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Hairify is an innovative AI-powered chat assistant dedicated to guiding individuals in improving their hair health. The Hairify website boasts a user-friendly UI tailored to help users find solutions for their hair health concerns.

• AI chat assistant to give solutions to your hair problems
• analyze the picture of your scalp condition and detect hairfall stage
• give you suggestion to tackle your hair loss buy suggesting medication

Hairify application runs on 2 server

• Frontend
• Backend

• Frontend
  • Next JS
  • Tailwind
• Backend
  • Django
  • Gemini (Gen AI)
  • SSE
  • Langchain
• Tools
  • Vercel
  • Render
  • Postgress
  • Cloudinary
  • Langchain

• At first we head to landing page. (https://hairify-ai.vercel.app/)
• Then we go to login -> signup. We create a new accout. After that, we login with it. (https://hairify-ai.vercel.app/login) (https://hairify-ai.vercel.app/signup)
• After, we can chat with AI for hair and scalp related issues. (https://hairify-ai.vercel.app/chat)
• Also, from landing page we can go to Image Report generation. We need to upload the image of our scalp/hairline. It will generate a report. (https://hairify-ai.vercel.app/report)
• In Dashboard, we can see previous report histories that we have generated before. (https://hairify-ai.vercel.app/dashboard)

- Open Terminal
- Naviagate to your directory and navigate to the frontend folder
- Install Node if not present in your machine
- Do `npm install`
- Create a .env file and copy paste the content of .env.sample
- Do `npm run dev` to start the project

- Create a Cloudinary Account and get the creditnals
- Host Postgres locally or on Cloud, you can also use a docker Image
- Create a Gemini Account and get the API Key
- Install Python and Pip if not present
- Do `pip install virtualenv`
- Navigate to backend folder
- Setup virtualenv in your backend folder by typing `virtualenv env` in your terminal
- Activate Virtualenv by doing `.\env\Scripts\activate` (for windows) or `source env/bin/activate` (for linux)
- Do `pip install -r requirements.txt`
- Create a .env file and add all the variables of .env.sample file with relevant keys
- Do `cd hairify` in backend
- Finally do `python manage.py runserver ` to start the server