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Added Week 4 exercise: AI-Powered Code Review Assistant

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Philip Omoigui
2025-11-02 13:19:41 +01:00
parent 80ae1df886
commit b0e6254a29
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import json\n",
"import re\n",
"from typing import List, Dict, Optional\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr\n",
"from IPython.display import Markdown, display\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Groq API Key not set (optional)\n",
"OpenRouter API Key loaded (begins with sk-or-)\n"
]
}
],
"source": [
"load_dotenv(override=True)\n",
"\n",
"# Ollama connection \n",
"ollama_url = \"http://localhost:11434/v1\"\n",
"ollama_client = OpenAI(api_key=\"ollama\", base_url=ollama_url)\n",
"\n",
"# Groq connection\n",
"groq_api_key = os.getenv('GROQ_API_KEY')\n",
"groq_url = \"https://api.groq.com/openai/v1\"\n",
"groq_client = None\n",
"if groq_api_key:\n",
" groq_client = OpenAI(api_key=groq_api_key, base_url=groq_url)\n",
" print(f\"Groq API Key loaded (begins with {groq_api_key[:4]})\")\n",
"else:\n",
" print(\"Groq API Key not set (optional)\")\n",
"\n",
"# OpenRouter connection\n",
"openrouter_api_key = os.getenv('OPENROUTER_API_KEY')\n",
"openrouter_url = \"https://openrouter.ai/api/v1\"\n",
"openrouter_client = None\n",
"if openrouter_api_key:\n",
" openrouter_client = OpenAI(api_key=openrouter_api_key, base_url=openrouter_url)\n",
" print(f\"OpenRouter API Key loaded (begins with {openrouter_api_key[:6]})\")\n",
"else:\n",
" print(\"OpenRouter API Key not set (optional)\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Configured 3 models\n",
"OpenRouter models available (perfect for limited storage demos!)\n"
]
}
],
"source": [
"# Open-source code models configuration\n",
"MODELS = {}\n",
"\n",
"if groq_client:\n",
" MODELS.update({\n",
" \"gpt-oss-20b-groq\": {\n",
" \"name\": \"GPT-OSS-20B (Groq)\",\n",
" \"client\": groq_client,\n",
" \"model\": \"gpt-oss:20b\",\n",
" \"description\": \"Cloud\"\n",
" },\n",
" \"gpt-oss-120b-groq\": {\n",
" \"name\": \"GPT-OSS-120B (Groq)\",\n",
" \"client\": groq_client,\n",
" \"model\": \"openai/gpt-oss-120b\",\n",
" \"description\": \"Cloud - Larger GPT-OSS\"\n",
" },\n",
" \"qwen2.5-coder-32b-groq\": {\n",
" \"name\": \"Qwen2.5-Coder 32B (Groq)\",\n",
" \"client\": groq_client,\n",
" \"model\": \"qwen/qwen2.5-coder-32b-instruct\",\n",
" \"description\": \"Cloud\"\n",
" },\n",
" })\n",
"\n",
"# OpenRouter models\n",
"if openrouter_client:\n",
" MODELS.update({\n",
" \"qwen-2.5-coder-32b-openrouter\": {\n",
" \"name\": \"Qwen2.5-Coder 32B (OpenRouter)\",\n",
" \"client\": openrouter_client,\n",
" \"model\": \"qwen/qwen-2.5-coder-32b-instruct\",\n",
" \"description\": \"Cloud - Perfect for demos, 50 req/day free\"\n",
" },\n",
" \"gpt-oss-20b-groq\": {\n",
" \"name\": \"GPT-OSS-20B\",\n",
" \"client\": openrouter_client,\n",
" \"model\": \"openai/gpt-oss-20b\",\n",
" \"description\": \"Cloud - OpenAI's open model, excellent for code!\"\n",
" },\n",
" })\n",
"\n",
"print(f\"Configured {len(MODELS)} models\")\n",
"if openrouter_client:\n",
" print(\"OpenRouter models available (perfect for limited storage demos!)\")\n",
"if groq_client:\n",
" print(\"Groq models available (fast cloud inference!)\")\n",
"if \"qwen2.5-coder:7b\" in MODELS:\n",
" print(\"Ollama models available (unlimited local usage!)\")\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"BUG_DETECTION_SYSTEM_PROMPT = \"\"\"You are an expert code reviewer specializing in finding bugs, security vulnerabilities, and logic errors.\n",
"\n",
"Your task is to analyze Python code and identify issues. Return ONLY a valid JSON array with this exact format:\n",
"[{\n",
" \"severity\": \"critical|high|medium|low\",\n",
" \"line\": number,\n",
" \"issue\": \"brief description of the problem\",\n",
" \"suggestion\": \"specific fix recommendation\"\n",
"}]\n",
"\n",
"Be thorough but concise. Focus on real bugs and security issues.\"\"\"\n",
"\n",
"IMPROVEMENTS_SYSTEM_PROMPT = \"\"\"You are a senior software engineer specializing in code quality and best practices.\n",
"\n",
"Analyze the Python code and suggest improvements for:\n",
"- Code readability and maintainability\n",
"- Performance optimizations\n",
"- Pythonic idioms and conventions\n",
"- Better error handling\n",
"\n",
"Return ONLY a JSON array:\n",
"[{\n",
" \"category\": \"readability|performance|style|error_handling\",\n",
" \"line\": number,\n",
" \"current\": \"current code snippet\",\n",
" \"improved\": \"improved code snippet\",\n",
" \"explanation\": \"why this is better\"\n",
"}]\n",
"\n",
"Only suggest meaningful improvements.\"\"\"\n",
"\n",
"TEST_GENERATION_SYSTEM_PROMPT = \"\"\"You are an expert in writing comprehensive unit tests.\n",
"\n",
"Generate pytest unit tests for the given Python code. Include:\n",
"- Test cases for normal operation\n",
"- Edge cases and boundary conditions\n",
"- Error handling tests\n",
"- Tests for any bugs that were identified\n",
"\n",
"Return ONLY Python code with pytest tests. Include the original code at the top if needed.\n",
"Do not include explanations or markdown formatting.\"\"\"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def extract_json_from_response(text: str) -> List[Dict]:\n",
" \"\"\"Extract JSON array from model response, handling markdown code blocks.\"\"\"\n",
" # Remove markdown code blocks\n",
" text = re.sub(r'```json\\n?', '', text)\n",
" text = re.sub(r'```\\n?', '', text)\n",
" \n",
" # Try to find JSON array\n",
" json_match = re.search(r'\\[\\s*\\{.*\\}\\s*\\]', text, re.DOTALL)\n",
" if json_match:\n",
" try:\n",
" return json.loads(json_match.group())\n",
" except json.JSONDecodeError:\n",
" pass\n",
" \n",
" # Fallback: try parsing entire response\n",
" try:\n",
" return json.loads(text.strip())\n",
" except json.JSONDecodeError:\n",
" return []\n",
"\n",
"def detect_bugs(code: str, model_key: str) -> Dict:\n",
" \"\"\"Detect bugs and security issues in code.\"\"\"\n",
" model_config = MODELS[model_key]\n",
" client = model_config[\"client\"]\n",
" model_name = model_config[\"model\"]\n",
" \n",
" user_prompt = f\"Analyze this Python code for bugs and security issues:\\n\\n```python\\n{code}\\n```\"\n",
" \n",
" try:\n",
" response = client.chat.completions.create(\n",
" model=model_name,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": BUG_DETECTION_SYSTEM_PROMPT},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ],\n",
" temperature=0.1\n",
" )\n",
" \n",
" content = response.choices[0].message.content\n",
" issues = extract_json_from_response(content)\n",
" \n",
" return {\n",
" \"model\": model_config[\"name\"],\n",
" \"issues\": issues,\n",
" \"raw_response\": content,\n",
" \"success\": True\n",
" }\n",
" except Exception as e:\n",
" return {\n",
" \"model\": model_config[\"name\"],\n",
" \"issues\": [],\n",
" \"error\": str(e),\n",
" \"success\": False\n",
" }\n",
"\n",
"def suggest_improvements(code: str, model_key: str) -> Dict:\n",
" \"\"\"Suggest code improvements and best practices.\"\"\"\n",
" model_config = MODELS[model_key]\n",
" client = model_config[\"client\"]\n",
" model_name = model_config[\"model\"]\n",
" \n",
" user_prompt = f\"Suggest improvements for this Python code:\\n\\n```python\\n{code}\\n```\"\n",
" \n",
" try:\n",
" response = client.chat.completions.create(\n",
" model=model_name,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": IMPROVEMENTS_SYSTEM_PROMPT},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ],\n",
" temperature=0.2\n",
" )\n",
" \n",
" content = response.choices[0].message.content\n",
" improvements = extract_json_from_response(content)\n",
" \n",
" return {\n",
" \"model\": model_config[\"name\"],\n",
" \"improvements\": improvements,\n",
" \"raw_response\": content,\n",
" \"success\": True\n",
" }\n",
" except Exception as e:\n",
" return {\n",
" \"model\": model_config[\"name\"],\n",
" \"improvements\": [],\n",
" \"error\": str(e),\n",
" \"success\": False\n",
" }\n",
"\n",
"def generate_tests(code: str, bugs: List[Dict], model_key: str) -> Dict:\n",
" \"\"\"Generate unit tests for the code.\"\"\"\n",
" model_config = MODELS[model_key]\n",
" client = model_config[\"client\"]\n",
" model_name = model_config[\"model\"]\n",
" \n",
" bugs_context = \"\"\n",
" if bugs:\n",
" bugs_context = f\"\\n\\nNote: The following bugs were identified:\\n\" + \"\\n\".join([f\"- Line {b.get('line', '?')}: {b.get('issue', '')}\" for b in bugs])\n",
" \n",
" user_prompt = f\"Generate pytest unit tests for this Python code:{bugs_context}\\n\\n```python\\n{code}\\n```\"\n",
" \n",
" try:\n",
" response = client.chat.completions.create(\n",
" model=model_name,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": TEST_GENERATION_SYSTEM_PROMPT},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ],\n",
" temperature=0.3\n",
" )\n",
" \n",
" content = response.choices[0].message.content\n",
" # Remove markdown code blocks if present\n",
" test_code = re.sub(r'```python\\n?', '', content)\n",
" test_code = re.sub(r'```\\n?', '', test_code)\n",
" \n",
" return {\n",
" \"model\": model_config[\"name\"],\n",
" \"test_code\": test_code.strip(),\n",
" \"raw_response\": content,\n",
" \"success\": True\n",
" }\n",
" except Exception as e:\n",
" return {\n",
" \"model\": model_config[\"name\"],\n",
" \"test_code\": \"\",\n",
" \"error\": str(e),\n",
" \"success\": False\n",
" }\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def format_bugs_output(result: Dict) -> str:\n",
" \"\"\"Format bug detection results for display.\"\"\"\n",
" if not result.get(\"success\"):\n",
" return f\"**Error with {result['model']}:** {result.get('error', 'Unknown error')}\"\n",
" \n",
" issues = result.get(\"issues\", [])\n",
" if not issues:\n",
" return f\"✅ **{result['model']}**: No issues found. Code looks good!\"\n",
" \n",
" output = [f\"**{result['model']}** - Found {len(issues)} issue(s):\\n\"]\n",
" \n",
" severity_order = {\"critical\": 0, \"high\": 1, \"medium\": 2, \"low\": 3}\n",
" sorted_issues = sorted(issues, key=lambda x: severity_order.get(x.get(\"severity\", \"low\"), 3))\n",
" \n",
" for issue in sorted_issues:\n",
" severity = issue.get(\"severity\", \"unknown\").upper()\n",
" line = issue.get(\"line\", \"?\")\n",
" issue_desc = issue.get(\"issue\", \"\")\n",
" suggestion = issue.get(\"suggestion\", \"\")\n",
" \n",
" severity_emoji = {\n",
" \"CRITICAL\": \"🔴\",\n",
" \"HIGH\": \"🟠\",\n",
" \"MEDIUM\": \"🟡\",\n",
" \"LOW\": \"🔵\"\n",
" }.get(severity, \"⚪\")\n",
" \n",
" output.append(f\"{severity_emoji} **{severity}** (Line {line}): {issue_desc}\")\n",
" if suggestion:\n",
" output.append(f\" 💡 *Fix:* {suggestion}\")\n",
" output.append(\"\")\n",
" \n",
" return \"\\n\".join(output)\n",
"\n",
"def format_improvements_output(result: Dict) -> str:\n",
" \"\"\"Format improvement suggestions for display.\"\"\"\n",
" if not result.get(\"success\"):\n",
" return f\"**Error with {result['model']}:** {result.get('error', 'Unknown error')}\"\n",
" \n",
" improvements = result.get(\"improvements\", [])\n",
" if not improvements:\n",
" return f\"✅ **{result['model']}**: Code follows best practices. No major improvements needed!\"\n",
" \n",
" output = [f\"**{result['model']}** - {len(improvements)} suggestion(s):\\n\"]\n",
" \n",
" for imp in improvements:\n",
" category = imp.get(\"category\", \"general\").replace(\"_\", \" \").title()\n",
" line = imp.get(\"line\", \"?\")\n",
" current = imp.get(\"current\", \"\")\n",
" improved = imp.get(\"improved\", \"\")\n",
" explanation = imp.get(\"explanation\", \"\")\n",
" \n",
" output.append(f\"\\n📝 **{category}** (Line {line}):\")\n",
" if current and improved:\n",
" output.append(f\" Before: `{current[:60]}{'...' if len(current) > 60 else ''}`\")\n",
" output.append(f\" After: `{improved[:60]}{'...' if len(improved) > 60 else ''}`\")\n",
" if explanation:\n",
" output.append(f\" 💡 {explanation}\")\n",
" \n",
" return \"\\n\".join(output)\n",
"\n",
"def format_tests_output(result: Dict) -> str:\n",
" \"\"\"Format test generation results for display.\"\"\"\n",
" if not result.get(\"success\"):\n",
" return f\"**Error with {result['model']}:** {result.get('error', 'Unknown error')}\"\n",
" \n",
" test_code = result.get(\"test_code\", \"\")\n",
" if not test_code:\n",
" return f\"⚠️ **{result['model']}**: No tests generated.\"\n",
" \n",
" return test_code\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def review_code(code: str, model_key: str, include_tests: bool = True) -> tuple:\n",
" \"\"\"Main function to perform complete code review.\"\"\"\n",
" if not code.strip():\n",
" return \"Please provide code to review.\", \"\", \"\"\n",
" \n",
" # Detect bugs\n",
" bugs_result = detect_bugs(code, model_key)\n",
" bugs_output = format_bugs_output(bugs_result)\n",
" bugs_issues = bugs_result.get(\"issues\", [])\n",
" \n",
" # Suggest improvements\n",
" improvements_result = suggest_improvements(code, model_key)\n",
" improvements_output = format_improvements_output(improvements_result)\n",
" \n",
" # Generate tests\n",
" tests_output = \"\"\n",
" if include_tests:\n",
" tests_result = generate_tests(code, bugs_issues, model_key)\n",
" tests_output = format_tests_output(tests_result)\n",
" \n",
" return bugs_output, improvements_output, tests_output\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"def compare_models(code: str, model_keys: List[str]) -> str:\n",
" \"\"\"Compare multiple models on the same code.\"\"\"\n",
" if not code.strip():\n",
" return \"Please provide code to review.\"\n",
" \n",
" results = []\n",
" all_issues = []\n",
" \n",
" for model_key in model_keys:\n",
" result = detect_bugs(code, model_key)\n",
" results.append(result)\n",
" if result.get(\"success\"):\n",
" all_issues.extend(result.get(\"issues\", []))\n",
" \n",
" # Build comparison output\n",
" output = [\"# Model Comparison Results\\n\"]\n",
" \n",
" for result in results:\n",
" model_name = result[\"model\"]\n",
" issues = result.get(\"issues\", [])\n",
" success = result.get(\"success\", False)\n",
" \n",
" if success:\n",
" output.append(f\"\\n**{model_name}**: Found {len(issues)} issue(s)\")\n",
" if issues:\n",
" severity_counts = {}\n",
" for issue in issues:\n",
" sev = issue.get(\"severity\", \"low\")\n",
" severity_counts[sev] = severity_counts.get(sev, 0) + 1\n",
" output.append(f\" Breakdown: {dict(severity_counts)}\")\n",
" else:\n",
" output.append(f\"\\n**{model_name}**: Error - {result.get('error', 'Unknown')}\")\n",
" \n",
" # Find consensus issues (found by multiple models)\n",
" if len(results) > 1:\n",
" issue_signatures = {}\n",
" for result in results:\n",
" if result.get(\"success\"):\n",
" for issue in result.get(\"issues\", []):\n",
" # Create signature from line and issue description\n",
" sig = f\"{issue.get('line')}-{issue.get('issue', '')[:50]}\"\n",
" if sig not in issue_signatures:\n",
" issue_signatures[sig] = []\n",
" issue_signatures[sig].append(result[\"model\"])\n",
" \n",
" consensus = [sig for sig, models in issue_signatures.items() if len(models) > 1]\n",
" if consensus:\n",
" output.append(f\"\\n\\n **Consensus Issues**: {len(consensus)} issue(s) identified by multiple models\")\n",
" \n",
" return \"\\n\".join(output)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Gradio UI\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7883\n",
"* To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7883/\" 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": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Example buggy code for testing\n",
"EXAMPLE_CODE = '''def divide_numbers(a, b):\n",
" return a / b\n",
"\n",
"def process_user_data(user_input):\n",
" # Missing input validation\n",
" result = eval(user_input)\n",
" return result\n",
"\n",
"def get_user_by_id(user_id):\n",
" # SQL injection vulnerability\n",
" query = f\"SELECT * FROM users WHERE id = {user_id}\"\n",
" return query\n",
"\n",
"def calculate_average(numbers):\n",
" total = sum(numbers)\n",
" return total / len(numbers) # Potential division by zero\n",
"'''\n",
"\n",
"def create_ui():\n",
" with gr.Blocks(title=\"AI Code Review Assistant\", theme=gr.themes.Soft()) as demo:\n",
" gr.Markdown(\"\"\"\n",
" # 🔍 AI-Powered Code Review Assistant\n",
" \n",
" Review your Python code using open-source AI models. Detect bugs, get improvement suggestions, and generate unit tests.\n",
" \"\"\")\n",
" \n",
" with gr.Row():\n",
" with gr.Column(scale=2):\n",
" code_input = gr.Code(\n",
" label=\"Python Code to Review\",\n",
" value=EXAMPLE_CODE,\n",
" language=\"python\",\n",
" lines=20\n",
" )\n",
" \n",
" with gr.Row():\n",
" model_selector = gr.Dropdown(\n",
" choices=list(MODELS.keys()),\n",
" value=list(MODELS.keys())[0],\n",
" label=\"Select Model\",\n",
" info=\"Choose an open-source code model\"\n",
" )\n",
" \n",
" include_tests = gr.Checkbox(\n",
" label=\"Generate Tests\",\n",
" value=True\n",
" )\n",
" \n",
" with gr.Row():\n",
" review_btn = gr.Button(\"🔍 Review Code\", variant=\"primary\", scale=2)\n",
" compare_btn = gr.Button(\"📊 Compare Models\", variant=\"secondary\", scale=1)\n",
" \n",
" with gr.Column(scale=3):\n",
" with gr.Tabs() as tabs:\n",
" with gr.Tab(\"🐛 Bug Detection\"):\n",
" bugs_output = gr.Markdown(value=\"Select a model and click 'Review Code' to analyze your code.\")\n",
" \n",
" with gr.Tab(\"✨ Improvements\"):\n",
" improvements_output = gr.Markdown(value=\"Get suggestions for code improvements and best practices.\")\n",
" \n",
" with gr.Tab(\"🧪 Unit Tests\"):\n",
" tests_output = gr.Code(\n",
" label=\"Generated Test Code\",\n",
" language=\"python\",\n",
" lines=25\n",
" )\n",
" \n",
" with gr.Tab(\"📊 Comparison\"):\n",
" comparison_output = gr.Markdown(value=\"Compare multiple models side-by-side.\")\n",
" \n",
" # Event handlers\n",
" review_btn.click(\n",
" fn=review_code,\n",
" inputs=[code_input, model_selector, include_tests],\n",
" outputs=[bugs_output, improvements_output, tests_output]\n",
" )\n",
" \n",
" def compare_selected_models(code):\n",
" # Compare first 3 models by default\n",
" model_keys = list(MODELS.keys())[:3]\n",
" return compare_models(code, model_keys)\n",
" \n",
" compare_btn.click(\n",
" fn=compare_selected_models,\n",
" inputs=[code_input],\n",
" outputs=[comparison_output]\n",
" )\n",
" \n",
" gr.Examples(\n",
" examples=[\n",
" [EXAMPLE_CODE],\n",
" [\"\"\"def fibonacci(n):\n",
" if n <= 1:\n",
" return n\n",
" return fibonacci(n-1) + fibonacci(n-2)\n",
"\"\"\"],\n",
" [\"\"\"def parse_config(file_path):\n",
" with open(file_path) as f:\n",
" return eval(f.read())\n",
"\"\"\"]\n",
" ],\n",
" inputs=[code_input]\n",
" )\n",
" \n",
" return demo\n",
"\n",
"demo = create_ui()\n",
"demo.launch(inbrowser=True, share=False)\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"**Qwen2.5-Coder 32B (OpenRouter)** - Found 2 issue(s):\n",
"\n",
"🔴 **CRITICAL** (Line 2): No division by zero protection\n",
" 💡 *Fix:* Add a check for b == 0 and raise ValueError or handle ZeroDivisionError\n",
"\n",
"🟡 **MEDIUM** (Line 2): No input validation for numeric types\n",
" 💡 *Fix:* Add type checking to ensure a and b are numbers (int/float)\n",
"\n"
]
}
],
"source": [
"# Test with a simple example\n",
"test_code = \"\"\"def divide(a, b):\n",
" return a / b\n",
"\"\"\"\n",
"\n",
"# Test bug detection\n",
"result = detect_bugs(test_code, list(MODELS.keys())[0])\n",
"print(format_bugs_output(result))\n"
]
}
],
"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": "3.12.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}