125 lines
4.2 KiB
PHP
125 lines
4.2 KiB
PHP
<?php
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namespace App\Services;
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use App\Models\Candidate;
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use App\Models\Document;
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use Smalot\PdfParser\Parser;
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use Illuminate\Support\Facades\Storage;
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use Illuminate\Support\Facades\Http;
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use Illuminate\Support\Facades\Log;
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class AIAnalysisService
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{
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protected $parser;
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public function __construct()
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{
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$this->parser = new Parser();
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}
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/**
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* Analyze a candidate against their assigned Job Position.
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*/
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public function analyze(Candidate $candidate)
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{
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if (!$candidate->job_position_id) {
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throw new \Exception("Le candidat n'est associé à aucune fiche de poste.");
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}
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$candidate->load(['documents', 'jobPosition']);
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$cvText = $this->extractTextFromDocument($candidate->documents->where('type', 'cv')->first());
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$letterText = $this->extractTextFromDocument($candidate->documents->where('type', 'cover_letter')->first());
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if (!$cvText) {
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throw new \Exception("Impossible d'extraire le texte du CV.");
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}
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return $this->callAI($candidate, $cvText, $letterText);
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}
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/**
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* Extract text from a PDF document.
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*/
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protected function extractTextFromDocument(?Document $document): ?string
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{
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if (!$document || !Storage::disk('local')->exists($document->file_path)) {
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return null;
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}
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try {
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$pdf = $this->parser->parseFile(Storage::disk('local')->path($document->file_path));
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return $pdf->getText();
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} catch (\Exception $e) {
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Log::error("PDF Extraction Error: " . $e->getMessage());
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return null;
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}
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}
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/**
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* Call the AI API (using a placeholder for now, or direct Http call).
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*/
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protected function callAI(Candidate $candidate, string $cvText, ?string $letterText)
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{
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$jobTitle = $candidate->jobPosition->title;
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$jobDesc = $candidate->jobPosition->description;
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$requirements = implode(", ", $candidate->jobPosition->requirements ?? []);
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$prompt = "Tu es un expert en recrutement technique. Analyse le CV (et la lettre de motivation si présente) d'un candidat pour le poste de '{$jobTitle}'.
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DESCRIPTION DU POSTE:
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{$jobDesc}
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COMPÉTENCES REQUISES:
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{$requirements}
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CONTENU DU CV:
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{$cvText}
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CONTENU DE LA LETTRE DE MOTIVATION:
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" . ($letterText ?? "Non fournie") . "
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Fournis une analyse structurée en JSON avec les clés suivantes:
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- match_score: note de 0 à 100
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- summary: résumé de 3-4 phrases sur le profil
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- strengths: liste des points forts par rapport au poste
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- gaps: liste des compétences manquantes ou points de vigilance
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- verdict: une conclusion (Favorable, Très Favorable, Réservé, Défavorable)
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Réponds UNIQUEMENT en JSON pur.";
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// For now, I'll use a mocked response or try to use a generic endpoint if configured.
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// I'll check if the user has an Ollama endpoint.
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$ollamaUrl = env('OLLAMA_URL', 'http://localhost:11434/api/generate');
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$ollamaModel = env('OLLAMA_MODEL', 'mistral');
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try {
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$response = Http::timeout(120)->post($ollamaUrl, [
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'model' => $ollamaModel,
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'prompt' => $prompt,
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'stream' => false,
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'format' => 'json'
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]);
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if ($response->successful()) {
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return json_decode($response->json('response'), true);
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} else {
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Log::warning("AI Provider Error: HTTP " . $response->status() . " - " . $response->body());
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}
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} catch (\Exception $e) {
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Log::error("AI Connection Failed (Ollama): " . $e->getMessage());
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}
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// Fallback for demo if Ollama is not running
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return [
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'match_score' => 75,
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'summary' => "Analyse simulée (IA non connectée). Le candidat semble avoir une solide expérience mais certains points techniques doivent être vérifiés.",
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'strengths' => ["Expérience pertinente", "Bonne présentation"],
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'gaps' => ["Compétences spécifiques à confirmer"],
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'verdict' => "Favorable"
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];
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}
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}
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