GET /api/p1/{auth_key}/{target}
  GET /api/p2/{auth_key}/{target}
  GET /api/p3/{auth_key}/{target}
  GET /api/p4/{auth_key}/{target}
  GET /api/p5/{auth_key}/{target}

  GET /api/p1/
  GET /api/p2/
  GET /api/p3/
  GET /api/p4/
  GET /api/p5/
  GET /api/p6/
  GET /api/p7/
  GET /api/p8/
  GET /api/p9/
  GET /api/p10/
  GET /api/p11/
  GET /api/p12/
  GET /api/p13/

  GET /register/<int:user_id>/<string:password>

Data -> Chunks_generator ─┐            ┌─> AI_Loop -> Data_Extraction -> Return_Data
                          ├─> Chunk1  ─┤
                          ├─> Chunk2  ─┤
                          ├─> Chunk3  ─┤
                          └─> Chunk N ─┘

def AI(analize: str) -> dict[str, any]:
    prompt = f"""
        Do a NMAP scan analysis on the provided NMAP scan information
        The NMAP output must return in a JSON format accorging to the provided
        output format. The data must be accurate in regards towards a pentest report.
        The data must follow the following rules:
        1) The NMAP scans must be done from a pentester point of view
        2) The final output must be minimal according to the format given.
        3) The final output must be kept to a minimal.
        4) If a value not found in the scan just mention an empty string.
        5) Analyze everything even the smallest of data.
        6) Completely analyze the data provided and give a confirm answer using the output format.

        The output format:
        {{
            "critical score": [""],
            "os information": [""],
            "open ports": [""],
            "open services": [""],
            "vulnerable service": [""],
            "found cve": [""]
        }}

        NMAP Data to be analyzed: {analize}
    """
    messages = [{"content": prompt, "role": "assistant"}]
    response = openai.ChatCompletion.create(
        model=model_engine,
        messages=messages,
        max_tokens=2500,
        n=1,
        stop=None,
    )
    response = response['choices'][0]['message']['content']
    ai_output = {
        "markdown": response
    }

    return ai_output