models.py

"""Data models for PageSpeed Insights analysis."""
from dataclasses import dataclass
from typing import List, Optional, Dict, Any
from datetime import datetime


@dataclass
class Opportunity:
    """Represents a performance optimization opportunity."""
    title: str
    description: str
    savings: float


@dataclass
class PageSpeedData:
    """Contains PageSpeed Insights analysis data for a single URL."""
    url_original: str
    fetch_time: str
    device: str
    performance_score: float
    accessibility_score: float
    best_practices_score: float
    seo_score: float
    fcp: float
    lcp: float
    tbt: float
    cls: float
    speed_index: float
    opportunities: List[Opportunity]

    @classmethod
    def from_lighthouse_data(cls, lighthouse_data: Dict[str, Any], site_url: str, strategy: str) -> 'PageSpeedData':
        """Create PageSpeedData from Lighthouse API response."""
        audits = lighthouse_data.get('audits', {})
        categories = lighthouse_data.get('categories', {})

        opportunities = []
        for audit_id, audit_data in audits.items():
            if audit_data.get('details', {}).get('type') == 'opportunity':
                if audit_data.get('score', 1) < 1:
                    opportunities.append(Opportunity(
                        title=audit_data.get('title', ''),
                        description=audit_data.get('description', ''),
                        savings=audit_data.get('details', {}).get('overallSavingsMs', 0) / 1000
                    ))

        return cls(
            url_original=lighthouse_data.get('finalDisplayedUrl', site_url),
            fetch_time=lighthouse_data.get('fetchTime', datetime.now().isoformat()),
            device=strategy,
            performance_score=categories.get('performance', {}).get('score', 0) * 100,
            accessibility_score=categories.get('accessibility', {}).get('score', 0) * 100,
            best_practices_score=categories.get('best-practices', {}).get('score', 0) * 100,
            seo_score=categories.get('seo', {}).get('score', 0) * 100,
            fcp=audits.get('first-contentful-paint', {}).get('numericValue', 0) / 1000,
            lcp=audits.get('largest-contentful-paint', {}).get('numericValue', 0) / 1000,
            tbt=audits.get('total-blocking-time', {}).get('numericValue', 0),
            cls=audits.get('cumulative-layout-shift', {}).get('numericValue', 0),
            speed_index=audits.get('speed-index', {}).get('numericValue', 0) / 1000,
            opportunities=opportunities
        )


@dataclass
class MetricComparison:
    """Comparison of a single metric between two analyses."""
    antes: float
    depois: float
    diferenca: float


@dataclass
class ComparisonResult:
    """Complete comparison result between two PageSpeed analyses."""
    performance: MetricComparison
    accessibility: MetricComparison
    best_practices: MetricComparison
    seo: MetricComparison
    fcp: MetricComparison
    lcp: MetricComparison
    tbt: MetricComparison
    cls: MetricComparison
    speed_index: MetricComparison

    @classmethod
    def from_data(cls, data1: PageSpeedData, data2: PageSpeedData) -> 'ComparisonResult':
        """Create comparison result from two PageSpeedData objects."""
        return cls(
            performance=MetricComparison(
                antes=data1.performance_score,
                depois=data2.performance_score,
                diferenca=data2.performance_score - data1.performance_score
            ),
            accessibility=MetricComparison(
                antes=data1.accessibility_score,
                depois=data2.accessibility_score,
                diferenca=data2.accessibility_score - data1.accessibility_score
            ),
            best_practices=MetricComparison(
                antes=data1.best_practices_score,
                depois=data2.best_practices_score,
                diferenca=data2.best_practices_score - data1.best_practices_score
            ),
            seo=MetricComparison(
                antes=data1.seo_score,
                depois=data2.seo_score,
                diferenca=data2.seo_score - data1.seo_score
            ),
            fcp=MetricComparison(
                antes=round(data1.fcp, 2),
                depois=round(data2.fcp, 2),
                diferenca=round(data2.fcp - data1.fcp, 2)
            ),
            lcp=MetricComparison(
                antes=round(data1.lcp, 2),
                depois=round(data2.lcp, 2),
                diferenca=round(data2.lcp - data1.lcp, 2)
            ),
            tbt=MetricComparison(
                antes=round(data1.tbt, 0),
                depois=round(data2.tbt, 0),
                diferenca=round(data2.tbt - data1.tbt, 0)
            ),
            cls=MetricComparison(
                antes=round(data1.cls, 3),
                depois=round(data2.cls, 3),
                diferenca=round(data2.cls - data1.cls, 3)
            ),
            speed_index=MetricComparison(
                antes=round(data1.speed_index, 2),
                depois=round(data2.speed_index, 2),
                diferenca=round(data2.speed_index - data1.speed_index, 2)
            )
        )

    def to_dict(self) -> Dict[str, Dict[str, float]]:
        """Convert comparison result to dictionary format for display."""
        return {
            'Performance': {
                'antes': self.performance.antes,
                'depois': self.performance.depois,
                'diferenca': self.performance.diferenca
            },
            'Accessibility': {
                'antes': self.accessibility.antes,
                'depois': self.accessibility.depois,
                'diferenca': self.accessibility.diferenca
            },
            'Best Practices': {
                'antes': self.best_practices.antes,
                'depois': self.best_practices.depois,
                'diferenca': self.best_practices.diferenca
            },
            'SEO': {
                'antes': self.seo.antes,
                'depois': self.seo.depois,
                'diferenca': self.seo.diferenca
            },
            'FCP (s)': {
                'antes': self.fcp.antes,
                'depois': self.fcp.depois,
                'diferenca': self.fcp.diferenca
            },
            'LCP (s)': {
                'antes': self.lcp.antes,
                'depois': self.lcp.depois,
                'diferenca': self.lcp.diferenca
            },
            'TBT (ms)': {
                'antes': self.tbt.antes,
                'depois': self.tbt.depois,
                'diferenca': self.tbt.diferenca
            },
            'CLS': {
                'antes': self.cls.antes,
                'depois': self.cls.depois,
                'diferenca': self.cls.diferenca
            },
            'Speed Index (s)': {
                'antes': self.speed_index.antes,
                'depois': self.speed_index.depois,
                'diferenca': self.speed_index.diferenca
            }
        }