Create LASS #546
Create LASS #546
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A powerful engine that helps every student succeed. *coming future*
Summary of ChangesHello @lalitparihar-ui, 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 aims to introduce a new AI-powered study agent, "LSAA — Laggle Study AI Agent," intended to assist users with various educational tasks through integration with the Google Gemini API. The PR description provides a complete Python script outlining the agent's functionalities, such as generating study notes, solving doubts, creating MCQs, explaining topics, listing formulas, and generating quizzes. While the intent is clear, the actual file added in the repository, named "LASS", is currently empty, suggesting that the provided code needs to be incorporated into this file. Highlights
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Code Review
This pull request aims to create the 'Lagle Study AI Agent' (LSAA). The Python code for the agent is included in the pull request description, but it has not been committed to a file. The only file change in this PR is the creation of an empty file named LASS.
To proceed, please create a new Python file (e.g., lass_agent.py), paste the code from the description into it, and commit the file to this pull request.
Although I cannot add comments directly to the code lines because the code is not in a file, I have reviewed the script from the description and have the following feedback to help improve it:
Critical Security Issue:
- Hardcoded API Key: The line
API_KEY = "YOUR_API_KEY"is a major security risk. Committing API keys or other secrets directly into your source code can lead to them being exposed and misused. It's crucial to load secrets from a secure location.- Suggestion: Use environment variables or a secrets manager to handle your API key. For example, in a local environment, you can use
os.getenv(). In a Kaggle Notebook, you should use Kaggle's built-in secrets management.import os import google.generativeai as genai # Example using environment variables: # api_key = os.getenv("GEMINI_API_KEY") # genai.configure(api_key=api_key) # Example for Kaggle Notebooks: from kaggle_secrets import UserSecretsClient user_secrets = UserSecretsClient() api_key = user_secrets.get_secret("GEMINI_API_KEY") genai.configure(api_key=api_key)
- Suggestion: Use environment variables or a secrets manager to handle your API key. For example, in a local environment, you can use
High Priority Feedback:
- Missing Error Handling: The calls to
model.generate_content()do not handle potential errors. If the API call fails (e.g., due to network issues, an invalid key, or content filtering), the script will crash.- Suggestion: Wrap the API calls in a
try...exceptblock to gracefully handle exceptions and inform the user.def doubt_agent(question): # ... prompt setup ... try: response = model.generate_content(prompt) return response.text except Exception as e: print(f"An error occurred: {e}") return "Sorry, I was unable to process your request at this time."
- Suggestion: Wrap the API calls in a
Medium Priority - Code Refactoring:
- Repetitive Agent Functions: All
*_agentfunctions have the same structure: define a prompt string and call the model. This can be refactored into a single generic function to reduce code duplication and improve maintainability.- Suggestion: Create one function that takes the prompt as an argument.
def generate_response(prompt_text): """Generates a response from the Gemini model based on a prompt.""" try: response = model.generate_content(prompt_text) return response.text except Exception as e: print(f"Error generating content: {e}") return "Sorry, I couldn't process your request." # Then, the agent functions become simpler wrappers: def notes_agent(topic_text): prompt = f""" Convert the following chapter/paragraph into clean study notes. Use points, headings, definitions, examples: TEXT: {topic_text} """ return generate_response(prompt)
- Suggestion: Create one function that takes the prompt as an argument.
- Main Loop Logic: The
if/elif/elsechain in the main loop works, but using a dictionary to map user choices to functions and their corresponding input prompts can make the code cleaner and easier to extend.
Low Priority - Minor Improvements:
- Dependency Installation: The
!pip installcommand is specific to notebook environments (like Jupyter or Kaggle) and will cause an error if the code is run as a standard Python script. This instruction should be removed from the script and placed in arequirements.txtfile or aREADME.mdfor setup instructions. - Variable Naming: Some variable names like
chandqare very short. Using more descriptive names likechoiceandquestionwould improve readability, following PEP 8 guidelines.
2 participants
===============================================
LSAA — Laggle Study AI Agent
Complete Kaggle Notebook Code
===============================================
!pip install -q google-generativeai
import google.generativeai as genai
-----------------------------------------------
1. SETUP
-----------------------------------------------
API_KEY = "YOUR_API_KEY" # <-- यहां अपनी Gemini API key डालें
genai.configure(api_key=API_KEY)
model = genai.GenerativeModel("gemini-1.5-flash")
print("LSAA is Ready!")
-----------------------------------------------
2. MAIN AGENT FUNCTIONS
-----------------------------------------------
1️⃣ Notes Maker
def notes_agent(topic_text):
prompt = f"""
Convert the following chapter/paragraph into clean study notes.
Use points, headings, definitions, examples:
TEXT: {topic_text}
"""
response = model.generate_content(prompt)
return response.text
2️⃣ Doubt Solver
def doubt_agent(question):
prompt = f"""
Answer the following study doubt in a clear, simple way suitable for class 10:
DOUBT: {question}
"""
response = model.generate_content(prompt)
return response.text
3️⃣ MCQ Generator
def mcq_agent(topic):
prompt = f"""
Generate 5 MCQs with answers & explanations for:
TOPIC: {topic}
Format:
Q1)
A)
B)
C)
D)
Correct Answer:
Explanation:
"""
response = model.generate_content(prompt)
return response.text
4️⃣ Explanation Agent
def explain_agent(hard_topic):
prompt = f"""
Explain this topic in very simple class 10 level language:
TOPIC: {hard_topic}
Provide examples.
"""
response = model.generate_content(prompt)
return response.text
5️⃣ Formula Helper
def formula_agent(subject_topic):
prompt = f"""
List all important formulas for:
TOPIC: {subject_topic}
Write in simple format.
"""
response = model.generate_content(prompt)
return response.text
6️⃣ Chapter Quiz Agent
def quiz_agent(chapter):
prompt = f"""
Create a chapter-wise quiz for:
CHAPTER: {chapter}
Include:
- 5 MCQs
- 5 True/False
- 5 Fill in the blanks
"""
response = model.generate_content(prompt)
return response.text
-----------------------------------------------
3. SIMPLE MENU / UI
-----------------------------------------------
def menu():
print("\n==============================")
print(" LSAA — Laggle Study AI Agent ")
print("==============================")
print("1. Notes Maker")
print("2. Doubt Solver")
print("3. MCQ Generator")
print("4. Simple Explanation")
print("5. Formula Helper")
print("6. Chapter Quiz")
print("7. Exit")
print("==============================")
-----------------------------------------------
4. MAIN LOOP
-----------------------------------------------
while True:
ch = menu()
LSAA — Laggle Study AI Agent
Complete Kaggle Notebook Code
===============================================
!pip install -q google-generativeai
import google.generativeai as genai
-----------------------------------------------
1. SETUP
-----------------------------------------------
API_KEY = "YOUR_API_KEY" # <-- यहां अपनी Gemini API key डालें
genai.configure(api_key=API_KEY)
model = genai.GenerativeModel("gemini-1.5-flash")
print("LSAA is Ready!")
-----------------------------------------------
2. MAIN AGENT FUNCTIONS
-----------------------------------------------
1️⃣ Notes Maker
def notes_agent(topic_text):
prompt = f"""
Convert the following chapter/paragraph into clean study notes.
Use points, headings, definitions, examples:
TEXT: {topic_text}
"""
response = model.generate_content(prompt)
return response.text
2️⃣ Doubt Solver
def doubt_agent(question):
prompt = f"""
Answer the following study doubt in a clear, simple way suitable for class 10:
DOUBT: {question}
"""
response = model.generate_content(prompt)
return response.text
3️⃣ MCQ Generator
def mcq_agent(topic):
prompt = f"""
Generate 5 MCQs with answers & explanations for:
TOPIC: {topic}
Format:
Q1)
A)
B)
C)
D)
Correct Answer:
Explanation:
"""
response = model.generate_content(prompt)
return response.text
4️⃣ Explanation Agent
def explain_agent(hard_topic):
prompt = f"""
Explain this topic in very simple class 10 level language:
TOPIC: {hard_topic}
Provide examples.
"""
response = model.generate_content(prompt)
return response.text
5️⃣ Formula Helper
def formula_agent(subject_topic):
prompt = f"""
List all important formulas for:
TOPIC: {subject_topic}
Write in simple format.
"""
response = model.generate_content(prompt)
return response.text
6️⃣ Chapter Quiz Agent
def quiz_agent(chapter):
prompt = f"""
Create a chapter-wise quiz for:
CHAPTER: {chapter}
Include:
- 5 MCQs
- 5 True/False
- 5 Fill in the blanks
"""
response = model.generate_content(prompt)
return response.text
-----------------------------------------------
3. SIMPLE MENU / UI
-----------------------------------------------
def menu():
print("\n==============================")
print(" LSAA — Laggle Study AI Agent ")
print("==============================")
print("1. Notes Maker")
print("2. Doubt Solver")
print("3. MCQ Generator")
print("4. Simple Explanation")
print("5. Formula Helper")
print("6. Chapter Quiz")
print("7. Exit")
print("==============================")
-----------------------------------------------
4. MAIN LOOP
-----------------------------------------------
while True:
ch = menu()