset 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.
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+ More details about the project are provided in [this blog post](insertalink.com).
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+
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+ ## Table of Contents
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+
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+ - [Shopping Buddy](#shopping-buddy)
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+ * [Overview](#overview)
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+ * [Installation & Setup](#installation--setup)
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+ + [1. Clone the Repository](#1-clone-the-repository)
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+ + [2. Install Dependencies](#2-install-dependencies)
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+ + [3. Environment Variables](#3-environment-variables)
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+ + [4. Data Preprocessing](#4-data-preprocessing)
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+ * [Running the Application](#running-the-application)
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+ * [Contributing](#contributing)
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+
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+
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+ ## Installation & Setup
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+
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+ ### 1. Clone the Repository
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+
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+ ```bash
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+ git clone git@github.com:romaissaMe/shopping-buddy.git
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+ cd shopping-buddy
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+ ```
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+
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+ ### 2. Install Dependencies
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+
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+ ```bash
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+ pip install -r requirements.txt
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+ ```
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+
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+ ### 3. Environment Variables
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+
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+ Set up your environment variables. This project uses the `dotenv` library to manage environment variables. Create a `.env` file in the root directory:
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+ ```bash
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+ cp .env_example .env
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+ ```
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+
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+ and add the following variables:
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+
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+ ```bash
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+ HUGGINGFACEHUB_API_TOKEN=your_huggingface_api_token
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+ OPENAI_API_KEY=your_openai_api_key
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+ REDIS_HOST=your_redis_host
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+ REDIS_PORT=your_redis_port
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+ REDIS_KEY=your_redis_key
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+ ```
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+
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+ ### 4. Data Preprocessing
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+
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+ Before running the main application, preprocess and import your data into a database using:
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+
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+ ```bash
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+ python preprocess.py
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+ ```
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+ [data_link](https://drive.google.com/file/d/1tHWB6u3yQCuAgOYc-DxtZ8Mru3uV5_lj/view)
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+
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+ ## Running the Application
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+
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+ Once the setup is complete, you can run the main application using:
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+
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+ ```bash
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+ streamlit run app.py
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+ ```
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+
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+ This will launch the Streamlit application, and you can access the chatbot via the provided URL.
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  <img
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+ src="./ShoppingBudddy.png"
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  style="display: inline-block;margin: 0 auto ; max-width:400px">
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+
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+ ## Contributing
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+
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+ If you're looking to contribute to this project, kindly follow the standard GitHub workflow:
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+
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+ 1. Fork the repository.
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+ 2. Create a new branch for your feature or fix.
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+ 3. Commit your changes and open a pull request.
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+ 4. Ensure that your code adheres to the project's style and standards.
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