Week4 exercise: Code and test app
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week4/community-contributions/Exercise_week4_jom.ipynb
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week4/community-contributions/Exercise_week4_jom.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fee27f39",
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"metadata": {},
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"outputs": [],
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"source": [
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"# imports\n",
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"\n",
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"import os\n",
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"from dotenv import load_dotenv\n",
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"from openai import OpenAI\n",
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"import gradio as gr\n",
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"\n",
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"load_dotenv(override=True)\n",
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"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
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"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
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"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
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"ollama_api_key = os.getenv('OLLAMA_API_KEY')\n",
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"\n",
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"if openai_api_key:\n",
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" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
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"else:\n",
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" print(\"OpenAI API Key not set\")\n",
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" \n",
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"if anthropic_api_key:\n",
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" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
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"else:\n",
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" print(\"Anthropic API Key not set (and this is optional)\")\n",
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"\n",
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"if google_api_key:\n",
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" print(f\"Google API Key exists and begins {google_api_key[:2]}\")\n",
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"else:\n",
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" print(\"Google API Key not set (and this is optional)\")\n",
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"\n",
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"if ollama_api_key:\n",
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" print(f\"OLLAMA API Key exists and begins {ollama_api_key[:2]}\")\n",
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"else:\n",
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" print(\"OLLAMA API Key not set (and this is optional)\")\n",
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"\n",
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"# Connect to client libraries\n",
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"\n",
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"openai = OpenAI()\n",
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"\n",
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"anthropic_url = \"https://api.anthropic.com/v1/\"\n",
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"gemini_url = \"https://generativelanguage.googleapis.com/v1beta/openai/\"\n",
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"ollama_url = \"http://localhost:11434/v1\"\n",
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"\n",
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"anthropic = OpenAI(api_key=anthropic_api_key, base_url=anthropic_url)\n",
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"gemini = OpenAI(api_key=google_api_key, base_url=gemini_url)\n",
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"ollama = OpenAI(api_key=ollama_api_key, base_url=ollama_url)\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d26f4175",
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"metadata": {},
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"outputs": [],
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"source": [
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"models = [\"gpt-5\", \"claude-sonnet-4-5-20250929\", \"gemini-2.5-pro\", \"gpt-oss:20b-cloud\", ]\n",
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"\n",
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"clients = {\"gpt-5\": openai, \"claude-sonnet-4-5-20250929\": anthropic, \"gemini-2.5-pro\": gemini, \"gpt-oss:20b-cloud\": ollama}\n",
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"\n",
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"# Want to keep costs ultra-low? Replace this with models of your choice, using the examples from yesterday"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "76563884",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_prompt_doc = \"\"\"You are an expert Python developer and code reviewer.\n",
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"Your job is to read the user's provided function, and return:\n",
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"1. A concise, PEP-257-compliant docstring summarizing what the function does, clarifying types, parameters, return values, and side effects.\n",
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"2. Helpful inline comments that improve both readability and maintainability, without restating what the code obviously does.\n",
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"\n",
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"Only output the function, not explanations or additional text. \n",
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"Do not modify variable names or refactor the function logic.\n",
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"Your response should improve the code's clarity and documentation, making it easier for others to understand and maintain.\n",
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"Don't be extremely verbose.\n",
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"Your answer should be at a {level} level of expertise.\n",
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"\"\"\"\n",
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"\n",
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"system_prompt_tests = \"\"\"You are a seasoned Python developer and testing expert.\n",
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"Your task is to read the user's provided function, and generate:\n",
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"1. A concise set of meaningful unit tests that thoroughly validate the function's correctness, including typical, edge, and error cases.\n",
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"2. The tests should be written for pytest (or unittest if pytest is not appropriate), use clear, descriptive names, and avoid unnecessary complexity.\n",
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"3. If dependencies or mocking are needed, include minimal necessary setup code (but avoid over-mocking).\n",
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"\n",
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"Only output the relevant test code, not explanations or extra text.\n",
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"Do not change the original function; focus solely on comprehensive, maintainable test coverage that other developers can easily understand and extend.\n",
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"\"\"\"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1bd82e96",
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"metadata": {},
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"outputs": [],
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"source": [
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"def generate_documentation(code, model, level):\n",
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" response = clients[model].chat.completions.create(\n",
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" model=model,\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": system_prompt_doc.format(level=level)},\n",
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" {\"role\": \"user\", \"content\": code}\n",
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" ],\n",
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" stream=True\n",
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" )\n",
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" output = \"\"\n",
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" for chunk in response:\n",
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" output += chunk.choices[0].delta.content or \"\"\n",
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" yield output.replace(\"```python\", \"\").replace(\"```\", \"\")\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b01b3421",
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"metadata": {},
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"outputs": [],
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"source": [
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"def generate_tests(code, model ):\n",
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" response = clients[model].chat.completions.create(\n",
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" model=model,\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": system_prompt_tests},\n",
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" {\"role\": \"user\", \"content\": code}\n",
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" ],\n",
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" stream=True\n",
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" )\n",
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" output = \"\"\n",
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" for chunk in response:\n",
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" output += chunk.choices[0].delta.content or \"\"\n",
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" yield output.replace(\"```python\", \"\").replace(\"```\", \"\")\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "16b71915",
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"metadata": {},
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"outputs": [],
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"source": [
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"vscode_dark = gr.themes.Monochrome(\n",
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" primary_hue=\"blue\",\n",
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" secondary_hue=\"slate\",\n",
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" neutral_hue=\"slate\",\n",
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").set(\n",
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" body_background_fill=\"#1e1e1e\",\n",
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" body_background_fill_dark=\"#1e1e1e\",\n",
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" block_background_fill=\"#252526\",\n",
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" block_background_fill_dark=\"#252526\",\n",
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" block_border_color=\"#3e3e42\",\n",
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" block_border_color_dark=\"#3e3e42\",\n",
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" border_color_primary=\"#3e3e42\",\n",
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" block_label_background_fill=\"#252526\",\n",
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" block_label_background_fill_dark=\"#252526\",\n",
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" block_label_text_color=\"#cccccc\",\n",
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" block_label_text_color_dark=\"#cccccc\",\n",
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" block_title_text_color=\"#cccccc\",\n",
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" block_title_text_color_dark=\"#cccccc\",\n",
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" body_text_color=\"#d4d4d4\",\n",
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" body_text_color_dark=\"#d4d4d4\",\n",
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" button_primary_background_fill=\"#0e639c\",\n",
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" button_primary_background_fill_dark=\"#0e639c\",\n",
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" button_primary_background_fill_hover=\"#1177bb\",\n",
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" button_primary_background_fill_hover_dark=\"#1177bb\",\n",
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" button_primary_text_color=\"#ffffff\",\n",
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" button_primary_text_color_dark=\"#ffffff\",\n",
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" input_background_fill=\"#3c3c3c\",\n",
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" input_background_fill_dark=\"#3c3c3c\",\n",
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" color_accent=\"#007acc\",\n",
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" color_accent_soft=\"#094771\",\n",
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")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "23311022",
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"metadata": {},
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"outputs": [],
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"source": [
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"import gradio as gr\n",
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"\n",
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"with gr.Blocks(theme=vscode_dark, css=\"\"\"\n",
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" .gradio-container {font-family: 'Consolas', 'Monaco', monospace;}\n",
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" h1 {color: #d4d4d4 !important;}\n",
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"\"\"\") as ui:\n",
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" gr.Markdown(\"# 🧑💻 Python Code Reviewer & Test Generator\", elem_id=\"app-title\")\n",
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" with gr.Tab(\"Docstring & Comments\") as tab1:\n",
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" gr.Markdown(\"# Function Docstring & Comment Helper\\nPaste your function below and get helpful docstrings and inline comments!\")\n",
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"\n",
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" with gr.Row():\n",
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" code_input_1 = gr.Code(label=\"Paste your Python function here\", lines=10, language=\"python\")\n",
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" code_output = gr.Code(label=\"Function with improved docstring and comments\", lines=10, language=\"python\")\n",
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" \n",
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" with gr.Row(equal_height=True):\n",
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" level_radio = gr.Radio(choices=[\"Junior\", \"Mid\", \"Senior\"], value=\"Mid\", label=\"Reviewer level\", interactive=True)\n",
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" model_dropdown = gr.Dropdown(choices=models, value=models[-1], label=\"Select model\")\n",
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" submit_doc_btn = gr.Button(\"Generate docstring & comments\", scale=0.5)\n",
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"\n",
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" submit_doc_btn.click(\n",
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" generate_documentation, \n",
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" inputs=[code_input_1, model_dropdown, level_radio], \n",
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" outputs=code_output\n",
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" )\n",
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"\n",
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" with gr.Tab(\"Unit Tests\") as tab2:\n",
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" gr.Markdown(\"# Unit Test Generator\\nPaste your function below and get auto-generated unit tests!\")\n",
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"\n",
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" with gr.Row():\n",
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" code_input_2 = gr.Code(label=\"Paste your Python function here\", lines=10, language=\"python\")\n",
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" code_output_2 = gr.Code(label=\"Generated tests\", lines=10, language=\"python\")\n",
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" \n",
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" with gr.Row(equal_height=True):\n",
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" model_dropdown_2 = gr.Dropdown(choices=models, value=models[-1], label=\"Select model\")\n",
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" submit_test_btn = gr.Button(\"Generate unit tests\", scale=0.5)\n",
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"\n",
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" submit_test_btn.click(\n",
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" generate_tests, \n",
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" inputs=[code_input_2, model_dropdown_2], \n",
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" outputs=code_output_2\n",
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" )\n",
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" \n",
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" tab2.select(lambda x: x, inputs=code_input_1, outputs=code_input_2)\n",
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" tab1.select(lambda x: x, inputs=code_input_2, outputs=code_input_1)\n",
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"\n",
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"ui.launch(share=False, inbrowser=True)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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