Update README

README.md

@@ -7,4 +7,58 @@ app_file: main.py
 sdk: gradio
 sdk_version: 4.16.0
 pinned: true
+license: mit
 ---
+
+# Fitness Q&A
+This project is a simple question-answering (Q&A) bot focused on fitness-related queries. It utilized a combination of machine learning models and retrieval techniques to provide informative responses to user questions.
+
+Check out the chatbot here: https://huggingface.co/spaces/lstetson/Fitness_QA_Bot
+
+The main objective is to assist users in obtaining relevant information about fitness and recovery topics. The bot accepts user questions as inputs and returns answers along with a link to Dr. Andrew Huberman's videos for further context. The videos come from his [Fitness and Recovery playlist](https://www.youtube.com/playlist?list=PLPNW_gerXa4O24l7ZHoJbMC2xOO7SpS7K).
+
+## Usage
+To run the project, you need to have Python installed on your system along with the required dependencies specified in ``requirements.txt``.
+
+```bash
+pip install -r requirements.txt
+```
+
+After installing the dependencies, you need to set up your OpenAI API Key. You can sign up for an API Key at [OpenAI's website](https://openai.com/). Once you have your API Key, you should set it as an environment variable named ``OPENAI_API_KEY``.
+```bash
+export OPENAI_API_KEY='your-api-key'
+```
+
+You can now run the `main.py` file. This will launch a Gradio interface where you can interact with the system.
+
+## Data Extraction, Transformation, and Loading (ETL)
+
+In addition to the main functionality provided by `main.py`, this project includes a script for Data Extraction, Transformation, and Loading (ETL). This script, `run_etl.py`, allows you to extract metadata (YouTube ids and video titles) from a JSON file, extract data, transform it, and load it into a database.
+
+### Usage
+
+To use the ETL script, follow these steps:
+
+1. Navigate to the root directory of the project in your terminal.
+2. Run the `run_etl.py` script using Python:
+
+```bash
+python run_etl.py
+```
+You will be prompted to provide the following information:
+
+1. Path to the JSON file containing the data.
+2. Path to the database where you want to store the transformed data.
+3. Batch size for processing the data (leave blank for no batching).
+4. Batch Overlap (leave blank for no overlap).
+
+Here's an example:
+
+Enter the path to the JSON file: **data/input_data.json**  
+Enter the path to the database: **data/output_database.db**  
+Enter batch size (leave blank for no batching): **10**  
+Enter overlap (leave blank for no overlap): **2**  
+
+## Acknowledgements
+* Dr. Andrew Huberman - For his informative videos on health-related topics.
+* [The Full Stack](https://github.com/the-full-stack/) - For inspiring this project and providing helpful resources.