Aluminium Industry Data Processing and Streamlit App

Overview

This project involves extracting relevant data from the aluminium industry, converting the data to vector embeddings, and deploying a Streamlit application to visualize the embeddings.

Steps

Step 1: Extract Data

• Objective: Extract relevant data from the aluminium industry.
• Data Fields:
  • Title: The title of the data entry.
  • Summary: A brief summary of the data entry.
  • Date: The date of the data entry.

Step 2: Convert Data to Vector Embeddings

• Objective: Convert the extracted scrap data to vector embeddings.
• Method: Utilized text-embedding-ada-002 model to generate vector embeddings from the text data.

Step 3: Streamlit Application

• Objective: Create a Streamlit application that loads and displays the vector embeddings.
• Functionality:
  • Load vector embeddings.
  • Provide a user interface to interact with the data.
  • Visualize the embeddings effectively.

Step 4: Deployment

• Objective: Deploy the Streamlit application for public access.
• Deployment Details: The application is publicly accessible at https://actalystai.streamlit.app/.

Getting Started

Prerequisites

• Python 3.x
• Streamlit
• OpenAI API for text-embedding-ada-002

Installation

1. Clone the Repository:

   git clone [(https://github.com/Akashkalasagond/Actalyst_AI)]

2. Navigate to the Project Directory:

   cd [Actalyst_AI]

3. Install Dependencies:

   pip install -r requirements.txt

4. Deployment

   streamlit run app.py