otovo_prep_exercises_generation.py

import openai
import os

openai.api_key = os.environ["OPENAI_API_KEY"]

def get_chatgpt_response(prompt_question):
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
                {"role": "system", "content": "You are a helpful coding and learning assistant."},
                {"role": "user", "content": prompt_question},
            ]
        )
        return response["choices"][0]["message"]["content"]





# For each of these lists create a .txt file entitled "otovo_prep_exercises_day1.txt" and so on
# Inside each of these files, write each of the strings in the list on a new line and submit 

if __name__=="__main__":
    day1_prep = [
    "Refresher on Python basics, advanced concepts, and Python libraries used in AI/ML.",
    "Practice Python exercises on platforms like HackerRank or LeetCode.",
    "Brush up SQL syntax and operations. Practice SQL queries on platforms like Mode Analytics SQL School.",
    "Review key AWS services such as S3, EC2, RDS, and SageMaker.",
    "Complete a hands-on lab on Qwiklabs or A Cloud Guru related to AWS.",
    "Learn about geospatial analysis via online courses, focus on GIS in Python.",
    "Work on a mini-project analyzing solar radiation data based on geolocation."
    ]

    day2_prep = [
        "Overview of AI and ML algorithms including supervised and unsupervised learning, reinforcement learning, regression, classification, and clustering.",
        "Practice evaluating model performance metrics (precision, recall, accuracy, F1 score).",
        "Learn basics of Langchain, OpenCV, and TensorFlow.",
        "Implement a simple OCR program using OpenCV and TensorFlow.",
        "Understand Docker and Kubernetes basics.",
        "Containerize a simple Python app using Docker and deploy it using Kubernetes.",
        "Learn about solar power generation and modeling via online resources.",
        "Using solar power installation data, create a predictive model for solar energy production."
    ]

    day3_prep = [
        "Understand principles of REST and GraphQL.",
        "Build a simple application that interacts with a public RESTful API and a public GraphQL API.",
        "Review basics of statistical analysis and optimization algorithms.",
        "Apply optimization algorithms to a dataset to find the best fit or solution.",
        "Learn about economic modeling via online resources, focusing on energy economics and pricing.",
        "Using energy price data and solar energy generation data, model the savings from installing a solar panel system.",
        "Understand the concept of a feature store and its role in ML workflows.",
        "Set up a simple feature store using tools like Feast.",
        "Use platforms like Pramp or Interviewing.io to simulate a technical interview."
    ]
    for actionable_task in day1_prep:
        prompt_question = f"Given this actionable task: {actionable_task}, create a comprehensive list of strategic exercises to prepare for it."
        response = get_chatgpt_response(prompt_question)
        with open(f"otovo_prep_exercises_day1.txt", "a") as f:
            f.write(response + "\n")
    for actionable_task in day2_prep:
        prompt_question = f"Given this actionable task: {actionable_task}, create a comprehensive list of strategic exercises to prepare for it."
        response = get_chatgpt_response(prompt_question)
        with open(f"otovo_prep_exercises_day2.txt", "a") as f:
            f.write(response + "\n")
    for actionable_task in day3_prep:
        prompt_question = f"Given this actionable task: {actionable_task}, create a comprehensive list of strategic exercises to prepare for it."
        response = get_chatgpt_response(prompt_question)
        with open(f"otovo_prep_exercises_day3.txt", "a") as f:
            f.write(response + "\n")