CoroNet stores all vehicle registrations in a CSV-based database system, providing a lightweight yet robust solution for managing vehicle entry records. The system supports full CRUD operations (Create, Read, Update, Delete) with automatic ID assignment and file-based persistence.
New registrations are created through the /guardar endpoint:
@app.route("/guardar", methods=["POST"])def guardar(): ensure_csv() file = request.files.get("imagen") propietario = request.form.get("propietario", "") tipo = request.form.get("tipo_vehiculo", "") obs = request.form.get("observacion", "") # Validate image upload if not file: flash("⚠️ Debes subir una imagen", "error") return redirect(url_for("index")) # Save image with timestamp filename = f"matricula_{datetime.now().strftime('%Y%m%d_%H%M%S')}.jpg" path = os.path.join(UPLOADS_DIR, filename) file.save(path) # Detect license plate (GPT-4o + Tesseract fallback) matricula = extract_plate_from_image(path) if matricula == "NO_DETECTADA": image = Image.open(path) ocr_text = pytesseract.image_to_string(image, lang="eng") ocr_text = ocr_text.strip().replace(" ", "").replace("\n", "").upper() matricula = "".join([c for c in ocr_text if c.isalnum() or c == "-"])[:10] # Load existing records rows = read_csv() # Auto-increment ID new_id = str(len(rows) + 1) fecha = datetime.now().strftime("%Y-%m-%d %H:%M:%S") # Append new record rows.append({ "id": new_id, "fecha_hora": fecha, "matricula": matricula, "propietario": propietario, "tipo_vehiculo": tipo, "observacion": obs, "imagen": filename }) # Persist to CSV write_csv(rows) flash("✅ Registro guardado correctamente", "ok") return redirect(url_for("index"))
Key features of the create operation:
Auto-incrementing IDs: New ID calculated as len(rows) + 1 (app.py:113)
Automatic timestamps: Current date/time in YYYY-MM-DD HH:MM:SS format (app.py:114)
Image validation: Requires image upload before processing (app.py:94-96)
Dual OCR detection: Attempts GPT-4o first, falls back to Tesseract (app.py:103-110)
User feedback: Flash messages confirm successful save (app.py:127)
The system uses a read-modify-write pattern: load all records, append new record, write all records back. This ensures data integrity but may have performance implications for very large datasets.
Serves uploaded images directly from the uploads directory (app.py:149-151), allowing the web interface to display vehicle photos alongside registration data.
The current implementation does not include an explicit update endpoint. To modify a registration, you would need to delete the existing record and create a new one, or implement a custom update route.
To add update functionality, you could implement:
@app.route("/editar/<id>", methods=["GET", "POST"])def editar(id): rows = read_csv() if request.method == "POST": # Update the matching record for row in rows: if row["id"] == id: row["propietario"] = request.form.get("propietario") row["tipo_vehiculo"] = request.form.get("tipo_vehiculo") row["observacion"] = request.form.get("observacion") break write_csv(rows) flash("Registro actualizado", "ok") return redirect(url_for("registros")) # GET: Find and display record for editing registro = next((r for r in rows if r["id"] == id), None) return render_template("editar.html", registro=registro)
The /eliminar/<id> endpoint handles record deletion:
@app.route("/eliminar/<id>")def eliminar(id): rows = read_csv() # Filter out the record to delete filtered = [r for r in rows if r["id"] != id] # Find the deleted record to remove its image deleted = [r for r in rows if r["id"] == id] if deleted: img = deleted[0]["imagen"] img_path = os.path.join(UPLOADS_DIR, img) if os.path.exists(img_path): os.remove(img_path) # Write updated records write_csv(filtered) flash("Registro eliminado correctamente", "ok") return redirect(url_for("registros"))
Delete operation features:
List filtering: Creates new list excluding the target ID (app.py:138)
Image cleanup: Removes associated image file from uploads directory (app.py:139-144)
Safe deletion: Checks if image file exists before attempting removal (app.py:143)
Cascading delete: Both database record and physical image file are removed
User feedback: Confirms successful deletion (app.py:146)
ID-based Deletion
Image Cleanup
Records are deleted by ID, which is unique and auto-incremented. The ID is passed as a URL parameter:
/eliminar/5 # Deletes registration with ID "5"
The system maintains referential integrity by removing both:
The CSV row containing registration data
The physical image file stored in uploads/
This prevents orphaned files and wasted disk space.
