# STREAMING ERROR BETWEEN BACKEND AND FRONTEND
## backend logs:

[2025-03-01T12:50:17.265+0000] {views.py:123} INFO - COMPLETE RESPONSE START >>>
[2025-03-01T12:50:17.265+0000] {views.py:124} INFO - I'll provide you with a factorial implementation in Apache Airflow using a PythonOperator. Here's an example DAG that calculates factorial:

```python
from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime, timedelta

def calculate_factorial(number, **context):
    """
    Calculate factorial of a given number
    """
    def factorial(n):
        if n == 0 or n == 1:
            return 1
        else:
            return n * factorial(n-1)
    
    result = factorial(number)
    print(f"Factorial of {number} is: {result}")
    return result

with DAG(
    'factorial_dag',
    default_args={
        'owner': 'airflow',
        'start_date': datetime(2023, 1, 1),
        'retries': 1,
        'retry_delay': timedelta(minutes=5),
    },
    schedule_interval='@once'
) as dag:
    
    factorial_task = PythonOperator(
        task_id='calculate_factorial',
        python_callable=calculate_factorial,
        op_args=[5],  # Calculate factorial of 5
        dag=dag
    )

    factorial_task
```

This DAG does the following:

1. Defines a `calculate_factorial` function that computes the factorial recursively
2. Uses a PythonOperator to execute the factorial calculation
3. Prints the result and returns the factorial value
4. Sets up a simple DAG that runs once

A few Airflow-specific notes:
- Uses PythonOperator for custom Python function execution
- Demonstrates passing arguments via `op_args`
- Uses a simple scheduling strategy (`@once`)

You can modify the number in `op_args` to calculate factorial for different values.

Would you like me to elaborate on any part of the implementation?
[2025-03-01T12:50:17.266+0000] {views.py:125} INFO - <<< COMPLETE RESPONSE END



## Frontend result

```
I'll provide you with a factorial implementation in Apache Airflow using a PythonOperator. Here's an example DAG that calculates factorial:G PythonOperator calculate_factorial(number, **context):def factorial(n): n == 0 or n == 1:1 factorial(n-1) = factorial(number)print(f"Factorial of {number} is: {result}") DAG(factorial_dag',args={ 'airflow',date': datetime(2023, 1, 1),,(minutes=5),},once'ator(factorial',factorial,=[5],  # Calculate factorial of 5. Defines a `calculate_factorial` function that computes the factorial recursivelyythonOperator to execute the factorial calculation Prints the result and returns the factorial value up a simple DAG that runs oncespecific notes: PythonOperator for custom Python function execution passing arguments via `op_args` scheduling strategy (`@once`) the number in `op_args` to calculate factorial for different values.d you like me to elaborate on any part of the implementation?
```


## Console logs

```
Complete response: I'll provide you with a factorial implementation in Apache Airflow using a PythonOperator. Here's an example DAG that calculates factorial:G PythonOperator calculate_factorial(number, **context):def factorial(n): n == 0 or n == 1:1 factorial(n-1) = factorial(number)print(f"Factorial of {number} is: {result}") DAG(factorial_dag',args={ 'airflow',date': datetime(2023, 1, 1),,(minutes=5),},once'ator(factorial',factorial,=[5],  # Calculate factorial of 5. Defines a `calculate_factorial` function that computes the factorial recursivelyythonOperator to execute the factorial calculation Prints the result and returns the factorial value up a simple DAG that runs oncespecific notes: PythonOperator for custom Python function execution passing arguments via `op_args` scheduling strategy (`@once`) the number in `op_args` to calculate factorial for different values.d you like me to elaborate on any part of the implementation?
```
