Initial commit for Greenlens
This commit is contained in:
Timo Knuth
564
services/backend/openAiScanService.ts
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564
services/backend/openAiScanService.ts
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@@ -0,0 +1,564 @@
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import { CareInfo, IdentificationResult, Language } from '../../types';
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type OpenAiScanMode = 'primary' | 'review';
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export interface OpenAiHealthIssue {
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title: string;
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confidence: number;
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details: string;
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}
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export interface OpenAiHealthAnalysis {
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overallHealthScore: number;
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status: 'healthy' | 'watch' | 'critical';
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likelyIssues: OpenAiHealthIssue[];
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actionsNow: string[];
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plan7Days: string[];
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}
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const OPENAI_API_KEY = (process.env.EXPO_PUBLIC_OPENAI_API_KEY || '').trim();
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const OPENAI_SCAN_MODEL = (process.env.EXPO_PUBLIC_OPENAI_SCAN_MODEL || 'gpt-5-mini').trim();
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const OPENAI_SCAN_MODEL_PRO = (process.env.EXPO_PUBLIC_OPENAI_SCAN_MODEL_PRO || OPENAI_SCAN_MODEL).trim();
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const OPENAI_HEALTH_MODEL = (process.env.EXPO_PUBLIC_OPENAI_HEALTH_MODEL || OPENAI_SCAN_MODEL).trim();
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const OPENAI_SCAN_FALLBACK_MODELS = (process.env.EXPO_PUBLIC_OPENAI_SCAN_FALLBACK_MODELS || 'gpt-5-mini,gpt-4.1-mini').trim();
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const OPENAI_SCAN_FALLBACK_MODELS_PRO = (process.env.EXPO_PUBLIC_OPENAI_SCAN_FALLBACK_MODELS_PRO || OPENAI_SCAN_FALLBACK_MODELS).trim();
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const OPENAI_HEALTH_FALLBACK_MODELS = (process.env.EXPO_PUBLIC_OPENAI_HEALTH_FALLBACK_MODELS || OPENAI_SCAN_FALLBACK_MODELS).trim();
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const OPENAI_CHAT_COMPLETIONS_URL = 'https://api.openai.com/v1/chat/completions';
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const OPENAI_TIMEOUT_MS = (() => {
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const raw = (process.env.EXPO_PUBLIC_OPENAI_TIMEOUT_MS || '45000').trim();
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const parsed = Number.parseInt(raw, 10);
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if (Number.isFinite(parsed) && parsed >= 10000) return parsed;
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return 45000;
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})();
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const parseModelChain = (primaryModel: string, fallbackModels: string): string[] => {
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const models = [primaryModel];
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fallbackModels.split(',').forEach((model) => {
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const normalized = model.trim();
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if (normalized) models.push(normalized);
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});
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return [...new Set(models)];
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};
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const OPENAI_SCAN_MODEL_CHAIN = parseModelChain(OPENAI_SCAN_MODEL, OPENAI_SCAN_FALLBACK_MODELS);
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const OPENAI_SCAN_MODEL_CHAIN_PRO = parseModelChain(OPENAI_SCAN_MODEL_PRO, OPENAI_SCAN_FALLBACK_MODELS_PRO);
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const OPENAI_HEALTH_MODEL_CHAIN = parseModelChain(OPENAI_HEALTH_MODEL, OPENAI_HEALTH_FALLBACK_MODELS);
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const getScanModelChain = (plan: 'free' | 'pro'): string[] => {
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return plan === 'pro' ? OPENAI_SCAN_MODEL_CHAIN_PRO : OPENAI_SCAN_MODEL_CHAIN;
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};
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const clamp = (value: number, min: number, max: number): number => {
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return Math.min(max, Math.max(min, value));
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};
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const toErrorMessage = (error: unknown): string => {
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if (error instanceof Error) return error.message;
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return String(error);
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};
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const summarizeImageUri = (imageUri: string): string => {
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const trimmed = imageUri.trim();
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if (!trimmed) return 'empty';
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if (trimmed.startsWith('data:image')) return `data-uri(${Math.round(trimmed.length / 1024)}kb)`;
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return trimmed.length > 120 ? `${trimmed.slice(0, 120)}...` : trimmed;
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};
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const toJsonString = (content: string): string => {
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const trimmed = content.trim();
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if (!trimmed) return trimmed;
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const fenced = trimmed.match(/^```(?:json)?\s*([\s\S]*?)\s*```$/i);
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if (fenced?.[1]) return fenced[1].trim();
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return trimmed;
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};
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const parseContentToJson = (content: string): Record<string, unknown> | null => {
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try {
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const parsed = JSON.parse(toJsonString(content));
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if (parsed && typeof parsed === 'object' && !Array.isArray(parsed)) {
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return parsed as Record<string, unknown>;
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}
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return null;
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} catch {
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return null;
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}
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};
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const getString = (value: unknown): string => {
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return typeof value === 'string' ? value.trim() : '';
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};
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const getNumber = (value: unknown): number | null => {
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if (typeof value === 'number' && Number.isFinite(value)) return value;
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if (typeof value === 'string' && value.trim()) {
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const parsed = Number(value);
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if (Number.isFinite(parsed)) return parsed;
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}
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return null;
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};
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const getStringArray = (value: unknown): string[] => {
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if (!Array.isArray(value)) return [];
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return value
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.map((item) => (typeof item === 'string' ? item.trim() : ''))
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.filter(Boolean);
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};
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const normalizeResult = (
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raw: Record<string, unknown>,
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language: Language,
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): IdentificationResult | null => {
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const name = getString(raw.name);
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const botanicalName = getString(raw.botanicalName);
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const description = getString(raw.description);
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const confidenceRaw = getNumber(raw.confidence);
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const careInfoRaw = raw.careInfo;
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if (!name || !botanicalName || !careInfoRaw || typeof careInfoRaw !== 'object' || Array.isArray(careInfoRaw)) {
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return null;
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}
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const careInfoObj = careInfoRaw as Record<string, unknown>;
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const waterIntervalRaw = getNumber(careInfoObj.waterIntervalDays);
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const light = getString(careInfoObj.light);
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const temp = getString(careInfoObj.temp);
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if (waterIntervalRaw == null || !light || !temp) {
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return null;
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}
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const fallbackDescription = language === 'de'
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? `${name} wurde per KI erkannt. Pflegehinweise sind unten aufgefuehrt.`
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: language === 'es'
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? `${name} se detecto con IA. Debajo veras recomendaciones de cuidado.`
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: `${name} was identified with AI. Care guidance is shown below.`;
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return {
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name,
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botanicalName,
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confidence: clamp(confidenceRaw ?? 0.72, 0.05, 0.99),
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description: description || fallbackDescription,
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careInfo: {
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waterIntervalDays: Math.round(clamp(waterIntervalRaw, 1, 45)),
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light,
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temp,
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},
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};
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};
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const getLanguageLabel = (language: Language): string => {
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if (language === 'de') return 'German';
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if (language === 'es') return 'Spanish';
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return 'English';
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};
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const buildPrompt = (language: Language, mode: OpenAiScanMode): string => {
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const reviewInstruction = mode === 'review'
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? 'Re-check your first hypothesis with stricter botanical accuracy and correct any mismatch.'
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: 'Identify the most likely houseplant species from this image with conservative confidence.';
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const nameLanguageInstruction = language === 'en'
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? '- "name" must be an English common name only. Never return a German or other non-English common name. If no reliable English common name is known, use "botanicalName" as "name" instead of inventing or translating.'
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: `- "name" must be strictly written in ${getLanguageLabel(language)}. If a reliable common name in that language is not known, use "botanicalName" as "name" instead of inventing a localized name.`;
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return [
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`${reviewInstruction}`,
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`Return strict JSON only in this shape:`,
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`{"name":"...","botanicalName":"...","confidence":0.0,"description":"...","careInfo":{"waterIntervalDays":7,"light":"...","temp":"..."}}`,
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`Rules:`,
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nameLanguageInstruction,
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`- "description" and "careInfo.light" must be written in ${getLanguageLabel(language)}.`,
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`- "botanicalName" must use accepted Latin scientific naming and must not be invented or misspelled.`,
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`- If species is uncertain, prefer genus-level naming (for example: "Calathea sp.").`,
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`- "confidence" must be between 0 and 1.`,
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`- Keep confidence <= 0.55 when the image is ambiguous, blurred, or partially visible.`,
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`- "waterIntervalDays" must be an integer between 1 and 45.`,
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`- Do not include markdown, explanations, or extra keys.`,
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].join('\n');
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};
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const buildHealthPrompt = (
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language: Language,
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plantContext?: {
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name: string;
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botanicalName: string;
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careInfo: CareInfo;
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description?: string;
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},
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): string => {
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const contextLines = plantContext
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? [
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`Plant context:`,
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`- name: ${plantContext.name}`,
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`- botanicalName: ${plantContext.botanicalName}`,
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`- care.light: ${plantContext.careInfo.light}`,
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`- care.temp: ${plantContext.careInfo.temp}`,
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`- care.waterIntervalDays: ${plantContext.careInfo.waterIntervalDays}`,
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`- description: ${plantContext.description || 'n/a'}`,
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]
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: ['Plant context: not provided'];
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return [
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`Analyze this plant photo for real health condition signs with focus on yellowing leaves, watering stress, pests, and light stress.`,
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`Return strict JSON only in this shape:`,
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`{"overallHealthScore":72,"status":"watch","likelyIssues":[{"title":"...","confidence":0.64,"details":"..."}],"actionsNow":["..."],"plan7Days":["..."]}`,
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`Rules:`,
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`- "overallHealthScore" must be an integer between 0 and 100.`,
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`- "status" must be one of: "healthy", "watch", "critical".`,
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`- "likelyIssues" must contain 1 to 4 items sorted by confidence descending.`,
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`- "confidence" must be between 0 and 1.`,
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`- "title", "details", "actionsNow", and "plan7Days" must be written in ${getLanguageLabel(language)}.`,
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`- "actionsNow" should be immediate steps for the next 24 hours.`,
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`- "plan7Days" should be short actionable steps for the next week.`,
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`- Do not include markdown, explanations, or extra keys.`,
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...contextLines,
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].join('\n');
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};
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const buildFallbackHealthAnalysis = (
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language: Language,
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plantContext?: {
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name: string;
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botanicalName: string;
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careInfo: CareInfo;
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description?: string;
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},
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): OpenAiHealthAnalysis => {
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if (language === 'de') {
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return {
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overallHealthScore: 58,
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status: 'watch',
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likelyIssues: [
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{
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title: 'Eingeschraenkte KI-Analyse',
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confidence: 0.42,
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details: `${plantContext?.name || 'Die Pflanze'} konnte wegen instabiler Antwort nicht vollstaendig bewertet werden.`,
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},
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],
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actionsNow: [
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'Neues Foto bei hellem, indirektem Licht aufnehmen.',
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'Blaetter auf Flecken, Schaedlinge und trockene Raender pruefen.',
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'Erst giessen, wenn die oberen 2-3 cm Erde trocken sind.',
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],
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plan7Days: [
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'In 2 Tagen mit neuem Foto erneut pruefen.',
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'Farbe und Blattspannung taeglich beobachten.',
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'Bei Verschlechterung Standort und Giessrhythmus anpassen.',
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],
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};
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}
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if (language === 'es') {
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return {
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overallHealthScore: 58,
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status: 'watch',
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likelyIssues: [
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{
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title: 'Analisis de IA limitado',
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confidence: 0.42,
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details: `${plantContext?.name || 'La planta'} no pudo evaluarse por completo por una respuesta inestable.`,
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},
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],
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actionsNow: [
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'Tomar una foto nueva con luz brillante e indirecta.',
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'Revisar hojas por manchas, plagas y bordes secos.',
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'Regar solo si los 2-3 cm superiores del sustrato estan secos.',
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],
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plan7Days: [
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'Revisar otra vez en 2 dias con una foto nueva.',
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'Observar color y firmeza de hojas cada dia.',
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'Si empeora, ajustar ubicacion y frecuencia de riego.',
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],
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};
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}
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return {
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overallHealthScore: 58,
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status: 'watch',
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likelyIssues: [
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{
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title: 'Limited AI analysis',
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confidence: 0.42,
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details: `${plantContext?.name || 'This plant'} could not be fully assessed due to an unstable provider response.`,
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},
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],
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actionsNow: [
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'Capture a new photo in bright indirect light.',
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'Inspect leaves for spots, pests, and dry edges.',
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'Water only if the top 2-3 cm of soil is dry.',
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],
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plan7Days: [
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'Re-check in 2 days with a new photo.',
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'Track leaf color and firmness daily.',
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'If symptoms worsen, adjust placement and watering cadence.',
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],
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};
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};
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const normalizeHealthAnalysis = (
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raw: Record<string, unknown>,
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language: Language,
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): OpenAiHealthAnalysis | null => {
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const scoreRaw = getNumber(raw.overallHealthScore);
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const statusRaw = getString(raw.status);
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const issuesRaw = raw.likelyIssues;
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const actionsNowRaw = getStringArray(raw.actionsNow).slice(0, 6);
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const plan7DaysRaw = getStringArray(raw.plan7Days).slice(0, 7);
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if (scoreRaw == null || !statusRaw || !Array.isArray(issuesRaw)) {
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return null;
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}
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const status: OpenAiHealthAnalysis['status'] = statusRaw === 'healthy' || statusRaw === 'watch' || statusRaw === 'critical'
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? statusRaw
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: 'watch';
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const likelyIssues = issuesRaw
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.map((entry) => {
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if (!entry || typeof entry !== 'object' || Array.isArray(entry)) return null;
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const issueObj = entry as Record<string, unknown>;
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const title = getString(issueObj.title);
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const details = getString(issueObj.details);
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const confidenceRaw = getNumber(issueObj.confidence);
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if (!title || !details || confidenceRaw == null) return null;
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return {
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title,
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details,
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confidence: clamp(confidenceRaw, 0.05, 0.99),
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} as OpenAiHealthIssue;
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})
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.filter((entry): entry is OpenAiHealthIssue => Boolean(entry))
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.slice(0, 4);
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if (likelyIssues.length === 0 || actionsNowRaw.length === 0 || plan7DaysRaw.length === 0) {
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const fallbackIssue = language === 'de'
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? 'Die KI konnte keine stabilen Gesundheitsmerkmale extrahieren.'
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: language === 'es'
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? 'La IA no pudo extraer senales de salud estables.'
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: 'AI could not extract stable health signals.';
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return {
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overallHealthScore: Math.round(clamp(scoreRaw, 0, 100)),
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status,
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likelyIssues: [
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{
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title: language === 'de'
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? 'Analyse unsicher'
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: language === 'es'
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? 'Analisis incierto'
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: 'Uncertain analysis',
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confidence: 0.35,
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details: fallbackIssue,
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},
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],
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actionsNow: actionsNowRaw.length > 0
|
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? actionsNowRaw
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: [language === 'de' ? 'Neues, schaerferes Foto aufnehmen.' : language === 'es' ? 'Tomar una foto nueva y mas nitida.' : 'Capture a new, sharper photo.'],
|
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plan7Days: plan7DaysRaw.length > 0
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? plan7DaysRaw
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: [language === 'de' ? 'In 2 Tagen erneut pruefen.' : language === 'es' ? 'Volver a revisar en 2 dias.' : 'Re-check in 2 days.'],
|
||||
};
|
||||
}
|
||||
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||||
return {
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||||
overallHealthScore: Math.round(clamp(scoreRaw, 0, 100)),
|
||||
status,
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||||
likelyIssues,
|
||||
actionsNow: actionsNowRaw,
|
||||
plan7Days: plan7DaysRaw,
|
||||
};
|
||||
};
|
||||
|
||||
const extractMessageContent = (payload: unknown): string => {
|
||||
const response = payload as {
|
||||
choices?: Array<{
|
||||
message?: {
|
||||
content?: string | Array<{ type?: string; text?: string }>;
|
||||
};
|
||||
}>;
|
||||
};
|
||||
|
||||
const content = response.choices?.[0]?.message?.content;
|
||||
if (typeof content === 'string') return content;
|
||||
if (Array.isArray(content)) {
|
||||
return content
|
||||
.map((chunk) => (chunk?.type === 'text' ? chunk.text || '' : ''))
|
||||
.join('')
|
||||
.trim();
|
||||
}
|
||||
return '';
|
||||
};
|
||||
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||||
const postChatCompletion = async (
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||||
modelChain: string[],
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||||
imageUri: string,
|
||||
messages: Array<Record<string, unknown>>,
|
||||
): Promise<{ payload: Record<string, unknown> | null; modelUsed: string | null; attemptedModels: string[] }> => {
|
||||
const attemptedModels: string[] = [];
|
||||
|
||||
for (const model of modelChain) {
|
||||
attemptedModels.push(model);
|
||||
const controller = new AbortController();
|
||||
const timeout = setTimeout(() => controller.abort(), OPENAI_TIMEOUT_MS);
|
||||
|
||||
try {
|
||||
const response = await fetch(OPENAI_CHAT_COMPLETIONS_URL, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
Authorization: `Bearer ${OPENAI_API_KEY}`,
|
||||
},
|
||||
body: JSON.stringify({
|
||||
model,
|
||||
response_format: { type: 'json_object' },
|
||||
messages,
|
||||
}),
|
||||
signal: controller.signal,
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
const body = await response.text();
|
||||
console.warn('OpenAI request HTTP error.', {
|
||||
status: response.status,
|
||||
model,
|
||||
image: summarizeImageUri(imageUri),
|
||||
bodyPreview: body.slice(0, 300),
|
||||
});
|
||||
continue;
|
||||
}
|
||||
|
||||
const payload = (await response.json()) as Record<string, unknown>;
|
||||
return { payload, modelUsed: model, attemptedModels };
|
||||
} catch (error) {
|
||||
const isTimeoutAbort = error instanceof Error && error.name === 'AbortError';
|
||||
console.warn('OpenAI request failed.', {
|
||||
model,
|
||||
timeoutMs: OPENAI_TIMEOUT_MS,
|
||||
aborted: isTimeoutAbort,
|
||||
error: toErrorMessage(error),
|
||||
image: summarizeImageUri(imageUri),
|
||||
});
|
||||
continue;
|
||||
} finally {
|
||||
clearTimeout(timeout);
|
||||
}
|
||||
}
|
||||
|
||||
return { payload: null, modelUsed: null, attemptedModels };
|
||||
};
|
||||
|
||||
export const openAiScanService = {
|
||||
isConfigured: (): boolean => Boolean(OPENAI_API_KEY),
|
||||
|
||||
identifyPlant: async (
|
||||
imageUri: string,
|
||||
language: Language,
|
||||
mode: OpenAiScanMode = 'primary',
|
||||
plan: 'free' | 'pro' = 'free',
|
||||
): Promise<IdentificationResult | null> => {
|
||||
if (!OPENAI_API_KEY) return null;
|
||||
const modelChain = getScanModelChain(plan);
|
||||
const completion = await postChatCompletion(
|
||||
modelChain,
|
||||
imageUri,
|
||||
[
|
||||
{
|
||||
role: 'system',
|
||||
content: 'You are a plant identification assistant. Return strict JSON only.',
|
||||
},
|
||||
{
|
||||
role: 'user',
|
||||
content: [
|
||||
{ type: 'text', text: buildPrompt(language, mode) },
|
||||
{ type: 'image_url', image_url: { url: imageUri } },
|
||||
],
|
||||
},
|
||||
],
|
||||
);
|
||||
|
||||
if (!completion.payload) return null;
|
||||
|
||||
const content = extractMessageContent(completion.payload);
|
||||
if (!content) {
|
||||
console.warn('OpenAI plant scan returned empty message content.', {
|
||||
model: completion.modelUsed || modelChain[0],
|
||||
mode,
|
||||
image: summarizeImageUri(imageUri),
|
||||
});
|
||||
return null;
|
||||
}
|
||||
|
||||
const parsed = parseContentToJson(content);
|
||||
if (!parsed) {
|
||||
console.warn('OpenAI plant scan returned non-JSON content.', {
|
||||
model: completion.modelUsed || modelChain[0],
|
||||
mode,
|
||||
preview: content.slice(0, 220),
|
||||
});
|
||||
return null;
|
||||
}
|
||||
|
||||
const normalized = normalizeResult(parsed, language);
|
||||
if (!normalized) {
|
||||
console.warn('OpenAI plant scan JSON did not match required schema.', {
|
||||
model: completion.modelUsed || modelChain[0],
|
||||
mode,
|
||||
keys: Object.keys(parsed),
|
||||
});
|
||||
}
|
||||
|
||||
return normalized;
|
||||
},
|
||||
|
||||
analyzePlantHealth: async (
|
||||
imageUri: string,
|
||||
language: Language,
|
||||
plantContext?: {
|
||||
name: string;
|
||||
botanicalName: string;
|
||||
careInfo: CareInfo;
|
||||
description?: string;
|
||||
},
|
||||
): Promise<OpenAiHealthAnalysis | null> => {
|
||||
if (!OPENAI_API_KEY) return null;
|
||||
const completion = await postChatCompletion(
|
||||
OPENAI_HEALTH_MODEL_CHAIN,
|
||||
imageUri,
|
||||
[
|
||||
{
|
||||
role: 'system',
|
||||
content: 'You are a plant health diagnosis assistant. Return strict JSON only.',
|
||||
},
|
||||
{
|
||||
role: 'user',
|
||||
content: [
|
||||
{ type: 'text', text: buildHealthPrompt(language, plantContext) },
|
||||
{ type: 'image_url', image_url: { url: imageUri } },
|
||||
],
|
||||
},
|
||||
],
|
||||
);
|
||||
|
||||
if (!completion.payload) return buildFallbackHealthAnalysis(language, plantContext);
|
||||
|
||||
const content = extractMessageContent(completion.payload);
|
||||
if (!content) {
|
||||
console.warn('OpenAI health check returned empty content.', {
|
||||
model: completion.modelUsed || OPENAI_HEALTH_MODEL_CHAIN[0],
|
||||
image: summarizeImageUri(imageUri),
|
||||
});
|
||||
return buildFallbackHealthAnalysis(language, plantContext);
|
||||
}
|
||||
|
||||
const parsed = parseContentToJson(content);
|
||||
if (!parsed) {
|
||||
console.warn('OpenAI health check returned non-JSON content.', {
|
||||
model: completion.modelUsed || OPENAI_HEALTH_MODEL_CHAIN[0],
|
||||
preview: content.slice(0, 220),
|
||||
});
|
||||
return buildFallbackHealthAnalysis(language, plantContext);
|
||||
}
|
||||
|
||||
return normalizeHealthAnalysis(parsed, language) || buildFallbackHealthAnalysis(language, plantContext);
|
||||
},
|
||||
};
|
||||
Reference in New Issue
Block a user