Code Assistants

from transformers import AutoModelForCausalLM, AutoTokenizer
import ast

class CodeAssistant:
    def __init__(self, model_name="codellama/CodeLlama-7b-Python-hf"):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForCausalLM.from_pretrained(model_name)
    
    def generate_code(self, prompt, context="", max_length=256):
        # Construct prompt with context
        full_prompt = f"""
        Context:
        {context}
        
        Task: {prompt}
        
        Code:
        """
        
        # Tokenize and generate
        inputs = self.tokenizer(full_prompt, return_tensors="pt")
        outputs = self.model.generate(
            **inputs,
            max_length=max_length,
            temperature=0.7,
            do_sample=True
        )
        
        # Decode and extract code
        generated = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        code = self.extract_code(generated)
        
        return code
    
    def extract_code(self, text):
        # Extract code block from generated text
        lines = text.split('\n')
        code_lines = []
        in_code = False
        
        for line in lines:
            if '```' in line:
                in_code = not in_code
            elif in_code:
                code_lines.append(line)
        
        return '\n'.join(code_lines)
    
    def validate_syntax(self, code):
        try:
            ast.parse(code)
            return True, "Valid Python syntax"
        except SyntaxError as e:
            return False, str(e)

import {
    createConnection,
    TextDocuments,
    CompletionItem,
    CompletionItemKind,
    TextDocumentPositionParams
} from 'vscode-languageserver/node';

const connection = createConnection();
const documents = new TextDocuments();

connection.onCompletion(
    async (params: TextDocumentPositionParams): Promise => {
        const document = documents.get(params.textDocument.uri);
        const position = params.position;
        
        // Get context around cursor
        const context = getContext(document, position);
        
        // Generate completions using AI model
        const suggestions = await aiModel.complete(context);
        
        // Convert to LSP completion items
        return suggestions.map(s => ({
            label: s.text,
            kind: CompletionItemKind.Function,
            detail: s.description,
            insertText: s.code,
            documentation: s.documentation
        }));
    }
);

connection.listen();

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import asyncio

app = FastAPI()

class CodeRequest(BaseModel):
    prompt: str
    language: str
    context: str = ""
    max_tokens: int = 256

class CodeResponse(BaseModel):
    code: str
    explanation: str
    confidence: float

@app.post("/generate", response_model=CodeResponse)
async def generate_code(request: CodeRequest):
    try:
        # Generate code
        code = await assistant.generate(
            prompt=request.prompt,
            language=request.language,
            context=request.context
        )
        
        # Validate generated code
        is_valid, errors = validate_code(code, request.language)
        
        # Generate explanation
        explanation = await assistant.explain(code)
        
        return CodeResponse(
            code=code,
            explanation=explanation,
            confidence=0.95 if is_valid else 0.5
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/refactor")
async def refactor_code(request: CodeRequest):
    # Analyze and improve existing code
    improvements = await assistant.suggest_improvements(request.context)
    return {"suggestions": improvements}