friendlly 0.2.1

Your friendlly ai assistant

FriendLLy

Install

pip install friendlly

Front-end support

Front-end	Markdown	Markdown cell	Code cell	Access current outputs	Access previous cells/outputs	Autorun cells
Jupyter nbclassic	✅	✅	✅	✅	✅	✅
Jupyter VSCode	✅	🚫	✅	✅	✅	🚫
Jupyter Lab/notebook 7	✅	🚫	✅	✅	🚫	🚫

⚠️ FriendLLy works best in Jupyter nbclassic, and is useable in VSCode. Jupyter lab/notebook 7 support is somewhat limited for now.

You can install jupyter nbclassic alongside jupyter notebook or juter lab without causing any conflicts:

pip install nbclassic

And run it with

jupyter nbclassic

How to use

import os
# os.environ['ANTHROPIC_API_KEY'] = 'your-very-secret-anthropic-api-key'

# Or even better, use dotenv instead:
# (assuming you have an .env file with 'ANTHROPIC_API_KEY=your-very-secret-anthropic-api-key' in it)
import dotenv; dotenv.load_dotenv("../.env");

---

%load_ext friendlly

# %%fr
# Download an image of a cat, resize to 250 on long side, then very briefly describe it in the style of Jeremy Howard

Certainly! I’ll download an image of a cat, resize it, and then describe it briefly in Jeremy Howard’s style. Let’s break this down into steps:

🧠

1. We’ll use the requests library to download an image.
2. We’ll use PIL (Python Imaging Library) to process and resize the image.
3. After resizing, we’ll display the image.
4. Then, I’ll provide a brief description in Jeremy Howard’s style.

# %%fr +2 reply
import requests
from PIL import Image
from io import BytesIO

# Download an image of a cat
url = "https://cataas.com/cat"
response = requests.get(url)
img = Image.open(BytesIO(response.content))

# Resize the image
max_size = 250
ratio = max_size / max(img.size)
new_size = tuple([int(x*ratio) for x in img.size])
resized_img = img.resize(new_size, Image.LANCZOS)

# Display the resized image
resized_img

Alright, let’s describe this image in Jeremy Howard’s style:

“Right, so we’ve got this absolutely cracking image of a cat here. It’s a proper chonker, sitting on what looks like a blue chair or sofa. The cat’s got this brilliant tabby pattern - you know, those classic stripes - and a white belly. And get this, it’s doing that classic cat thing where it’s squinting its eyes, looking all content and smug. It’s like it’s saying, ‘Yeah, I own this place.’ Brilliant stuff, really. This is exactly the kind of image that would make for a fantastic dataset in computer vision tasks. You could use it for all sorts of things - cat breed classification, pose estimation, you name it. Absolutely fascinating, isn’t it?”

Looking at existing cells

from PIL import Image
Image.open("cat2.jpg")

# %%fr +1
# Describe the image

The image shows a cat sitting upright on what appears to be a light-colored surface or floor. The cat has distinctive features typical of an Abyssinian breed:

1. Slender, athletic build
2. Large, alert ears
3. Almond-shaped eyes
4. Reddish-brown or ruddy coat color
5. Ticked fur pattern, giving it a wild appearance

The cat is sitting in a poised, attentive posture with its tail wrapped neatly around its body. Its gaze is directed off to the side, giving it an alert and curious expression. The background appears to be a plain white or light-colored wall, which contrasts nicely with the cat’s warm-toned coat.

This image showcases the elegant and regal appearance that Abyssinian cats are known for, highlighting their unique coat color and sleek physique.

Messing around

You might have noticed the reply added to the magic of the generated cells. This is the only way FriendLLy distinguishes between user- and assistant-generated cells (plus some heuristics for Markdown cells). You can add such cells yourself, and FriendLLy will think they are assistant-generated.