336 lines
10 KiB
Plaintext
336 lines
10 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "07bb451d-2b91-425f-b8ea-6f35ced780b0",
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"metadata": {},
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"source": [
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"# AI Code Commenting Assistant \n",
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"\n",
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"## Project Summary \n",
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"\n",
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"**Purpose**: \n",
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"An AI-powered assistant that automatically generates **clear, concise code comments** to improve code readability and maintainability. \n",
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"\n",
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"**Key Features**: \n",
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"- **Language-Agnostic**: Auto-detects programming languages or allows manual specification \n",
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"- **Smart Commenting**: Focuses on explaining **complex logic, algorithms, and edge cases** (not obvious syntax) \n",
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"- **Customizable**: Optional focus areas let users prioritize specific parts (e.g., database queries, recursion) \n",
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"- **Efficient Workflow**: Processes code in chunks and preserves original formatting \n",
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"\n",
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"**Benefits**: \n",
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"✔ Saves time writing documentation \n",
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"✔ Helps developers understand unfamiliar code \n",
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"✔ Supports multiple languages (Python, JavaScript, C++, SQL, etc.) \n",
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"✔ Avoids redundant comments on trivial operations \n",
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"\n",
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"**Example Use Case**: \n",
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"```python \n",
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"# Before AI: \n",
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"def fib(n): \n",
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" if n <= 1: return n \n",
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" else: return fib(n-1) + fib(n-2) \n",
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"\n",
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"# After AI: \n",
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"def fib(n): \n",
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" # Recursively computes nth Fibonacci number (O(2^n) time) \n",
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" if n <= 1: return n # Base case \n",
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" else: return fib(n-1) + fib(n-2) # Recursive case "
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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": "a0413ae1-0348-4884-ba95-384c4c8f841c",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install --upgrade huggingface_hub"
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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": "b22da766-042b-402f-9e05-78aa8f45ddd4",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import io\n",
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"from dotenv import load_dotenv\n",
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"from google import genai\n",
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"from google.genai import types\n",
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"from openai import OpenAI\n",
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"from anthropic import Anthropic\n",
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"from huggingface_hub import InferenceClient\n",
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"import gradio as gr"
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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": "5af6d3de-bab6-475e-b2f3-7b788bb2e529",
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"metadata": {},
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"outputs": [],
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"source": [
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"# load environments\n",
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"load_dotenv(override=True)\n",
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"os.environ['ANTHROPIC_API_KEY'] = os.getenv(\"CLAUDE_API_KEY\")\n",
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"os.environ[\"HF_TOKEN\"] = os.getenv(\"HF_TOKEN\")\n",
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"gemini_api_key= os.getenv(\"GEMINI_API_KEY\")"
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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": "cad0755e-4174-4fbc-84e6-15cc54bc609a",
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"metadata": {},
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"outputs": [],
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"source": [
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"#initialize remote models\n",
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"claude= Anthropic()\n",
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"gemini = genai.Client(api_key=gemini_api_key)\n",
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"\n",
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"#opensource models\n",
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"qwen = InferenceClient(provider=\"featherless-ai\")"
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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": "31d75812-1cd3-4512-8446-022c3357c354",
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"metadata": {},
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"outputs": [],
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"source": [
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"#initialize local model\n",
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"llama = OpenAI(base_url=\"http://localhost:11434/v1\", api_key=\"ollama\")"
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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": "31316379-2a56-4707-b207-ea60b490f536",
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"metadata": {},
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"outputs": [],
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"source": [
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"#models\n",
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"claude_model = \"claude-3-5-haiku-latest\"\n",
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"gemini_model = \"gemini-2.5-pro\"\n",
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"qwen_model= \"Qwen/Qwen2.5-Coder-32B-Instruct\"\n",
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"llama_model = \"llama3:8b\""
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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": "b7d9c4bf-0955-4406-8717-ffa7bdd0bec9",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_message=\"\"\"\n",
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"You are an expert AI specialized in code documentation. Your task is to generate concise, meaningful comments that explain the purpose and logic of provided code. Follow these rules:\n",
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"\n",
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"1. **Infer language**: Auto-detect programming language and use appropriate comment syntax\n",
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"2. **Explain why, not what**: Focus on purpose, edge cases, and non-obvious logic\n",
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"3. **Be concise**: Maximum 1-2 sentences per comment block\n",
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"4. **Prioritize key sections**: Only comment complex logic, algorithms, or critical operations\n",
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"5. **Maintain structure**: Preserve original code formatting and indentation\n",
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"6. **Output format**: Return ONLY commented code with no additional text\n",
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"\n",
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"Commenting guidelines by language:\n",
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"- Python: `# Inline comments` and `\"\"Docstrings\"\"`\n",
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"- JavaScript/Java: `// Line comments` and `/* Block comments */`\n",
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"- C/C++: `//` and `/* */`\n",
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"- SQL: `-- Line comments`\n",
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"\"\"\""
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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": "79dfe110-1523-40c7-ad90-2787ed22fd8d",
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"metadata": {},
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"outputs": [],
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"source": [
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"def user_prompt(code):\n",
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" prompt = f\"\"\"\n",
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" i want to document my code for better understanding. Please generate meaningful necessary comments\n",
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" here is my code:\n",
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" {code}\n",
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"\n",
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" Return ONLY commented code with no additional text\n",
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" \"\"\"\n",
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"\n",
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" return prompt"
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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": "c7bcf29e-ec78-4cfd-9b41-f2dc86400435",
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"metadata": {},
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"outputs": [],
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"source": [
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"def conversation_template(code):\n",
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" messages = [\n",
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" {\"role\":\"system\", \"content\":system_message},\n",
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" {\"role\":\"user\",\"content\":user_prompt(code)}\n",
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" ]\n",
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" return messages"
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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": "a36fec0f-7eba-4ccd-8fc4-cbf5ade76fa2",
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"metadata": {},
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"outputs": [],
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"source": [
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"def stream_gemini(code):\n",
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" message = user_prompt(code)\n",
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" response = gemini.models.generate_content_stream(\n",
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" model=gemini_model,\n",
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" config= types.GenerateContentConfig(\n",
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" system_instruction = system_message,\n",
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" temperature = 0.8,\n",
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" ),\n",
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" contents = [message]\n",
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" )\n",
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"\n",
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" result = \"\"\n",
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" for chunk in response:\n",
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" result += chunk.text or \"\"\n",
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" yield result"
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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": "e5d1e0c0-dc88-43ee-8698-82ad9ce7c51b",
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"metadata": {},
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"outputs": [],
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"source": [
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"def stream_claude(code):\n",
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" messages = [{\"role\":\"user\",\"content\":user_prompt(code)}]\n",
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" response = claude.messages.stream(\n",
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" model= claude_model,\n",
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" temperature=0.8,\n",
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" messages = messages,\n",
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" max_tokens=5000\n",
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" )\n",
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"\n",
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" result = \"\"\n",
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" with response as stream:\n",
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" for text in stream.text_stream:\n",
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" result += text or \"\"\n",
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" yield result\n",
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" "
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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": "903c97e5-9170-449e-8a0f-9f906351ec45",
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"metadata": {},
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"outputs": [],
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"source": [
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"def stream_opensource(code,model):\n",
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" model = model.lower()\n",
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" client = globals()[model]\n",
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" model = globals()[f\"{model}_model\"]\n",
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" stream = client.chat.completions.create(\n",
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" model = model,\n",
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" messages= conversation_template(code),\n",
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" temperature = 0.7,\n",
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" stream = True\n",
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" )\n",
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"\n",
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" result = \"\"\n",
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" for chunk in stream:\n",
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" result += chunk.choices[0].delta.content or \"\"\n",
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" yield result"
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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": "ff051c22-a2f8-4153-b970-f8a466a4cf5a",
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"metadata": {},
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"outputs": [],
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"source": [
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"def commentor(code, model):\n",
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" model =model.lower()\n",
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" if model == \"claude\":\n",
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" result = stream_claude(code)\n",
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" elif model == \"gemini\":\n",
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" result = stream_gemini(code)\n",
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" elif model == \"qwen\" or model == \"llama\":\n",
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" result = stream_opensource(code, model)\n",
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"\n",
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"\n",
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" for code in result:\n",
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" yield code.replace(\"```cpp\\n\",\"\").replace(\"```python\\n\",\"\").replace(\"```javascript\\n\",\"\").replace(\"```typescript\\n\",\"\").replace(\"```\",\"\")"
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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": "10daf070-3546-4073-a2a0-3f5f8fc156f0",
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"metadata": {},
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"outputs": [],
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"source": [
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"with gr.Blocks() as ui:\n",
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" gr.Markdown(\"# Genarate comment\")\n",
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" with gr.Row():\n",
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" raw_code = gr.Textbox(label=\"Raw Code:\", lines=10)\n",
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" commented_code = gr.Textbox(label=\"Commented_code\",lines=10)\n",
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" with gr.Row():\n",
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" models = gr.Dropdown([\"Gemini\",\"Claude\",\"Llama\",\"Qwen\"], value=\"Gemini\")\n",
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" with gr.Row():\n",
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" generate_comment = gr.Button(\"Generate Comment\")\n",
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"\n",
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" generate_comment.click(commentor, inputs=[raw_code, models], outputs=[commented_code])"
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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": "afb87f32-f25e-40c5-844a-d2b7af748192",
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"metadata": {},
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"outputs": [],
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"source": [
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"ui.launch(inbrowser=True,debug=True)"
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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": "96bc48ad-10ad-4821-b58e-ea1b22cdcdc9",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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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": "3.11.13"
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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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