Download CSV

Endpoint

GET /descargar

Downloads the complete vehicle registration database as a CSV file. This endpoint provides a full export of all records stored in data/registros.csv.

Request Parameters

This endpoint accepts no parameters.

Response

The endpoint returns the CSV file as a downloadable attachment. Status Code: 200 OK Content-Type: text/csv; charset=utf-8 Content-Disposition: attachment; filename="registros.csv"

CSV Structure

The downloaded CSV file contains the following columns:

string

Unique identifier for each record (auto-incremented)

fecha_hora

string

Timestamp when the record was created, format: YYYY-MM-DD HH:MM:SS

matricula

string

Extracted license plate number (uppercase, alphanumeric + hyphens, max 10 characters)

propietario

string

Name of the vehicle owner (may be empty)

tipo_vehiculo

string

Type of vehicle (e.g., “Sedan”, “SUV”, “Motorcycle”, may be empty)

observacion

string

Additional notes or observations about the record (may be empty)

imagen

string

Filename of the uploaded image stored in the uploads/ directory

Source Code Implementation

@app.route("/descargar")
def descargar():
    ensure_csv()
    return send_file(DATA_PATH, as_attachment=True, download_name="registros.csv")

The endpoint uses Flask’s send_file() function with:

DATA_PATH: Path to data/registros.csv
as_attachment=True: Forces browser download instead of display
download_name="registros.csv": Specifies the filename for download

Request Examples

cURL
Python Requests
JavaScript Fetch
wget
Browser

# Download to file
curl -O http://localhost:5000/descargar

# Or specify output filename
curl -o my_records.csv http://localhost:5000/descargar

# View in terminal
curl http://localhost:5000/descargar

import requests

url = "http://localhost:5000/descargar"
response = requests.get(url)

if response.status_code == 200:
    # Save to file
    with open('registros.csv', 'wb') as f:
        f.write(response.content)
    print("CSV downloaded successfully")
    
    # Or process directly
    import csv
    from io import StringIO
    
    csv_data = response.text
    reader = csv.DictReader(StringIO(csv_data))
    records = list(reader)
    
    for record in records:
        print(f"ID: {record['id']}, Plate: {record['matricula']}")

fetch('http://localhost:5000/descargar')
  .then(response => response.blob())
  .then(blob => {
    // Create download link
    const url = window.URL.createObjectURL(blob);
    const a = document.createElement('a');
    a.href = url;
    a.download = 'registros.csv';
    document.body.appendChild(a);
    a.click();
    a.remove();
    window.URL.revokeObjectURL(url);
  })
  .catch(error => console.error('Error:', error));

# Download with wget
wget http://localhost:5000/descargar -O registros.csv

# Or use default filename
wget http://localhost:5000/descargar

Simply navigate to:

http://localhost:5000/descargar

The browser will automatically download the file.

Sample CSV Output

id,fecha_hora,matricula,propietario,tipo_vehiculo,observacion,imagen
1,2026-03-04 15:30:45,ABC1234,Juan Pérez,Sedan,Vehículo de entrega,matricula_20260304_153045.jpg
2,2026-03-04 16:15:22,XYZ5678,María García,SUV,Acceso autorizado,matricula_20260304_161522.jpg
3,2026-03-04 17:20:10,DEF9012,Carlos López,Motorcycle,,matricula_20260304_172010.jpg
4,2026-03-04 18:45:33,GHI3456,Ana Martínez,Truck,Proveedor frecuente,matricula_20260304_184533.jpg
5,2026-03-04 19:12:08,NO_DETECTADA,Pedro Sánchez,Sedan,Placa ilegible,matricula_20260304_191208.jpg

Use Cases

1. Data Backup

Regularly download the CSV to create backups:

#!/bin/bash
# Daily backup script
DATE=$(date +%Y%m%d)
curl -o "backup_${DATE}.csv" http://localhost:5000/descargar

2. Data Analysis

Import the CSV into analysis tools:

import pandas as pd
import requests
from io import StringIO

response = requests.get('http://localhost:5000/descargar')
df = pd.read_csv(StringIO(response.text))

# Analyze data
print(f"Total records: {len(df)}")
print(f"Unique plates: {df['matricula'].nunique()}")
print(f"Most common vehicle type: {df['tipo_vehiculo'].mode()[0]}")

# Group by date
df['fecha'] = pd.to_datetime(df['fecha_hora']).dt.date
daily_counts = df.groupby('fecha').size()
print("\nRecords per day:")
print(daily_counts)

3. Report Generation

Generate reports from the exported data:

import csv
import requests
from io import StringIO

response = requests.get('http://localhost:5000/descargar')
reader = csv.DictReader(StringIO(response.text))

# Generate summary report
records = list(reader)
print(f"=== VEHICLE REGISTRATION REPORT ===")
print(f"Total Vehicles: {len(records)}")
print(f"\nRecent Entries:")

for record in records[-5:]:
    print(f"  {record['fecha_hora']}: {record['matricula']} - {record['propietario']}")

4. Database Migration

Export data for migration to another system:

import requests
import csv
from io import StringIO
import sqlite3

# Download CSV
response = requests.get('http://localhost:5000/descargar')
reader = csv.DictReader(StringIO(response.text))

# Import to SQLite
conn = sqlite3.connect('registros.db')
cursor = conn.cursor()

cursor.execute('''
    CREATE TABLE IF NOT EXISTS registros (
        id INTEGER PRIMARY KEY,
        fecha_hora TEXT,
        matricula TEXT,
        propietario TEXT,
        tipo_vehiculo TEXT,
        observacion TEXT,
        imagen TEXT
    )
''')

for record in reader:
    cursor.execute('''
        INSERT INTO registros VALUES (?, ?, ?, ?, ?, ?, ?)
    ''', (record['id'], record['fecha_hora'], record['matricula'],
          record['propietario'], record['tipo_vehiculo'], 
          record['observacion'], record['imagen']))

conn.commit()
conn.close()
print("Data imported to SQLite successfully")

File Initialization

The ensure_csv() function is called before serving the file. If data/registros.csv doesn’t exist, it creates an empty CSV with headers:

def ensure_csv():
    if not os.path.exists(DATA_PATH):
        with open(DATA_PATH, "w", newline="", encoding="utf-8") as f:
            csv.writer(f).writerow(CSV_HEADER)

This means the endpoint will always return a valid CSV file, even if no records exist:

id,fecha_hora,matricula,propietario,tipo_vehiculo,observacion,imagen

Character Encoding

The CSV file uses UTF-8 encoding to support international characters in names and observations:

encoding="utf-8"

This ensures proper display of characters like:

Spanish: á, é, í, ó, ú, ñ, ¿, ¡
Accented names: Pérez, García, López, Martínez

Performance Considerations

File Size: The CSV file grows linearly with the number of records. Each record adds approximately 100-200 bytes.

Memory Usage: The entire file is loaded into memory before sending. For very large datasets (10,000+ records), consider streaming the response.

Concurrent Access: Multiple simultaneous downloads are safe. The file is read-only during the download operation.

Integration with Other Endpoints

Full Export Workflow

import requests
import os
import csv
from io import StringIO

# 1. Download CSV
csv_response = requests.get('http://localhost:5000/descargar')
records = list(csv.DictReader(StringIO(csv_response.text)))

# 2. Download all images
for record in records:
    img_url = f"http://localhost:5000/uploads/{record['imagen']}"
    img_response = requests.get(img_url)
    
    if img_response.status_code == 200:
        with open(f"backup/{record['imagen']}", 'wb') as f:
            f.write(img_response.content)
        print(f"Downloaded: {record['imagen']}")

print(f"\nBackup complete: {len(records)} records and images")

Security Considerations

Data Exposure: This endpoint exposes all database records without authentication. Implement access control for production environments.

Read-Only: This endpoint only reads data and doesn’t modify the database. It’s safe from data corruption.

No PII Protection: The CSV may contain personal information (names). Ensure compliance with data protection regulations (GDPR, CCPA).

Recommended Enhancements

For production use:

Add authentication:

@app.route("/descargar")
@login_required
def descargar():
    # ... existing code

Add date range filtering:

@app.route("/descargar")
def descargar():
    start_date = request.args.get('start')
    end_date = request.args.get('end')
    # Filter records by date range

Add compression for large files:

import gzip
import io

# Create gzipped response
output = io.BytesIO()
with gzip.open(output, 'wt', encoding='utf-8') as f:
    # Write CSV to gzipped file

Implement streaming for large datasets:

from flask import Response

def generate():
    with open(DATA_PATH, 'r') as f:
        for line in f:
            yield line

return Response(generate(), mimetype='text/csv')

View Records

View records in HTML format

Upload Plate

Add new records to export

Delete Record

Remove records before export

Endpoints

Core Functions

Endpoint

Request Parameters

Response

CSV Structure

Source Code Implementation

Request Examples

Sample CSV Output

Use Cases

1. Data Backup

2. Data Analysis

3. Report Generation

4. Database Migration

File Initialization

Character Encoding

Performance Considerations

Integration with Other Endpoints

Full Export Workflow

Security Considerations

Recommended Enhancements

View Records

Upload Plate

Delete Record

Build docs developers (and LLMs) love

Endpoints

Core Functions

​Endpoint

​Request Parameters

​Response

​CSV Structure

​Source Code Implementation

​Request Examples

​Sample CSV Output

​Use Cases

​1. Data Backup

​2. Data Analysis

​3. Report Generation

​4. Database Migration

​File Initialization

​Character Encoding

​Performance Considerations

​Integration with Other Endpoints

​Full Export Workflow

​Security Considerations

​Recommended Enhancements

​Related Endpoints

View Records

Upload Plate

Delete Record

Build docs developers (and LLMs) love

Endpoint

Request Parameters

Response

CSV Structure

Source Code Implementation

Request Examples

Sample CSV Output

Use Cases

1. Data Backup

2. Data Analysis

3. Report Generation

4. Database Migration

File Initialization

Character Encoding

Performance Considerations

Integration with Other Endpoints

Full Export Workflow

Security Considerations

Recommended Enhancements

Related Endpoints