level1 → part1 full code

🤖 First api call to Frontier Model

import os
from dotenv import load_dotenv
from IPython.display import Markdown, display
from openai import OpenAI

# Load environment variables from .env file
load_dotenv(override=True)
api_key = os.getenv('OPENAI_API_KEY')

#Validate the key
if not api_key:
print("No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!")
elif not api_key.startswith("sk-proj-"):
print("An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook")
elif api_key.strip() != api_key:
print("An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook")
else:
print("API key found and looks good so far!")

# To give you a preview -- calling OpenAI with these messages is this easy.

message = "Hello, GPT! This is my first ever message to you! Hi!"
messages = [{"role": "user", "content": message}]
messages

openai = OpenAI()

response = openai.chat.completions.create(model="gpt-5-nano", messages=messages)
response.choices[0].message.content

🧠 System and User Prompt

system_prompt = "You are a helpful assistant who explains complex topics in simple, beginner-friendly language."

user_prompt = "Can you explain what a Large Language Model is?"

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": user_prompt}
]

response = openai.chat.completions.create(model="gpt-4o-mini", messages=messages)
print(response.choices[0].message.content)

🌐 Website Summariser Project

from scraper import fetch_website_contents

# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish."

system_prompt = """
You are a snarky assistant that analyzes the contents of a website,
and provides a short, snarky, humorous summary, ignoring text that might be navigation related.
Respond in markdown. Do not wrap the markdown in a code block - respond just with the markdown.
"""

# Define our user prompt

user_prompt_prefix = """
Here are the contents of a website.
Provide a short summary of this website.
If it includes news or announcements, then summarize these too.

"""

def messages_for(website):
    return [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_prompt_prefix + website}
    ]

# And now: call the OpenAI API. You will get very familiar with this!

def summarize(url):
    website = fetch_website_contents(url)
    response = openai.chat.completions.create(
        model = "gpt-4.1-mini",
        messages = messages_for(website)
    )
    return response.choices[0].message.content

summarize("<https://codeveda.dev>")

👀 View the Raw Scraped Data to compare with LLM version

scrapedData = fetch_website_contents("<https://codeveda.dev>")
print(scrapedData)

🎨 Bonus — Display the Summary in a Nicer Format

# A function to display this nicely in the output, using markdown

def display_summary(url):
    summary = summarize(url)
    display(Markdown(summary))
    
display_summary("<https://codeveda.dev>")