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Week4 GenAi Andela bootcamp project

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Chimwemwe Kachaje
2025-10-20 10:48:05 +02:00
parent 70250910f4
commit 71fb93947d
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152
week4/community-contributions/kachaje-genai-bootcamp/week4-bootcamp-docs.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"id": "ee939d6d",
"metadata": {},
"source": [
"# Docstring Generator for Code\n",
"\n",
"Tool for generating documentation/comments for code using a local Llama LLM model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d61ff2a0",
"metadata": {},
"outputs": [],
"source": [
"# imports \n",
"\n",
"from openai import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1410b7dd",
"metadata": {},
"outputs": [],
"source": [
"openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8391d095",
"metadata": {},
"outputs": [],
"source": [
"# model\n",
"\n",
"MODEL = \"llama3.2\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8f55ad72",
"metadata": {},
"outputs": [],
"source": [
"def create_user_prompt(code_snippet=\"\"\"\n",
"def calculate_total_price(price, tax_rate):\n",
" return price * (1 + tax_rate)\n",
"\"\"\"):\n",
" return f\"\"\"\n",
"Please generate a Google-style Python docstring for the following function. Explain its purpose, arguments, return value, and any exceptions it might raise. Include a small usage example if applicable.\n",
"\n",
"```python\n",
"{code_snippet}\n",
"```\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "48b0e6e3",
"metadata": {},
"outputs": [],
"source": [
"user_prompt = create_user_prompt()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "648e61f9",
"metadata": {},
"outputs": [],
"source": [
"print(user_prompt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "af787e3e",
"metadata": {},
"outputs": [],
"source": [
"def create_docstring_for_code(user_prompt):\n",
" system_message = (\n",
" \"You are a helpful assistant that generates docstrings for code.\"\n",
" )\n",
" response = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
" )\n",
" result = response.choices[0].message.content\n",
"\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e740c9e1",
"metadata": {},
"outputs": [],
"source": [
"result = create_docstring_for_code(user_prompt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f9b030c6",
"metadata": {},
"outputs": [],
"source": [
"print(result)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

152
week4/community-contributions/kachaje-genai-bootcamp/week4-bootcamp-unit-tests.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"id": "ee939d6d",
"metadata": {},
"source": [
"# Unit Tests Generator for Code\n",
"\n",
"Tool for generating unit tests for code using a local Llama LLM model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d61ff2a0",
"metadata": {},
"outputs": [],
"source": [
"# imports \n",
"\n",
"from openai import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1410b7dd",
"metadata": {},
"outputs": [],
"source": [
"openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8391d095",
"metadata": {},
"outputs": [],
"source": [
"# model\n",
"\n",
"MODEL = \"llama3.2\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8f55ad72",
"metadata": {},
"outputs": [],
"source": [
"def create_user_prompt(code_snippet=\"\"\"\n",
"def calculate_total_price(price, tax_rate):\n",
" return price * (1 + tax_rate)\n",
"\"\"\"):\n",
" return f\"\"\"\n",
"Please generate unit tests for the following code. Maximize on coverage. Take care of edge cases as well.\n",
"\n",
"```python\n",
"{code_snippet}\n",
"```\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "48b0e6e3",
"metadata": {},
"outputs": [],
"source": [
"user_prompt = create_user_prompt()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "648e61f9",
"metadata": {},
"outputs": [],
"source": [
"print(user_prompt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "af787e3e",
"metadata": {},
"outputs": [],
"source": [
"def create_unit_tests_for_code(user_prompt):\n",
" system_message = (\n",
" \"You are a helpful assistant that generates unit tests for code.\"\n",
" )\n",
" response = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
" )\n",
" result = response.choices[0].message.content\n",
"\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e740c9e1",
"metadata": {},
"outputs": [],
"source": [
"result = create_unit_tests_for_code(user_prompt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f9b030c6",
"metadata": {},
"outputs": [],
"source": [
"print(result)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}