Add career pivot navigator skeleton - #2
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Summary of ChangesHello @chanelle, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request establishes the foundational skeleton for a "career pivot navigator" application. It introduces a new module containing the core components necessary to prompt users for their profiles, perform a preliminary analysis (currently a placeholder for future LLM integration), load a predefined career map, and generate a basic career plan. The changes lay the groundwork for a more sophisticated tool aimed at guiding users through potential career transitions. Highlights
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Code Review
This pull request introduces a solid foundation for the career pivot navigator. The code is well-structured into separate modules, which is great for maintainability. I've provided a few suggestions to improve type hinting, adopt more modern Python idioms, and enhance the overall structure for better testability. These changes should help improve the clarity and robustness of the code.
| from typing import Dict | ||
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| def analyze_profile(prompt: str) -> Dict: |
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The return type hint Dict is too generic. It's better to be more specific about the dictionary's structure. Based on the implementation, Dict[str, str] would be a more accurate and descriptive type hint. This improves code clarity and allows static type checkers to be more effective.
| def analyze_profile(prompt: str) -> Dict: | |
| def analyze_profile(prompt: str) -> Dict[str, str]: |
| from plan_generator import generate_plan | ||
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| def load_career_map(path: str | None = None): |
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The type hint str | None is used here, which is available in Python 3.10+. However, other files in this project use types from the typing module (e.g., Dict in analyze.py). For consistency and to ensure compatibility with a wider range of Python versions (pre-3.10), it's recommended to use Optional[str] from the typing module.
To apply this change, you'll first need to add from typing import Optional to the import block at the top of the file.
| def load_career_map(path: str | None = None): | |
| def load_career_map(path: Optional[str] = None): |
| def main(): | ||
| user_profile = input("Describe your current role and skills: ") | ||
| prompt = get_user_profile_prompt(user_profile) | ||
| analysis = analyze_profile(prompt) | ||
| career_map = load_career_map() | ||
| plan = generate_plan(analysis, career_map) | ||
| print(json.dumps(plan, indent=2)) |
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The main function currently mixes I/O operations (input and print) with the core application logic. For better testability and modularity, it's a good practice to separate these concerns. Consider refactoring the core logic into a separate function that can be easily tested without mocking standard input/output. For example:
def create_plan(user_profile_str: str) -> dict:
"""Generates a career pivot plan for a given user profile."""
prompt = get_user_profile_prompt(user_profile_str)
analysis = analyze_profile(prompt)
career_map = load_career_map()
return generate_plan(analysis, career_map)
def main():
user_profile = input("Describe your current role and skills: ")
plan = create_plan(user_profile)
print(json.dumps(plan, indent=2))| return ( | ||
| "Analyze the following user profile for career pivot opportunities:\n" + profile | ||
| ) |
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For string formatting, f-strings are generally preferred over string concatenation with + as they are more readable and often faster.
return f"Analyze the following user profile for career pivot opportunities:\n{profile}"1 participant
Summary
career_pivot_navigatorfolder with initial implementationmain.py,prompts.py,analyze.py,plan_generator.pycareer_map.jsonTesting
python3 career_pivot_navigator/main.py <<EOF I am a software engineer with expertise in backend development. EOFhttps://chatgpt.com/codex/tasks/task_e_686de45d44d48327bc2e735fbb82b849