custom_pathway.py

from typing import Any, Dict, List
import asyncio
from dataclasses import dataclass

@dataclass
class DataStream:
    data: Any

    def transform(self, func):
        return DataStream(func(self.data))

    def run(self):
        return self.data

class PathwayAnalyzer:
    def __init__(self, llm):
        self.llm = llm
    
    def process_metrics(self, metrics: Dict) -> Dict:
        """Process health metrics through a pathway-like pipeline"""
        stream = DataStream(metrics)
        
        return (stream
                .transform(self._normalize_metrics)
                .transform(self._analyze_trends)
                .transform(self._generate_insights)
                .run())
    
    def _normalize_metrics(self, data: Dict) -> Dict:
        """Normalize health metrics"""
        normalized = {}
        for key, value in data['real_time'].items():
            if isinstance(value, (int, float)):
                normalized[key] = (value - 0) / (100 - 0)  # Simple normalization
        data['normalized'] = normalized
        return data
    
    def _analyze_trends(self, data: Dict) -> Dict:
        """Analyze trends in metrics"""
        trends = {}
        for key, value in data['normalized'].items():
            if value > 0.7:
                trends[key] = "High"
            elif value < 0.3:
                trends[key] = "Low"
            else:
                trends[key] = "Normal"
        data['trends'] = trends
        return data
    
    def _generate_insights(self, data: Dict) -> Dict:
        """Generate health insights"""
        insights = []
        for metric, trend in data['trends'].items():
            if trend != "Normal":
                insights.append(f"{metric.replace('_', ' ').title()} is {trend}")
        data['insights'] = insights
        return data