Run Locally

Step 1: Create a Firebase Project

1. Go to the Firebase Console.
2. Click on "Add Project."
3. Enter a name for your project and choose your preferred country/region.
4. Click "Create Project."

Step 2: Set up Firebase Authentication

1. In the Firebase Console, go to the "Authentication" section.
2. Enable the "Sign-in method" that you prefer (Email/Password, Google, etc.).
3. Follow the instructions to set up authentication methods.

Step 3: Get Firebase Configuration

1. In the Firebase Console, click on the gear icon in the left panel to go to "Project Settings."
2. In the "General" tab, scroll down to the "Your apps" section.
3. Click on the "</>" icon to add a web app to your project.
4. Register your app by providing a nickname (e.g., "MyApp").
5. Click "Register App."
6. Copy the generated configuration.

Step 4: Create a .env file

1. Create a new file in your project root directory and name it .env.
2. Open the .env file with a text editor.

Step 5: Add Firebase Configurations to .env

Paste the following lines into your .env file and replace the placeholders with the actual values from your Firebase project.

FIREBASE_API_KEY=your_api_key
FIREBASE_AUTH_DOMAIN=your_auth_domain
FIREBASE_PROJECT_ID=your_project_id
FIREBASE_STORAGE_BUCKET=your_storage_bucket
FIREBASE_MESSAGING_SENDER_ID=your_messaging_sender_id
FIREBASE_APP_ID=your_app_id
FIREBASE_MEASUREMENT_ID=your_measurement_id
FIREBASE_DATABASE_URL=your_database_url

Step 6: Save .env File

Save the changes to your .env file.

Now, your Firebase project is set up, and your credentials are securely stored in the .env file. Make sure to keep your .env file private and never expose it to the public.

Clone the project

  git clone https://github.com/meet447/Chipling-AI.git

Go to the project directory

  cd my-project

Install dependencies

  pip install -r requirements.txt 

Start the server

  python wsgi.py

Chipling-AI

Roadmap

• Create API Routes and Endpoints

• Add more Models

• Add Music Gen models

Available Models

Name	Type	Description
meta/llama-2-70b-chat	Text	A 70 billion parameter language model from Meta, fine tuned for chat completions
mistralai/mistral-7b-instruct-v0.1	Text	An instruction-tuned 7 billion parameter language model from Mistral
ai-forever/kandinsky-2.2	Image	multilingual text2image latent diffusion model
stability-ai/sdxl	Image	A text-to-image generative AI model that creates beautiful images
stability-ai/stable-diffusion	Image	A latent text-to-image diffusion model capable of generating photo-realistic images given any text input
lucataco/animate-diff	Video	Animate Your Personalized Text-to-Image Diffusion Models
anotherjesse/zeroscope-v2-xl	Video	Zeroscope V2 XL & 576w
fofr/latent-consistency-model	Image	Super-fast, 0.6s per image. LCM with img2img, large batching and canny controlnet
meta/codellama-13b	Text	A 13 billion parameter Llama tuned for code completion
anything/anythingv5	Image	Anything V5 is a popular choice among users for generating high-quality images from text prompts.
lykon/dreamshaper8	Image	One of the best models
lykon/absolutereality	Image	Genrate Realistic Images
stability-ai/stable-video-diffusion	Video	Animate your fav images to short vids

License

MIT

Tech Stack

Client: Html

Server: Python, Flask