Real-time AI Code Collaboration Tools

import asyncio
import websockets
import json
from typing import Dict, List
import openai

class RealTimeAICollaborator:
    def __init__(self, api_key: str):
        openai.api_key = api_key
        self.active_sessions = {}
        self.code_context = {}

    async def handle_connection(self, websocket, path):
        """Handle new collaborative session"""
        session_id = path.split('/')[-1]
        self.active_sessions[session_id] = {
            'websocket': websocket,
            'participants': [],
            'current_code': '',
            'ai_enabled': True
        }

        try:
            async for message in websocket:
                await self.handle_message(session_id, message)
        finally:
            del self.active_sessions[session_id]

    async def handle_message(self, session_id: str, message: str):
        """Process incoming collaboration messages"""
        data = json.loads(message)
        message_type = data.get('type')

        if message_type == 'code_change':
            await self.handle_code_change(session_id, data)
        elif message_type == 'ai_suggestion_request':
            await self.handle_ai_suggestion(session_id, data)
        elif message_type == 'context_update':
            await self.handle_context_update(session_id, data)

    async def handle_code_change(self, session_id: str, data: Dict):
        """Handle real-time code changes"""
        session = self.active_sessions[session_id]
        session['current_code'] = data['code']

        # Broadcast to all participants
        await self.broadcast_to_session(session_id, {
            'type': 'code_update',
            'code': data['code'],
            'author': data['author'],
            'timestamp': data['timestamp']
        })

        # Generate AI suggestions if enabled
        if session['ai_enabled']:
            suggestions = await self.generate_ai_suggestions(
                session['current_code'],
                data.get('cursor_position', 0)
            )

            await self.broadcast_to_session(session_id, {
                'type': 'ai_suggestions',
                'suggestions': suggestions,
                'context': 'code_change'
            })

    async def generate_ai_suggestions(self, code: str, cursor_position: int) -> List[Dict]:
        """Generate contextual AI suggestions"""

        # Analyze code context around cursor
        lines = code.split('\n')
        current_line = self._get_line_from_position(code, cursor_position)
        context_lines = self._get_context_lines(lines, current_line, 5)

        prompt = f"""
        Analyze this code and provide helpful suggestions:

        Current context (line {current_line}):
        {chr(10).join(context_lines)}

        Cursor position: {cursor_position}

        Provide suggestions for:
        1. Code completion
        2. Potential improvements
        3. Best practices
        4. Error detection

        Format as JSON array with type and description.
        """

        response = await openai.ChatCompletion.acreate(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )

        try:
            suggestions = json.loads(response.choices[0].message.content)
            return suggestions
        except:
            return [{"type": "info", "description": "AI suggestion parsing failed"}]

    async def broadcast_to_session(self, session_id: str, message: Dict):
        """Broadcast message to all session participants"""
        if session_id in self.active_sessions:
            websocket = self.active_sessions[session_id]['websocket']
            await websocket.send(json.dumps(message))

# WebSocket server setup
async def start_collaboration_server():
    collaborator = RealTimeAICollaborator("your-openai-key")

    start_server = websockets.serve(
        collaborator.handle_connection,
        "localhost",
        8765
    )

    await start_server
    print("AI Collaboration server started on ws://localhost:8765")

# Run the server
# asyncio.get_event_loop().run_until_complete(start_collaboration_server())

class AIConflictResolver:
    def __init__(self):
        self.conflict_patterns = []

    def resolve_merge_conflict(self, base_code: str, branch_a: str, branch_b: str) -> str:
        """Use AI to suggest conflict resolutions"""

        prompt = f"""
        Help resolve this merge conflict intelligently:

        Base code:
        {base_code}

        Branch A changes:
        {branch_a}

        Branch B changes:
        {branch_b}

        Provide:
        1. Analysis of the conflicting changes
        2. Recommended resolution that preserves both intents
        3. Potential issues with the resolution
        4. The resolved code
        """

        response = openai.ChatCompletion.create(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )

        return self._parse_resolution(response.choices[0].message.content)

    def suggest_collaboration_improvements(self, session_history: List[Dict]) -> List[str]:
        """Analyze collaboration patterns and suggest improvements"""

        # Extract patterns from session history
        patterns = self._analyze_patterns(session_history)

        prompt = f"""
        Based on this collaborative coding session analysis:

        {self._format_patterns(patterns)}

        Suggest improvements for team collaboration:
        1. Code organization recommendations
        2. Communication improvements
        3. Workflow optimizations
        4. Tool setup suggestions
        """

        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": prompt}]
        )

        return self._extract_suggestions(response.choices[0].message.content)

# Conflict resolution example
resolver = AIConflictResolver()

base_code = """
def calculate_price(item, discount=0):
    return item.price * (1 - discount)
"""

branch_a = """
def calculate_price(item, discount=0, tax_rate=0.1):
    base_price = item.price * (1 - discount)
    return base_price * (1 + tax_rate)
"""

branch_b = """
def calculate_price(item, discount=0):
    if discount > 0.5:
        raise ValueError("Discount cannot exceed 50%")
    return item.price * (1 - discount)
"""

resolved_code = resolver.resolve_merge_conflict(base_code, branch_a, branch_b)

// Frontend JavaScript for collaborative code review
class CollaborativeReviewAI {
  constructor(apiKey) {
    this.apiKey = apiKey;
    this.reviewSessions = new Map();
  }

  async startReviewSession(pullRequestId, code, participants) {
    const session = {
      id: pullRequestId,
      code: code,
      participants: participants,
      comments: [],
      aiSuggestions: [],
    };

    this.reviewSessions.set(pullRequestId, session);

    // Generate initial AI review
    const aiReview = await this.generateAIReview(code);
    session.aiSuggestions = aiReview;

    // Notify participants
    this.notifyParticipants(pullRequestId, {
      type: "review_started",
      aiSuggestions: aiReview,
    });

    return session;
  }

  async generateAIReview(code) {
    const response = await fetch("/api/ai-review", {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
        Authorization: `Bearer ${this.apiKey}`,
      },
      body: JSON.stringify({
        code: code,
        reviewType: "comprehensive",
        focusAreas: ["security", "performance", "maintainability", "testing", "documentation"],
      }),
    });

    return response.json();
  }

  async addHumanComment(sessionId, comment, lineNumber) {
    const session = this.reviewSessions.get(sessionId);
    if (!session) return;

    const newComment = {
      id: Date.now(),
      author: comment.author,
      content: comment.content,
      lineNumber: lineNumber,
      timestamp: new Date().toISOString(),
      type: "human",
    };

    session.comments.push(newComment);

    // Get AI perspective on the comment
    const aiResponse = await this.getAICommentResponse(comment.content, session.code, lineNumber);

    if (aiResponse) {
      session.comments.push({
        id: Date.now() + 1,
        author: "AI Assistant",
        content: aiResponse,
        lineNumber: lineNumber,
        timestamp: new Date().toISOString(),
        type: "ai_response",
      });
    }

    // Broadcast to all participants
    this.notifyParticipants(sessionId, {
      type: "comment_added",
      comment: newComment,
      aiResponse: aiResponse,
    });
  }

  async getAICommentResponse(humanComment, code, lineNumber) {
    const response = await fetch("/api/ai-comment-response", {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
        Authorization: `Bearer ${this.apiKey}`,
      },
      body: JSON.stringify({
        humanComment: humanComment,
        code: code,
        lineNumber: lineNumber,
        context: "code_review",
      }),
    });

    const result = await response.json();
    return result.response;
  }

  notifyParticipants(sessionId, message) {
    const session = this.reviewSessions.get(sessionId);
    if (!session) return;

    // WebSocket or other real-time notification
    session.participants.forEach((participant) => {
      this.sendToParticipant(participant.id, message);
    });
  }
}

// Usage
const reviewAI = new CollaborativeReviewAI("your-api-key");

// Start a review session
const session = await reviewAI.startReviewSession("pr-123", codeToReview, [
  { id: "user1", name: "Alice" },
  { id: "user2", name: "Bob" },
]);

// Add human comment with AI assistance
await reviewAI.addHumanComment(
  "pr-123",
  {
    author: "Alice",
    content: "This function seems overly complex. Could we simplify it?",
  },
  45
);