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Supported File Formats

The Extrator de Tarefas Auvo accepts three file formats for processing:
  • CSV (.csv)
  • Excel 97-2003 (.xls)
  • Excel 2007+ (.xlsx)

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File Processing Implementation

The application uses pandas to read files with format-specific handling: 
def processar_arquivo(file, palavras_chave):
    """Processa o arquivo CSV ou Excel e retorna os dados filtrados"""
    filename = file.filename.lower()
    
    if filename.endswith('.csv'):
        df = pd.read_csv(file, skiprows=5)
    elif filename.endswith(('.xls', '.xlsx')):
        df = pd.read_excel(file, skiprows=5, engine='openpyxl')
    else:
        raise ValueError("Formato de arquivo não suportado")
Reference: app.py:32-45

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Critical: skiprows Behavior

All file formats skip the first 5 rows during processing using skiprows=5.
This is designed for Auvo system exports which include:
  • Header information (rows 1-5)
  • Actual column headers (row 6)
  • Data (rows 7+)

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skiprows Parameter Details

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skiprows
integer
default:"5"
Number of rows to skip from the top of the file before reading column headers.Used by:
  • pd.read_csv() for CSV files
  • pd.read_excel() for Excel files
Effect: The 6th row becomes the header row for the DataFrame.

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Required Column Structure

After the header row (row 6), the file must contain these exact columns:
​
Data
string
required
Date when the task was registered or completed.Format: Any date format recognized by pandas
​
Cliente
string
required
Client name or identifier for the task.
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Endereco
string
required
Address where the task should be performed or service delivered.
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OS Digital
string
required
Digital work order identifier or URL.Special Handling: If the value starts with http, it’s automatically converted to a clickable HTML link in the UI and PDF exports.
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Relato
string
required
Task description or report. This is the primary search field.Purpose: All keyword filtering is performed on this column.Search Behavior:
  • Case-insensitive matching
  • Supports regex patterns
  • NA/null values are excluded from matches

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Column Filtering Logic

The application extracts only these columns from the filtered results: 
colunas_resultado = ['Data', 'Cliente', 'Endereco', 'OS Digital', 'Relato']
return df, necessidades[colunas_resultado]
Reference: app.py:52-53

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Keyword Search Implementation

regex_busca = '|'.join(palavras_chave)
coluna_descricao = 'Relato'
necessidades = df[df[coluna_descricao].astype(str).str.contains(
    regex_busca, 
    case=False, 
    na=False
)].copy()
Reference: app.py:47-50
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regex_busca
string
Pipe-separated regex pattern combining all keywordsExample: "solicitar peça|quebrado|trocar cabo"
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case
boolean
default:"False"
Case-insensitive search enabled
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na
boolean
default:"False"
Excludes rows where Relato is null/NA

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File Format Validation

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Upload Validation

File format is validated at upload time: 
if not file.filename.lower().endswith(('.csv', '.xls', '.xlsx')):
    return redirect(url_for('index', 
        error='Por favor, selecione um arquivo .csv, .xls ou .xlsx'))
Reference: app.py:133-134

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Error Handling

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ValueError
exception
Raised when an unsupported file format is providedMessage: “Formato de arquivo não suportado. Use .csv, .xls ou .xlsx”

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Excel Engine Configuration

For Excel files, the application uses openpyxl as the parsing engine: 
df = pd.read_excel(file, skiprows=5, engine='openpyxl')
openpyxl is explicitly specified in requirements.txt (version 3.1.5) and is required for reading .xlsx files.

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Example File Structure

Row 1: [Auvo Export Header]
Row 2: [System Information]
Row 3: [Date Range]
Row 4: [User Information]
Row 5: [Empty or Metadata]
Row 6: Data | Cliente | Endereco | OS Digital | Relato
Row 7: 01/03/2026 | Cliente A | Rua X, 123 | http://auvo.com/os/123 | Solicitar peça quebrada
Row 8: 02/03/2026 | Cliente B | Rua Y, 456 | http://auvo.com/os/124 | Manutenção preventiva
...
Only rows 7+ are processed as data after skiprows=5 removes rows 1-5.

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Data Type Handling

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String Conversion

All values in the Relato column are converted to strings before pattern matching: 
df[coluna_descricao].astype(str).str.contains(...)
This ensures numeric or date values in the Relato column don’t cause type errors.

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Column Name Sensitivity

Column names are case-sensitive and must match exactly:
  • Data (not data or DATA)
  • Cliente (not cliente)
  • Endereco (not Endereço or endereco)
  • OS Digital (not OS_Digital or os digital)
  • Relato (not relato)
Mismatched column names will cause KeyError exceptions during processing.

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