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Integration of Frontend, Backend, and DAGs
=========================================

This document explains how the user interacts with the application, from uploading a JSON file to triggering DAGs in Airflow for risk calculations.

.. contents::
    :local:
    :depth: 2

Frontend: Uploading JSON and Selecting the Client
-------------------------------------------------

1. The user uploads a **JSON file** containing the portfolio data.
2. Once uploaded, the **client selection dropdown is enabled**.
3. The user selects a **client** and clicks **"Calculate Risks"**.
4. A request is sent to the API with the following structure:

    .. code-block:: json

        {
            "client_id": "AlphaKlima",
            "portfolio_assets": [
            {
                "id": "1",
                "asset_id": "1",
                "name": "HOTEL ASTORIA S.A.",
                "asset_class": "RealEstateAsset",
                "latitude": 41.393579,
                "longitude": 2.1528003,
                ...
            }
            ],
            "portfolio_name": "assets_AlphaKlima.json"
        }

Backend: Receiving and Processing Data
--------------------------------------

1. The **FastAPI backend** receives the request at:

    .. code-block:: python

        @app.post("/api/calculate_portfolio_risks")
        async def calculate_risks(request: PortfolioRequest):
            return {"message": f"Calculating risks for {request.client_id}"}

2. The backend extracts the data and **converts it into a DataFrame** for processing.

3. Then, it **triggers an Airflow DAG**, passing the data inside the `conf` parameter:

    .. code-block:: python

        payload = {
            "dag_run_id": f"manual__{now}",
            "conf": request.dict()
        }

4. The backend makes an HTTP request to **Airflow** to start the DAG:

    .. code-block:: python

        requests.post(f"{AIRFLOW_URL}/dags/insert_portfolio_dag/dagRuns", json=payload)

DAG in Airflow: Risk Calculation
--------------------------------

1. The DAG retrieves the data and converts it into a **DataFrame**:

    .. code-block:: python

        import pandas as pd

        def process_portfolio(data):
            df = pd.DataFrame(data["portfolio_assets"])
            df["location_coordinates"] = df.apply(
                lambda row: f"POINT({row['longitude']} {row['latitude']})", axis=1
            )
            return df

2. It inserts the data into a **PostgreSQL database**.

3. The DAG performs **financial risk calculations**.

4. Once completed, it sends a **response back to the frontend**.

Conclusion
----------

This flow enables users to:

- Upload JSON files containing portfolios.
- Select a client before performing risk calculations.
- Automatically trigger DAGs via API requests.
- Retrieve processed risk calculations efficiently.