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Bootcamp/Euclid (JonasThamane): Week 4 - AI Code Generator from Python to C++ #3

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328 changes: 328 additions & 0 deletions community-contributions/Jonas Thamane Week 4 PR.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "intro",
"metadata": {},
"source": [
"# AI Coding Assistant\n",
"\n",
"Help you with writing code comments, unit tests, code fixing, and **converting Python to C++**.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "imports",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✓ Imports loaded\n"
]
}
],
"source": [
"\n",
"import os\n",
"import anthropic\n",
"import gradio as gr\n",
"from dotenv import load_dotenv\n",
"\n",
"load_dotenv(override=True)\n",
"\n",
"\n",
"MODEL = \"claude-sonnet-4-20250514\"\n",
"MAX_TOKENS = 4096\n",
"\n",
"SYSTEM_PROMPT = (\n",
" \"You are a senior software engineer fluent in both Python and C++. \"\n",
" \"When converting to C++, produce clean, compilable C++17 code with proper \"\n",
" \"memory management and idiomatic style.\"\n",
")\n",
"\n",
"print(\"✓ Imports loaded\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "client",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✓ Client helper ready\n"
]
}
],
"source": [
"\n",
"def get_client():\n",
" key = os.environ.get(\"ANTHROPIC_API_KEY\", \"\")\n",
" if not key:\n",
" raise ValueError(\n",
" \"ANTHROPIC_API_KEY not set. \"\n",
" \"Add it to your .env file or set it as an environment variable.\"\n",
" )\n",
" return anthropic.Anthropic(api_key=key)\n",
"\n",
"print(\"✓ Client helper ready\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "tasks",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✓ Task builder ready\n"
]
}
],
"source": [
"\n",
"TASK_OPTIONS = [\n",
" \"Add Comments\",\n",
" \"Explain Code\",\n",
" \"Write Unit Tests\",\n",
" \"Fix Code\",\n",
" \"Convert to C++\",\n",
"]\n",
"\n",
"\n",
"def build_prompt(tasks, code):\n",
" instructions = []\n",
"\n",
" if \"Add Comments\" in tasks:\n",
" instructions.append(\"Add clear, helpful inline comments to the code.\")\n",
" if \"Explain Code\" in tasks:\n",
" instructions.append(\"Explain what the code does in plain English.\")\n",
" if \"Write Unit Tests\" in tasks:\n",
" instructions.append(\"Write comprehensive unit tests using pytest.\")\n",
" if \"Fix Code\" in tasks:\n",
" instructions.append(\"Identify and fix any bugs or issues in the code.\")\n",
" if \"Convert to C++\" in tasks:\n",
" instructions.append(\n",
" \"Convert the Python code to idiomatic, modern C++17. \"\n",
" \"Include all necessary headers, use appropriate STL containers and algorithms, \"\n",
" \"add a main() function with a usage example, and add comments explaining \"\n",
" \"key differences from the Python version.\"\n",
" )\n",
"\n",
" return f\"Tasks:\\n- {'\\n- '.join(instructions)}\\n\\nPython Code:\\n```python\\n{code}\\n```\"\n",
"\n",
"\n",
"print(\"✓ Task builder ready\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "generation",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✓ Generation function ready\n"
]
}
],
"source": [
"\n",
"def run_code_assistant(model, tasks, code):\n",
" if not code.strip():\n",
" return \"Please provide some Python code.\"\n",
" if not tasks:\n",
" return \"Please select at least one task.\"\n",
"\n",
" try:\n",
" client = get_client()\n",
" user_prompt = build_prompt(tasks, code)\n",
"\n",
" response = client.messages.create(\n",
" model=model,\n",
" max_tokens=MAX_TOKENS,\n",
" system=SYSTEM_PROMPT,\n",
" messages=[{\"role\": \"user\", \"content\": user_prompt}],\n",
" )\n",
" return response.content[0].text\n",
"\n",
" except anthropic.AuthenticationError:\n",
" return \"⚠ Invalid API key. Check your ANTHROPIC_API_KEY.\"\n",
" except Exception as e:\n",
" return f\"⚠ Error: {e}\"\n",
"\n",
"\n",
"print(\"✓ Generation function ready\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "sample_code",
"metadata": {},
"outputs": [],
"source": [
"\n",
"SAMPLE_PYTHON = \"\"\"\\\n",
"class Stack:\n",
" def __init__(self):\n",
" self.items = []\n",
"\n",
" def push(self, item):\n",
" self.items.append(item)\n",
"\n",
" def pop(self):\n",
" if self.is_empty():\n",
" raise IndexError(\"pop from empty stack\")\n",
" return self.items.pop()\n",
"\n",
" def peek(self):\n",
" if self.is_empty():\n",
" raise IndexError(\"peek at empty stack\")\n",
" return self.items[-1]\n",
"\n",
" def is_empty(self):\n",
" return len(self.items) == 0\n",
"\n",
" def size(self):\n",
" return len(self.items)\n",
"\n",
"\n",
"def is_balanced(expression):\n",
" stack = Stack()\n",
" pairs = {\")\": \"(\", \"]\": \"[\", \"}\": \"{\"}\n",
" for char in expression:\n",
" if char in \"([{\":\n",
" stack.push(char)\n",
" elif char in \")]}\":\n",
" if stack.is_empty() or stack.pop() != pairs[char]:\n",
" return False\n",
" return stack.is_empty()\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "ui",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7863\n",
"* To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7863/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"CLAUDE_MODELS = [\n",
" \"claude-sonnet-4-20250514\",\n",
" \"claude-opus-4-20250514\",\n",
" \"claude-haiku-4-5-20251001\",\n",
"]\n",
"\n",
"with gr.Blocks(title=\"AI Coding Assistant\") as ui:\n",
"\n",
" gr.Markdown(\"# AI Coding Assistant\")\n",
" gr.Markdown(\n",
" \"Add comments, explain, fix, write tests, or **convert Python → C++**. \"\n",
" \"Pick a Claude model, tick your tasks, paste code, and hit **Run**.\"\n",
" )\n",
"\n",
"\n",
" model = gr.Dropdown(\n",
" choices=CLAUDE_MODELS,\n",
" value=CLAUDE_MODELS[0],\n",
" label=\"Claude Model\",\n",
" )\n",
"\n",
"\n",
" tasks = gr.CheckboxGroup(\n",
" choices=TASK_OPTIONS,\n",
" value=[\"Convert to C++\"],\n",
" label=\"Tasks\",\n",
" )\n",
"\n",
" \n",
" code_input = gr.Code(\n",
" value=SAMPLE_PYTHON,\n",
" language=\"python\",\n",
" label=\"Python Code\",\n",
" lines=25,\n",
" )\n",
"\n",
" with gr.Row():\n",
" run_btn = gr.Button(\"▶ Run\", variant=\"primary\")\n",
" clear_btn = gr.Button(\"🗑 Clear output\")\n",
"\n",
" output = gr.Markdown(label=\"Output\")\n",
"\n",
" run_btn.click(\n",
" run_code_assistant,\n",
" inputs=[model, tasks, code_input],\n",
" outputs=output,\n",
" )\n",
" clear_btn.click(lambda: \"\", outputs=output)\n",
"\n",
"ui.launch()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"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": "3.13.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}