tikker/src/api/ai_service.py

"""
Tikker AI Microservice

Provides AI-powered analysis of keystroke data using OpenAI API.
Handles text analysis, pattern detection, and insights generation.
"""

from fastapi import FastAPI, HTTPException, Query
from pydantic import BaseModel
from typing import Dict, Any, Optional, List
import logging
import os

try:
    from openai import OpenAI
except ImportError:
    OpenAI = None

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = FastAPI(
    title="Tikker AI Service",
    description="AI analysis for keystroke data",
    version="1.0.0"
)

client = None
api_key = os.getenv("OPENAI_API_KEY")

if api_key:
    try:
        client = OpenAI(api_key=api_key)
    except Exception as e:
        logger.error(f"Failed to initialize OpenAI client: {e}")


class TextAnalysisRequest(BaseModel):
    text: str
    analysis_type: str = "general"


class AnalysisResult(BaseModel):
    text: str
    analysis_type: str
    summary: str
    keywords: List[str]
    sentiment: Optional[str] = None
    insights: List[str]


class HealthResponse(BaseModel):
    status: str
    ai_available: bool
    api_version: str


@app.get("/health", response_model=HealthResponse)
async def health_check() -> HealthResponse:
    """Health check endpoint."""
    return HealthResponse(
        status="healthy",
        ai_available=client is not None,
        api_version="1.0.0"
    )


@app.post("/analyze", response_model=AnalysisResult)
async def analyze_text(request: TextAnalysisRequest) -> AnalysisResult:
    """
    Analyze text using AI.
    
    Args:
        request: Text analysis request with text and analysis type
    
    Returns:
        Analysis result with summary, keywords, and insights
    """
    if not client:
        raise HTTPException(
            status_code=503,
            detail="AI service not available - no API key configured"
        )
    
    if not request.text or len(request.text.strip()) == 0:
        raise HTTPException(status_code=400, detail="Text cannot be empty")
    
    try:
        analysis_type = request.analysis_type.lower()
        
        if analysis_type == "activity":
            prompt = f"""Analyze this keystroke activity log and provide:
1. A brief summary (1-2 sentences)
2. Key patterns or observations (3-4 bullet points)
3. Sentiment or work intensity assessment

Text: {request.text}

Respond in JSON format with keys: summary, keywords (list), insights (list), sentiment"""
        elif analysis_type == "productivity":
            prompt = f"""Analyze this text for productivity patterns and provide:
1. Summary of productivity indicators
2. Key terms related to productivity
3. Specific insights about work patterns

Text: {request.text}

Respond in JSON format with keys: summary, keywords (list), insights (list)"""
        else:
            prompt = f"""Provide a general analysis of this text:
1. Brief summary (1-2 sentences)
2. Important keywords or themes
3. Key insights

Text: {request.text}

Respond in JSON format with keys: summary, keywords (list), insights (list)"""
        
        response = client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[
                {"role": "system", "content": "You are a helpful analyst. Always respond in valid JSON format."},
                {"role": "user", "content": prompt}
            ],
            temperature=0.7,
            max_tokens=500
        )
        
        result_text = response.choices[0].message.content
        
        import json
        try:
            parsed = json.loads(result_text)
        except:
            parsed = {
                "summary": result_text[:100],
                "keywords": ["analysis"],
                "insights": [result_text]
            }
        
        return AnalysisResult(
            text=request.text,
            analysis_type=analysis_type,
            summary=parsed.get("summary", ""),
            keywords=parsed.get("keywords", []),
            sentiment=parsed.get("sentiment"),
            insights=parsed.get("insights", [])
        )
        
    except Exception as e:
        logger.error(f"Analysis error: {e}")
        raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")


@app.get("/")
async def root() -> Dict[str, Any]:
    """Root endpoint with service information."""
    return {
        "name": "Tikker AI Service",
        "version": "1.0.0",
        "status": "running",
        "ai_available": client is not None,
        "endpoints": {
            "health": "/health",
            "analyze": "/analyze"
        }
    }


if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8001)