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Bake the EXIF Orientation rotation into uploaded JPEG pixels (and strip the tag) so receipts are upright in every consumer, not just EXIF-aware viewers. Acts only when orientation is known (tag 2..8); images with no tag, tag 1, non-JPEG, or PDFs pass through byte-for-byte. Wired into the receipt, attachments, and AI-classify paths. Documented as spec item 13. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
114 lines
3.2 KiB
Go
114 lines
3.2 KiB
Go
package web
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import (
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"encoding/json"
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"io"
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"net/http"
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"strings"
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"time"
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"maisym.com/hsa/internal/classify"
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"maisym.com/hsa/internal/storage"
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)
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// classifyResponse is the JSON returned to the upload page to pre-fill the form.
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// IDs map the classifier's label suggestions onto the live lookup rows; they are
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// null when the classifier returned nothing or the label has no matching row.
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type classifyResponse struct {
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Amount *string `json:"amount"`
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Date *string `json:"date"`
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CategoryID *int64 `json:"category_id"`
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PersonID *int64 `json:"person_id"`
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Category string `json:"category"` // label, for display
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Person string `json:"person"` // label, for display
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RawName string `json:"raw_name"`
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RawDate string `json:"raw_date"`
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RawAmount string `json:"raw_amount"`
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Model string `json:"model"` // model that ran, for display
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CostCents *float64 `json:"cost_cents,omitempty"` // nil when the model's price is unknown
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}
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// handleClassify accepts an uploaded receipt image and returns suggested form
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// values from the AI classifier. It mutates no state — the user still reviews and
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// submits via POST /upload.
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func (s *Server) handleClassify(w http.ResponseWriter, r *http.Request) {
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if s.classifier == nil {
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http.Error(w, "classification not configured", http.StatusServiceUnavailable)
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return
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}
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r.Body = http.MaxBytesReader(w, r.Body, s.cfg.MaxUploadBytes)
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if err := r.ParseMultipartForm(10 << 20); err != nil {
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http.Error(w, "upload too large or malformed", http.StatusBadRequest)
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return
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}
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file, _, err := r.FormFile("receipt")
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if err != nil {
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http.Error(w, "attach a receipt image or PDF", http.StatusBadRequest)
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return
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}
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defer file.Close()
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data, err := io.ReadAll(file)
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if err != nil {
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http.Error(w, "could not read the uploaded file", http.StatusBadRequest)
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return
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}
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mimeType := detectMime(data)
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if !allowedMime(mimeType) {
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http.Error(w, "only images and PDFs are allowed", http.StatusUnsupportedMediaType)
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return
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}
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data = normalizeOrientation(data, mimeType) // upright image reads better for the AI
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sug, err := s.classifier.Classify(r.Context(), time.Now(), data, mimeType)
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if err != nil {
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s.serverError(w, "classify receipt", err)
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return
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}
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cats, people, err := s.lookups()
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if err != nil {
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s.serverError(w, "load lookups", err)
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return
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}
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out := classifyResponse{
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Amount: sug.Amount,
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Date: sug.Date,
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Category: sug.Category,
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RawName: sug.RawName,
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RawDate: sug.RawDate,
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RawAmount: sug.RawAmount,
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Model: s.classifier.Model,
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}
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if cents, ok := classify.CostCents(s.classifier.Model, sug.Usage); ok {
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out.CostCents = ¢s
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}
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if id, ok := idForLabel(sug.Category, cats); ok {
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out.CategoryID = &id
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}
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if sug.Person != nil {
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if id, ok := idForLabel(*sug.Person, people); ok {
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out.PersonID = &id
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out.Person = *sug.Person
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}
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}
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w.Header().Set("Content-Type", "application/json")
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_ = json.NewEncoder(w).Encode(out)
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}
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// idForLabel finds a lookup row by case-insensitive label match.
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func idForLabel(label string, set []storage.Lookup) (int64, bool) {
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label = strings.TrimSpace(label)
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for _, l := range set {
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if strings.EqualFold(l.Label, label) {
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return l.ID, true
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}
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}
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return 0, false
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}
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