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adding the notebook for unit tests generation assignment

pull/108/head
Elena Shirokova 4 months ago
parent
commit
84c8aded5e
  1. 432
      week4/community-contributions/unit-tests-generator.ipynb

432
week4/community-contributions/unit-tests-generator.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Requirements\n",
"\n",
"1. Install pytest and pytest-cov library\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pipenv install pytest pytest-cov"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"import re\n",
"import os\n",
"import sys\n",
"import textwrap\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import anthropic\n",
"import gradio as gr\n",
"from pathlib import Path\n",
"import subprocess\n",
"from IPython.display import Markdown"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Initialization\n",
"\n",
"load_dotenv()\n",
"\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
" \n",
"OPENAI_MODEL = \"gpt-4o-mini\"\n",
"CLAUDE_MODEL = \"claude-3-5-sonnet-20240620\"\n",
"openai = OpenAI()\n",
"claude = anthropic.Anthropic()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
"HEADERS = {\"Content-Type\": \"application/json\"}\n",
"OLLAMA_MODEL = \"llama3.2\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Code execution"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"\n",
"def extract_code(text):\n",
" # Regular expression to find text between ``python and ``\n",
" match = re.search(r\"```python(.*?)```\", text, re.DOTALL)\n",
"\n",
" if match:\n",
" code = match.group(0).strip() # Extract and strip extra spaces\n",
" else:\n",
" code = \"\"\n",
" print(\"No matching substring found.\")\n",
"\n",
" return code.replace(\"```python\\n\", \"\").replace(\"```\", \"\")\n",
"\n",
"\n",
"def execute_coverage_report(python_interpreter=sys.executable):\n",
" if not python_interpreter:\n",
" raise EnvironmentError(\"Python interpreter not found in the specified virtual environment.\")\n",
" # test_code_path = Path(\"tests\")\n",
" # command = [\"pytest\", \"-cov\",\"--capture=no\"]\n",
" command = [\"coverage\", \"run\", \"-m\", \"pytest\"]\n",
" # command =[\"pytest\", \"--cov=your_package\", \"--cov-report=term-missing\"]\n",
"\n",
" try:\n",
" result = subprocess.run(command, check=True, capture_output=True, text=True)\n",
" print(\"Tests ran successfully!\")\n",
" print(result.stdout)\n",
" return result.stdout\n",
" except subprocess.CalledProcessError as e:\n",
" print(\"Some tests failed!\")\n",
" print(\"Output:\\n\", e.stdout)\n",
" print(\"Errors:\\n\", e.stderr)\n",
" # Extracting failed test information\n",
" failed_tests = []\n",
" for line in e.stdout.splitlines():\n",
" if \"FAILED\" in line and \"::\" in line:\n",
" failed_tests.append(line.strip())\n",
" if failed_tests:\n",
" print(\"Failed Tests:\")\n",
" for test in failed_tests:\n",
" print(test)\n",
" return failed_tests\n",
"\n",
"def save_unit_tests(code):\n",
"\n",
" match = re.search(r\"def\\s+(\\w+)\\(\", code, re.DOTALL)\n",
"\n",
" if match:\n",
" function_name = match.group(1).strip() # Extract and strip extra spaces\n",
" else:\n",
" function_name = \"\"\n",
" print(\"No matching substring found.\")\n",
"\n",
" test_code_path = Path(\"tests\")\n",
" (test_code_path / f\"test_{function_name}.py\").write_text(extract_code(code))\n",
" Path(\"tests\", \"test_code.py\").unlink()\n",
" \n",
"\n",
"def execute_tests_in_venv(code_to_test, tests, python_interpreter=sys.executable):\n",
" \"\"\"\n",
" Execute the given Python code string within the specified virtual environment.\n",
" \n",
" Args:\n",
" - code_str: str, the Python code to execute.\n",
" - venv_dir: str, the directory path to the virtual environment created by pipenv.\n",
" \"\"\"\n",
" \n",
" if not python_interpreter:\n",
" raise EnvironmentError(\"Python interpreter not found in the specified virtual environment.\")\n",
"\n",
" # Prepare the command to execute the code\n",
" code_str = textwrap.dedent(code_to_test) + \"\\n\" + extract_code(tests)\n",
" test_code_path = Path(\"tests\")\n",
" test_code_path.mkdir(parents=True, exist_ok=True)\n",
" (test_code_path / f\"test_code.py\").write_text(code_str)\n",
" command = [\"pytest\", str(test_code_path)]\n",
"\n",
" try:\n",
" result = subprocess.run(command, check=True, capture_output=True, text=True)\n",
" print(\"Tests ran successfully!\")\n",
" print(result.stderr)\n",
" return result.stdout\n",
" except subprocess.CalledProcessError as e:\n",
" print(\"Some tests failed!\")\n",
" print(\"Output:\\n\", e.stdout)\n",
" print(\"Errors:\\n\", e.stderr)\n",
" # Extracting failed test information\n",
" failed_tests = []\n",
" for line in e.stdout.splitlines():\n",
" if \"FAILED\" in line and \"::\" in line:\n",
" failed_tests.append(line.strip())\n",
" if failed_tests:\n",
" print(\"Failed Tests:\")\n",
" for test in failed_tests:\n",
" print(test)\n",
" return e.stderr\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prompts and calls to the models"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"system_message = \"\"\"You are a helpful assistant which helps developers to write unit test cases for their code.\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def get_user_prompt(code):\n",
"\n",
" user_prompt = \"Write for a python code the unit test cases.\"\n",
" user_prompt += \"Return unit tests cases using pytest library, do not create any custom imports; do not explain your work other than a few comments.\"\n",
" user_prompt += \"Do not insert the function to be tested in the output before the tests. Validate both the case where the function is executed successfully and where it is expected to fail.\"\n",
" user_prompt += code\n",
"\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def stream_gpt(code):\n",
"\n",
" user_prompt = get_user_prompt(code)\n",
" stream = openai.chat.completions.create(\n",
" model=OPENAI_MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": user_prompt,\n",
" },\n",
" ],\n",
" stream=True,\n",
" )\n",
"\n",
" response = \"\"\n",
" for chunk in stream:\n",
" response += chunk.choices[0].delta.content or \"\"\n",
" yield response\n",
" \n",
" return response\n",
"\n",
"def stream_ollama(code):\n",
"\n",
" user_prompt = get_user_prompt(code)\n",
" ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
" stream = ollama_via_openai.chat.completions.create(\n",
" model=OLLAMA_MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": user_prompt,\n",
" },\n",
" ],\n",
" stream=True,\n",
" )\n",
"\n",
" response = \"\"\n",
" for chunk in stream:\n",
" response += chunk.choices[0].delta.content or \"\"\n",
" yield response\n",
" \n",
" return response\n",
"\n",
"\n",
"def stream_claude(code):\n",
" user_prompt = get_user_prompt(code)\n",
" result = claude.messages.stream(\n",
" model=CLAUDE_MODEL,\n",
" max_tokens=2000,\n",
" system=system_message,\n",
" messages=[\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": user_prompt,\n",
" }\n",
" ],\n",
" )\n",
" reply = \"\"\n",
" with result as stream:\n",
" for text in stream.text_stream:\n",
" reply += text\n",
" yield reply\n",
" print(text, end=\"\", flush=True)\n",
" return reply"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Code examples to test the inteface"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"function_to_test = \"\"\"\n",
" def lengthOfLongestSubstring(s):\n",
" max_length = 0\n",
" substring = \"\"\n",
" start_idx = 0\n",
" while start_idx < len(s):\n",
" string = s[start_idx:]\n",
" for i, x in enumerate(string):\n",
" substring += x\n",
" if len(substring) == len(set((list(substring)))):\n",
" \n",
" if len(set((list(substring)))) > max_length:\n",
" \n",
" max_length = len(substring)\n",
"\n",
" start_idx += 1\n",
" substring = \"\"\n",
" \n",
" \n",
" return max_length\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"test_code = \"\"\"```python\n",
"import pytest\n",
"\n",
"# Unit tests using pytest\n",
"def test_lengthOfLongestSubstring():\n",
" assert lengthOfLongestSubstring(\"abcabcbb\") == 3 # Case with repeating characters\n",
" assert lengthOfLongestSubstring(\"bbbbb\") == 1 # Case with all same characters\n",
" assert lengthOfLongestSubstring(\"pwwkew\") == 3 # Case with mixed characters\n",
" assert lengthOfLongestSubstring(\"\") == 0 # Empty string case\n",
" assert lengthOfLongestSubstring(\"abcdef\") == 6 # All unique characters\n",
" assert lengthOfLongestSubstring(\"abca\") == 3 # Case with pattern and repeat\n",
" assert lengthOfLongestSubstring(\"dvdf\") == 3 # Case with repeated characters separated\n",
" assert lengthOfLongestSubstring(\"a\") == 1 # Case with single character\n",
" assert lengthOfLongestSubstring(\"au\") == 2 # Case with unique two characters\n",
"```\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def optimize(code, model):\n",
" if model == \"GPT\":\n",
" result = stream_gpt(code)\n",
" elif model == \"Claude\":\n",
" result = stream_claude(code)\n",
" elif model == \"Ollama\":\n",
" result = stream_ollama(code)\n",
" else:\n",
" raise ValueError(\"Unknown model\")\n",
" for stream_so_far in result:\n",
" yield stream_so_far\n",
" return result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Gradio interface"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with gr.Blocks() as ui:\n",
" gr.Markdown(\"## Write unit tests for Python code\")\n",
" with gr.Row():\n",
" with gr.Column(scale=1, min_width=300):\n",
" python = gr.Textbox(label=\"Python code:\", value=function_to_test, lines=10)\n",
" model = gr.Dropdown([\"GPT\", \"Claude\", \"Ollama\"], label=\"Select model\", value=\"GPT\")\n",
" unit_tests = gr.Button(\"Write unit tests\")\n",
" with gr.Column(scale=1, min_width=300):\n",
" unit_tests_out = gr.TextArea(label=\"Unit tests\", value=test_code, elem_classes=[\"python\"])\n",
" unit_tests_run = gr.Button(\"Run unit tests\")\n",
" coverage_run = gr.Button(\"Coverage report\")\n",
" save_test_run = gr.Button(\"Save unit tests\")\n",
" with gr.Row():\n",
" \n",
" python_out = gr.TextArea(label=\"Unit tests result\", elem_classes=[\"python\"])\n",
" coverage_out = gr.TextArea(label=\"Coverage report\", elem_classes=[\"python\"])\n",
" \n",
"\n",
" unit_tests.click(optimize, inputs=[python, model], outputs=[unit_tests_out])\n",
" unit_tests_run.click(execute_tests_in_venv, inputs=[python, unit_tests_out], outputs=[python_out])\n",
" coverage_run.click(execute_coverage_report, outputs=[coverage_out])\n",
" save_test_run.click(save_unit_tests, inputs=[unit_tests_out])\n",
"\n",
"\n",
"ui.launch(inbrowser=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "llm_engineering-yg2xCEUG",
"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": "3.10.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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