Below is an outline of a commonly used, “tried and tested” approach to building a web interface that can launch and monitor both Python and C++ programs. The high-level idea is:

1. **Use a Python-based web framework** to handle incoming requests (like choosing which files to process).
2. **Spawn subprocesses** to run the C++ or Python scripts and capture their logs in real time.
3. **Stream or periodically update the web interface** with status and logs.

Below is a deeper breakdown of how you could structure it:

---

## 1. Choose a Server-Side Framework

A lightweight approach is to build the server using **Flask** or **FastAPI**:

- **Flask** is simple to set up, has a large ecosystem, and straightforward to use for smaller projects.
- **FastAPI** provides a more modern, asynchronous approach (if you need concurrency or streaming logs in real-time, FastAPI’s async features can be very helpful).

### Basic Setup

```bash
pip install flask
# or
pip install fastapi uvicorn
```

**Flask example skeleton**:

```python
from flask import Flask, request, jsonify
import subprocess

app = Flask(__name__)

@app.route("/run_cpp", methods=["POST"])
def run_cpp_script():
    data = request.get_json()  # This would have file list or parameters
    file_list = data.get("files", [])

    # This is where you'll spawn the C++ script
    # ...
    return jsonify({"message": "C++ script started, check logs..."})

@app.route("/run_python", methods=["POST"])
def run_python_script():
    data = request.get_json()
    # ...
    return jsonify({"message": "Python script started, check logs..."})

if __name__ == "__main__":
    app.run(debug=True)
```

---

## 2. Handling the Processes (C++ or Python)

### A. Using `subprocess`

For **C++**:
1. Compile your C++ program into an executable (e.g. `my_program`).
2. In Python, call it via `subprocess.Popen` or `subprocess.run`.

```python
import subprocess
import shlex

cmd = "./my_program --input /path/to/folder/ --files file1 file2 file3"
# You can assemble this based on the files chosen on the front end

process = subprocess.Popen(shlex.split(cmd), stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
```

For **Python scripts**:
1. Provide the path to the Python script (e.g. `scripts/my_script.py`).
2. Similarly call it:

```python
cmd = "python scripts/my_script.py --option value"
process = subprocess.Popen(shlex.split(cmd), stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
```

### B. Capturing Logs in Real-Time

To capture and stream logs back to the web interface:

1. **Synchronous reading** of `stdout`:
   ```python
   for line in process.stdout:
       line_str = line.decode().strip()
       # Store line_str somewhere (in-memory list, database, or queue)
   ```
2. **Push logs to the front end**. Two common ways:
   - **Short-polling**: The front end periodically sends an AJAX request to an endpoint like `/get_logs` to retrieve the latest lines.
   - **Server-Sent Events (SSE) or WebSockets**: A live connection where you stream logs. For Flask, you can use something like `Flask-SSE`. For FastAPI, you can use WebSockets easily with async routes.

#### Example with SSE (pseudo-code):
```python
@app.route('/stream_logs/<job_id>')
def stream_logs(job_id):
    def event_stream():
        # read from a queue or buffer
        while True:
            if new_log_message_available:
                yield f"data: {new_log_message}\n\n"
            time.sleep(1)
    return Response(event_stream(), mimetype="text/event-stream")
```

---

## 3. Managing Long-Running or Concurrent Tasks

If the C++ or Python processes might take a long time, you probably don’t want to block your web request. You can handle this in two ways:

### A. Async / Threaded Approach

- **In Flask**: you could spawn a thread that calls the process and immediately return a JSON response like `{ "job_id": some_unique_id }`.
- Then you can track the progress by storing logs in memory or a database keyed by `job_id`.
- The front end can poll or connect to SSE with that `job_id`.

### B. Use a Task Queue

- **Celery** or **RQ (Redis Queue)**:
  - The web server enqueues a “job” describing which script to run and which files to pass.
  - A Celery/RQ worker picks up that job, starts the script, and captures output.
  - The logs and job status are saved back to a datastore (Redis or database).
  - The front end can request the status/logs from the server with the job’s ID.

This approach is more robust if you have multiple requests in parallel or more advanced scheduling and retry logic.

---

## 4. Front-End Implementation

### A. Basic Polling

1. User selects files (check-box style or multi-select).
2. A `POST` request is sent to `/run_cpp` with JSON containing selected files.
3. The server spawns the process, stores logs in an in-memory list or database, returns a `job_id`.
4. The front end calls something like `/logs/<job_id>` every few seconds to get new logs.

### B. Live Streaming (SSE or WebSocket)

1. After receiving the `job_id`, the front end establishes a connection to `/stream_logs/<job_id>` to receive real-time log updates.
2. As logs are generated on the server, it “pushes” them to the client via the SSE or WebSocket stream.

---

## 5. Recommended “Ideal” Setup

For a production-friendly solution that’s relatively straightforward to maintain:

1. **FastAPI + Celery + Redis**
   - **FastAPI** for a modern async-based server.
   - **Celery** for managing background tasks (starting your C++ or Python scripts).
   - **Redis** as a backend for Celery and short-term storage for logs.
   - Use WebSockets or SSE for real-time updates.

2. **Execution Flow**:
   1. **Front End**:
      - The user picks files from a list.
      - Sends a `POST` to your FastAPI server at `/process-files`.
   2. **FastAPI**:
      - Receives the request, generates a job ID.
      - Calls a Celery task, passing in the list of files and job ID.
      - Immediately returns JSON like: `{"job_id": "1234", "status": "started"}`.
   3. **Celery Worker**:
      - Spawns the C++ script (via `subprocess.Popen`) with the appropriate arguments.
      - Captures `stdout` line by line.
      - Stores each log line in Redis under a key like `logs:1234`.
   4. **Front End**:
      - Listens on a WebSocket or SSE endpoint: `/stream_logs/1234`.
      - The FastAPI SSE/WebSocket endpoint retrieves logs from Redis and streams them as they come in.
   5. **Completion**:
      - Once the script finishes, the Celery task marks the job as “finished” in Redis.
      - The front end can see that the job is completed and show final logs or results.

3. **Why This Setup?**
   - Decoupling the web requests from the actual script execution prevents request timeouts and avoids blocking.
   - Using a task queue and an in-memory store like Redis keeps it robust, scalable, and easy to monitor.
   - SSE/WebSockets offers a clean solution to show live logs in the browser.

---

## 6. Deployment Considerations

- **Docker**: Containerize your FastAPI + Celery + Redis setup for easier deployment and scaling. You’d likely have a `docker-compose.yml` with services for `web`, `worker`, and `redis`.
- **Security**: If the scripts have file system access, ensure you handle user inputs properly (avoid arbitrary command injections by sanitizing file names, etc.).
- **Monitoring**: Tools like [Flower](https://flower.readthedocs.io/en/latest/) can monitor Celery tasks if using Celery.

---

## Summary

An **“ideal” solution** to build a web interface for running both Python scripts and C++ executables looks like this:

1. A Python-based web framework (Flask or FastAPI) to serve the interface and handle requests.
2. A background task system (threading, Celery, RQ, or custom) to avoid blocking web requests while scripts run.
3. Subprocess calls to your compiled C++ executables or separate Python scripts, capturing stdout.
4. A way to store logs (in-memory, Redis, or a simple database table) keyed by a unique job ID.
5. Real-time or periodic log updates in the web interface (via SSE, WebSockets, or polling).

This gives you the best balance of simplicity (because each piece is standard and well-documented) and scalability (because you can easily extend it for multiple concurrent jobs, advanced logging, or analytics).