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git clone https://github.com/sysadmin-info/ai-agent.git

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uvicorn index:app --host 0.0.0.0 --port 3000

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alias activate-agent="source $HOME/agent/venv/bin/activate"
alias activate-aidevs="source $HOME/aidevs/agent/venv/bin/activate"

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source ~/.bashrc

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import logging
from quart import Quart, request, jsonify
from lib.prompts import Prompts
from lib.ai import AnthropicCompletion
from typing import Dict, Any
from dotenv import load_dotenv
import os
import json
import datetime

# Załaduj zmienne środowiskowe
load_dotenv()

# Inicjalizacja aplikacji Quart
app = Quart(__name__)

# Konfiguracja loggera
class SensitiveDataFilter(logging.Filter):
    """
    Filtr logowania do ukrywania danych wrażliwych, takich jak klucz API.
    """
    def filter(self, record):
        if record.msg and isinstance(record.msg, str):
            record.msg = record.msg.replace(os.getenv("ANTHROPIC_API_KEY", ""), "[REDACTED]")
        return True

logging.basicConfig(level=logging.DEBUG, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger("AIAgentLogger")
logger.addFilter(SensitiveDataFilter())


class AIAgent:
    def __init__(self, api_key: str):
        self.completion_client = AnthropicCompletion(api_key)
        self.state = {
            "currentStage": "init",
            "currentStep": 1,
            "maxSteps": 15,
            "messages": [],
            "systemPrompt": "",
            "plan": "",
            "actionsTaken": [],
            "activeTool": {},
            "api_key": api_key
        }

    def _sanitize_state(self) -> Dict[str, Any]:
        """
        Usuwa wrażliwe dane (np. klucz API) przed logowaniem.
        """
        return {k: v for k, v in self.state.items() if k != "api_key"}

    async def final_answer(self) -> str:
        self.state["systemPrompt"] = Prompts.final_answer_prompt(self.state)
        messages = [{"role": "user", "content": self.state["systemPrompt"]}]
        logger.debug(f"Sending final_answer request: {messages}")
        answer = await self.completion_client.completion(messages)
        parsed_answer = self._parse_response(answer, step="final_answer")
        self._log_to_markdown("result", "Final Answer", json.dumps(parsed_answer))
        return parsed_answer

    async def run(self, initial_message: str) -> str:
        self.state["messages"] = [{"role": "user", "content": initial_message}]
        logger.debug(f"Initial state: {self._sanitize_state()}")

        while self.state["currentStep"] <= self.state["maxSteps"]:
            try:
                self._log_request_start("plan")
                await self._plan()
                self._log_request_end("plan")

                self._log_request_start("decide")
                await self._decide()
                self._log_request_end("decide")

                if not self.state.get("activeTool", {}).get("tool"):
                    raise ValueError("Active tool is not defined or missing the 'tool' property in state.")

                if self.state["activeTool"]["tool"] == "final_answer":
                    return await self.final_answer()

                self._log_request_start("describe")
                await self._describe()
                self._log_request_end("describe")

                self._log_request_start("execute")
                await self._execute()
                self._log_request_end("execute")

                self._log_request_start("reflect")
                await self._reflect()
                self._log_request_end("reflect")

                self.state["currentStep"] += 1
            except Exception as e:
                logger.error(f"Error during step {self.state['currentStage']}: {str(e)}")
                raise

    async def _plan(self):
        self.state["currentStage"] = "plan"
        self.state["systemPrompt"] = Prompts.plan_prompt(self.state)
        messages = [{"role": "user", "content": self.state["systemPrompt"]}]
        logger.debug(f"Plan request payload: {messages}")
        plan_response = await self.completion_client.completion(messages)
        self.state["plan"] = self._parse_response(plan_response, step="plan")

    async def _decide(self):
        self.state["currentStage"] = "decide"
        self.state["systemPrompt"] = Prompts.decide_prompt(self.state)
        messages = [{"role": "user", "content": self.state["systemPrompt"]}]
        logger.debug(f"Decide request payload: {messages}")
        decision_response = await self.completion_client.completion(messages)
        try:
            self.state["activeTool"] = json.loads(self._parse_response(decision_response, step="decide"))
            logger.debug(f"Active tool decided: {self.state['activeTool']}")
        except json.JSONDecodeError as e:
            logger.error(f"Failed to parse decision JSON: {decision_response}")
            raise ValueError(f"Error parsing decision JSON: {decision_response}") from e

    async def _describe(self):
        self.state["currentStage"] = "describe"
        if not self.state.get("activeTool", {}).get("tool"):
            raise ValueError("Active tool is not defined or missing the 'tool' property in state.")

        self.state["systemPrompt"] = Prompts.describe_prompt(self.state)
        messages = [{"role": "user", "content": self.state["systemPrompt"]}]
        logger.debug(f"Describe request payload: {messages}")
        describe_response = await self.completion_client.completion(messages)
        self.state["activeToolPayload"] = self._parse_response(describe_response, step="describe")

    async def _execute(self):
        self.state["currentStage"] = "execute"
        tool_name = self.state["activeTool"]["tool"]
        payload = self.state["activeToolPayload"]
        logger.debug(f"Executing tool: {tool_name} with payload: {payload}")
        result = f"Executed {tool_name} with payload {payload}"
        self.state["actionsTaken"].append({
            "name": tool_name,
            "payload": payload,
            "result": result,
            "reflection": ""
        })

    async def _reflect(self):
        self.state["currentStage"] = "reflect"
        self.state["systemPrompt"] = Prompts.reflection_prompt(self.state)
        messages = [{"role": "user", "content": self.state["systemPrompt"]}]
        logger.debug(f"Reflect request payload: {messages}")
        reflection_response = await self.completion_client.completion(messages)
        self.state["actionsTaken"][-1]["reflection"] = self._parse_response(reflection_response, step="reflect")

    def _parse_response(self, response: Dict[str, Any], step: str) -> str:
        logger.debug(f"Raw response for step {step}: {response}")
        try:
            if "completion" in response:
                return response["completion"]
            elif "content" in response:
                content_list = response.get("content", [])
                if content_list:
                    return content_list[0].get("text", "")
            raise ValueError("Invalid response format")
        except Exception as e:
            logger.error(f"Failed to parse response: {str(e)}")
            raise ValueError(f"Error parsing API response for step {step}: {response}")

    def _log_request_start(self, step):
        logger.debug(f"Step '{step}' started at {datetime.datetime.now()}")

    def _log_request_end(self, step):
        logger.debug(f"Step '{step}' ended at {datetime.datetime.now()}")

    def _log_to_markdown(self, log_type: str, header: str, content: str):
        with open("log.md", "a") as f:
            f.write(f"## {header}\n\n{content}\n\n")


@app.route("/", methods=["POST"])
async def process_request():
    data = await request.get_json()
    initial_message = data.get("messages", "")

    logger.debug(f"Incoming message: {initial_message}")

    api_key = os.getenv("ANTHROPIC_API_KEY")
    if not api_key:
        raise ValueError("API key is missing in environment variables or .env file.")

    agent = AIAgent(api_key)

    try:
        result = await agent.run(initial_message)
        logger.debug(f"Final sanitized state: {agent._sanitize_state()}")
        return jsonify({"response": result})
    except Exception as e:
        logger.error(f"Exception during agent execution: {str(e)}")
        return jsonify({"error": str(e)}), 500

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from typing import List, Optional, TypedDict, Literal, Any

# Define the stages that the agent can go through
Stage = Literal['init', 'plan', 'decide', 'describe', 'reflect', 'execute', 'final']

class ITool(TypedDict):
    """
    Represents a tool that the AI agent can use, with a name, instruction, and description.
    """
    name: str
    instruction: str
    description: str

class IAction(TypedDict):
    """
    Represents an action taken by the AI agent, with fields for the action name, payload, result, reflection, and the tool used.
    """
    name: str
    payload: str
    result: str
    reflection: str
    tool: str

class IState(TypedDict, total=False):
    """
    Represents the state of the AI agent, including system prompts, messages, the current stage and step,
    and actions taken so far.
    """
    systemPrompt: str                      # Current system prompt
    messages: List[dict]                   # All messages in the conversation
    currentStage: Stage                    # Stage on which the system prompt depends
    currentStep: int                       # Current step in the agent's operation
    maxSteps: int                          # Maximum number of steps allowed
    activeTool: Optional[ITool]            # The tool currently being used by the agent
    activeToolPayload: Optional[Any]       # Payload for the active tool
    plan: str                              # Current plan of action
    actionsTaken: List[IAction]            # List of actions taken so far

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 # task_manager.py
 class TaskManager:
     def __init__(self):
         pass

     def get_task(self, task_name):
         # Placeholder for retrieving a task based on task_name
         # Replace with actual logic
         tasks = {
             "example_task": {"function": self.example_task}
         }
         return tasks.get(task_name)

     def example_task(self):
         # Example task logic
         return "Task executed successfully"

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 # ssh_manager.py
 import asyncssh
 import asyncio

 class AsyncSSHManager:
     def __init__(self, hostname: str, username: str, password: str):
         self.hostname = hostname
         self.username = username
         self.password = password
         self.connection = None

     async def connect(self):
         """Establish an SSH connection."""
         self.connection = await asyncssh.connect(
             self.hostname,
             username=self.username,
             password=self.password
         )

     async def execute_command(self, command: str) -> str:
         """Execute a command over SSH and return the output."""
         if self.connection is None:
             raise ValueError("No active SSH connection")
         result = await self.connection.run(command, check=True)
         return result.stdout

     async def close_connection(self):
         """Close the SSH connection."""
         if self.connection:
             self.connection.close()
             await self.connection.wait_closed()
             self.connection = None

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import os
import httpx
from dotenv import load_dotenv
import time

# Załaduj zmienne środowiskowe
load_dotenv()

class AnthropicCompletion:
    def __init__(self, api_key: str = None):
        self.api_key = api_key or os.getenv("ANTHROPIC_API_KEY")
        if not self.api_key:
            raise ValueError("API key not provided or incorrectly set in environment variables")

    async def completion(self, messages: list, model: str = "claude-3-5-sonnet-20241022", retries: int = 3, delay: int = 5) -> dict:
        url = "https://api.anthropic.com/v1/messages"
        headers = {
            "x-api-key": self.api_key,
            "Content-Type": "application/json",
            "anthropic-version": "2023-06-01"
        }
        payload = {
            "model": model,
            "messages": messages,
            "max_tokens": 1000,
            "temperature": 0.7
        }

        for attempt in range(retries):
            async with httpx.AsyncClient() as client:
                try:
                    response = await client.post(url, headers=headers, json=payload)
                    if response.status_code == 529:
                        time.sleep(delay)
                        continue
                    response.raise_for_status()
                    return response.json()
                except httpx.RequestError as e:
                    if attempt == retries - 1:
                        raise
                except httpx.HTTPStatusError as e:
                    if attempt == retries - 1:
                        raise

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from typing import Dict

class Prompts:
    @staticmethod
    def tools_instruction() -> Dict[str, str]:
        return {
            "get_html_contents": (
                'Required payload: {"url": "URL that needs to be downloaded"} '
                'Response format: HTML content of the page.'
            ),
            "game_submit_form": (
                'Required payload: {"url": "URL to a file that will be passed to the game"}. '
                'Response format: The game\'s response after submitting the form.'
            ),
            "upload_text_file": (
                'Required payload: {"content": "Text content of the file", '
                '"file_name": "Name of the file (e.g., document.md)"} '
                'Response format: URL of the uploaded file.'
            ),
            "final_answer": (
                'Required payload: {"answer": "Your final answer"}. '
                'Response format: A direct response to the user.'
            ),
            "play_music": (
                'Required payload: JSON object with Spotify API details for actions like search, play, or playlist creation.'
            )
        }

    @staticmethod
    def available_tools() -> Dict[str, str]:
        return {
            "get_html_contents": "Fetch HTML content of a URL.",
            "upload_text_file": "Create and upload a text file.",
            "game_submit_form": "Submit a URL to the game.",
            "final_answer": "Provide the final response to the user.",
            "play_music": "Generate Spotify API JSON for playing or managing music."
        }

    @staticmethod
    def extract_user_query(state) -> str:
        try:
            return next(
                (msg["content"] for msg in state.get("messages", []) if msg.get("role") == "user"),
                "No specific query provided."
            )
        except Exception:
            return "No specific query provided."

    @staticmethod
    def plan_prompt(state) -> str:
        user_query = Prompts.extract_user_query(state)

        return f"""
<main_objective>
Analyze the user's query and decide whether to provide an immediate answer or develop a detailed plan.
</main_objective>

<rules>
- If the query is straightforward (e.g., "How far is the Moon from Earth?"), prioritize addressing it directly.
- If the query requires multiple steps, use available tools to create an actionable plan.
- Always respond with clarity and avoid unnecessary complexity.
</rules>

<user_query>
{user_query}
</user_query>

<available_tools>
{Prompts.tools_instruction()}
</available_tools>
"""

    @staticmethod
    def decide_prompt(state) -> str:
        user_query = Prompts.extract_user_query(state)

        return f"""
<main_objective>
Determine the next step based on the user's query and current context. Either select the appropriate tool to proceed or decide to provide the final answer.
</main_objective>

<rules>
- Be concise and provide a JSON response with the selected tool and reasoning.
- If the question is straightforward, move directly to the final answer.
- Always return a valid JSON string with the tool name.
- The JSON structure must include:
  {{
    "_thoughts": "Your internal reasoning",
    "tool": "precise name of the tool"
  }}
</rules>

<user_query>
{user_query}
</user_query>

<available_tools>
{Prompts.available_tools()}
</available_tools>

<current_plan>
Plan: {state['plan'] if state.get('plan') else 'No plan yet.'}
</current_plan>

<actions_taken>
{state['actionsTaken']}
</actions_taken>
"""

    @staticmethod
    def describe_prompt(state) -> str:
        if "activeTool" not in state or not state["activeTool"].get("tool"):
            raise ValueError("Active tool is not defined or missing the 'tool' property.")

        return f"""
<main_objective>
Provide the required details to execute the tool "{state['activeTool']['tool']}" based on the current state.
</main_objective>

<tool_details>
Tool Name: {state['activeTool']['tool']}
Tool Instructions: {state['activeTool'].get('instruction', 'No instructions available')}
</tool_details>

<actions_taken>
{state['actionsTaken']}
</actions_taken>
"""

    @staticmethod
    def reflection_prompt(state) -> str:
        return f"""
<main_objective>
Reflect on the last action performed and suggest improvements or adjustments to the plan if needed.
</main_objective>

<actions_taken>
{state['actionsTaken']}
</actions_taken>
"""

    @staticmethod
    def final_answer_prompt(state) -> str:
        user_query = Prompts.extract_user_query(state)

        return f"""
<main_objective>
Provide the final answer to the user's query: "{user_query}".
</main_objective>

<rules>
- Directly answer the user's question in a clear and actionable manner.
- If the query is unclear, ask for clarification.
- Summarize key findings and insights.
</rules>

<current_plan>
{state['plan'] if state.get('plan') else 'No plan created.'}
</current_plan>

<actions_taken>
{state['actionsTaken']}
</actions_taken>
"""

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import json
from lib.tools import browse, upload_file, play_music
from lib.prompts import Prompts
from lib.ai import AnthropicCompletion  # Use the AnthropicCompletion class
from typing import Dict, Any

def log_to_markdown(type: str, header: str, content: str):
    """
    Logs content to a markdown file.

    Parameters:
    - type (str): The type of log entry (e.g., 'basic', 'action', 'result').
    - header (str): The header of the log entry.
    - content (str): The content to log.
    """
    formatted_content = f"## {header}\n\n{content}\n\n" if type == "basic" else \
                        f"### {header}\n\n{content}\n\n" if type == "action" else \
                        f"#### {header}\n```\n{content}\n```\n\n"

    with open("log.md", "a") as f:
        f.write(formatted_content)

async def plan(state, anthropic_completion):
    """
    Generates a plan based on the current state.
    """
    state["currentStage"] = "plan"
    state["systemPrompt"] = Prompts.plan_prompt(state)
    state["plan"] = await anthropic_completion.completion(state["systemPrompt"])
    log_to_markdown("basic", "Planning", f"Current plan: {state['plan']}")

async def decide(state, anthropic_completion):
    """
    Decides the next tool to use based on the current state.
    """
    state["currentStage"] = "decide"
    state["systemPrompt"] = Prompts.decide_prompt(state)
    next_step = await anthropic_completion.completion(state["systemPrompt"])
    state["activeTool"] = {
        "name": next_step["tool"],
        "description": Prompts.available_tools().get(next_step["tool"]),
        "instruction": Prompts.tools_instruction().get(next_step["tool"])
    }
    log_to_markdown("action", "Decision", f"Next move: {json.dumps(next_step)}")

async def describe(state, anthropic_completion):
    """
    Generates a payload description for the active tool.
    """
    state["currentStage"] = "describe"
    state["systemPrompt"] = Prompts.describe_prompt(state)
    next_step = await anthropic_completion.completion(state["systemPrompt"])
    state["activeToolPayload"] = next_step
    log_to_markdown("action", "Description", f"Next step description: {json.dumps(next_step)}")

async def execute(state):
    """
    Asynchronously executes the active tool with the generated payload.
    """
    state["currentStage"] = "execute"
    if not state.get("activeTool"):
        raise ValueError("No active tool to execute")

    tool_name = state["activeTool"]["name"]
    payload = state.get("activeToolPayload", {})

    if tool_name == "get_html_contents":
        result = await browse(payload["url"])  # Async call to browse
    elif tool_name == "upload_text_file":
        result = await upload_file(payload)  # Async call to upload_file
    elif tool_name == "play_music":
        result = await play_music(payload)  # Async call to play_music
    else:
        result = f"Tool '{tool_name}' execution not defined."

    log_to_markdown("result", "Execution", f"Action result: {json.dumps(result)}")
    state["actionsTaken"].append({
        "name": tool_name,
        "payload": json.dumps(payload),
        "result": result,
        "reflection": ""
    })

async def reflect(state, anthropic_completion):
    """
    Reflects on the results of the last action.
    """
    state["currentStage"] = "reflect"
    state["systemPrompt"] = Prompts.reflection_prompt(state)
    reflection = await anthropic_completion.completion(state["systemPrompt"])
    state["actionsTaken"][-1]["reflection"] = reflection
    log_to_markdown("basic", "Reflection", reflection)

class AIAgent:
    def __init__(self, api_key: str):
        self.completion_client = AnthropicCompletion(api_key)
        self.state = {
            "currentStage": "init",
            "currentStep": 1,
            "maxSteps": 15,
            "messages": [],
            "systemPrompt": "",
            "plan": "",
            "actionsTaken": []
        }

    async def final_answer(self) -> str:
        """
        Generates the final answer based on the entire process.
        """
        self.state["systemPrompt"] = Prompts.final_answer_prompt(self.state)
        answer = await self.completion_client.completion(self.state["systemPrompt"])
        log_to_markdown("result", "Final Answer", json.dumps(answer))
        return answer

    async def run(self, initial_message: Dict[str, Any]) -> str:
        """
        Executes the agent's full process from planning to providing the final answer.

        Parameters:
        - initial_message (Dict[str, Any]): The initial message or prompt for the agent.

        Returns:
        - str: The final answer.
        """
        self.state["messages"] = [initial_message]

        while self.state["currentStep"] <= self.state["maxSteps"]:
            await plan(self.state, self.completion_client)
            await decide(self.state, self.completion_client)

            if self.state.get("activeTool", {}).get("name") == "final_answer":
                break

            await describe(self.state, self.completion_client)
            await execute(self.state)
            await reflect(self.state, self.completion_client)
            self.state["currentStep"] += 1

        return await self.final_answer()

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import httpx
from markdownify import markdownify as md
import os
import re

async def browse(url: str) -> str:
    """
    Asynchronicznie pobiera zawartość HTML z podanego URL i konwertuje ją do Markdown.

    Parameters:
    - url (str): The URL to fetch content from.

    Returns:
    - str: The markdown content of the fetched HTML, or an error message if the request fails.
    """
    if url in ["https://aidevs.pl", "https://www.aidevs.pl"]:
        return "You can't browse the main website. Try another URL."

    try:
        async with httpx.AsyncClient() as client:
            response = await client.get(url)
            response.raise_for_status()
            html_content = response.text

            # Extract script contents
            script_contents = ""
            script_tags = re.findall(r"<script\b[^>]*>([\s\S]*?)<\/script>", html_content)
            for i, script in enumerate(script_tags, 1):
                script_contents += f"\n\n--- Script {i} ---\n{script}"

            # Convert HTML to markdown using markdownify
            markdown_content = md(html_content)

            # Combine markdown content with script contents
            return f"{markdown_content}\n\n--- Script Contents ---{script_contents}"
    except httpx.RequestError as e:
        print("Error fetching URL:", e)
        return "Failed to fetch the URL, please try again."

async def upload_file(data: dict) -> str:
    """
    Asynchronicznie przesyła plik tekstowy na serwer i zwraca URL pliku.

    Parameters:
    - data (dict): Contains "content" and "file_name" keys.

    Returns:
    - str: The URL of the uploaded file or an error message.
    """
    url = os.getenv("UPLOAD_DOMAIN", "") + "/upload"
    if not url:
        return "ERROR: UPLOAD_DOMAIN environment variable is missing."

    # Sanitize file_name by removing protocol and replacing slashes
    data["file_name"] = data["file_name"].replace("://", "_").replace("/", "_")
    files = {
        'file': (data["file_name"], data["content"], 'text/plain'),
        'file_name': (None, data["file_name"])
    }

    try:
        async with httpx.AsyncClient() as client:
            response = await client.post(url, files=files)
            response.raise_for_status()
            result = response.json()
            return f"Uploaded file to the URL: {result['uploaded_file']}"
    except httpx.RequestError as e:
        print("Upload failed:", e)
        return "Upload failed"

async def play_music(data: dict) -> str:
    """
    Asynchronicznie wysyła żądanie do usługi odtwarzania muzyki.

    Parameters:
    - data (dict): JSON payload for the music service.

    Returns:
    - str: The response from the music service or an error message.
    """
    url = os.getenv("MUSIC_URL", "")
    if not url:
        return "ERROR: MUSIC_URL environment variable is missing."

    try:
        async with httpx.AsyncClient() as client:
            response = await client.post(url, json=data)
            response.raise_for_status()
            result = response.json()
            return result.get("data", "Music playback response received")
    except httpx.RequestError as e:
        print("Error playing music:", e)
        return "Failed to play music"

# Dictionary mapping tool names to functions
tools = {
    'get_html_contents': browse,
    'upload_text_file': upload_file,
    'play_music': play_music
}

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mkdir ansible_project
cd ansible_project
mkdir roles
mkdir playbooks

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mkdir -p roles/environment/tasks
mkdir -p roles/environment/files
mkdir -p roles/application_files/tasks
mkdir -p roles/application_files/files
mkdir -p roles/server_configuration/tasks

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---
- name: Utwórz katalog projektu
  file:
    path: "{{ project_dir }}"
    state: directory

- name: Utwórz środowisko wirtualne
  command: python3 -m venv "{{ project_dir }}/venv"
  args:
    creates: "{{ project_dir }}/venv"

- name: Skopiuj plik requirements.txt do katalogu projektu
  copy:
    src: "requirements.txt"  # Ansible automatycznie wyszuka plik w katalogu `files`
    dest: "{{ project_dir }}/requirements.txt"

- name: Aktywuj środowisko i zainstaluj wymagane pakiety
  pip:
    requirements: "{{ project_dir }}/requirements.txt"
    virtualenv: "{{ project_dir }}/venv"
    virtualenv_command: python3 -m venv

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---
- name: Skopiuj plik index.py do katalogu projektu
  copy:
    src: roles/application_files/files/index.py
    dest: "{{ project_dir }}/index.py"

- name: Skopiuj plik types.dt.py do katalogu projektu
  copy:
    src: roles/application_files/files/types.dt.py
    dest: "{{ project_dir }}/types.dt.py"

- name: Skopiuj plik README.md do katalogu projektu
  copy:
    src: roles/application_files/files/README.md
    dest: "{{ project_dir }}/README.md"

- name: Skopiuj plik log.md do katalogu projektu
  copy:
    src: roles/application_files/files/log.md
    dest: "{{ project_dir }}/log.md"

- name: Utwórz katalog lib
  file:
    path: "{{ project_dir }}/lib"
    state: directory

- name: Skopiuj plik ai.py do katalogu lib
  copy:
    src: roles/application_files/files/ai.py
    dest: "{{ project_dir }}/lib/ai.py"

- name: Skopiuj plik prompts.py do katalogu lib
  copy:
    src: roles/application_files/files/prompts.py
    dest: "{{ project_dir }}/lib/prompts.py"

- name: Skopiuj plik agent.py do katalogu lib
  copy:
    src: roles/application_files/files/agent.py
    dest: "{{ project_dir }}/lib/agent.py"

- name: Skopiuj plik tools.py do katalogu lib
  copy:
    src: roles/application_files/files/tools.py
    dest: "{{ project_dir }}/lib/tools.py"

- name: Skopiuj plik task_manager.py do katalogu projektu
  copy:
    src: roles/application_files/files/task_manager.py
    dest: "{{ project_dir }}/task_manager.py"

- name: Skopiuj plik ssh_manager.py do katalogu projektu
  copy:
    src: roles/application_files/files/ssh_manager.py
    dest: "{{ project_dir }}/ssh_manager.py"

- name: Skopiuj plik asgi_app.py do katalogu projektu
  copy:
    src: roles/application_files/files/asgi_app.py
    dest: "{{ project_dir }}/asgi_app.py"

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---
- name: Skonfiguruj plik .env
  copy:
    dest: "{{ project_dir }}/.env"
    content: |
      UPLOAD_DOMAIN=http://localhost:3000
      ANTHROPIC_API_KEY=your_anthropic_api_key      

- name: Uruchom serwer aplikacji z Uvicorn
  shell: |
    source "{{ project_dir }}/venv/bin/activate"
    nohup uvicorn index:app --host 0.0.0.0 --port 3000 &    
  args:
    executable: /bin/bash
  register: app_start_result
  ignore_errors: true  # Ignoruj błędy na wypadek ponownego uruchomienia

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---
- name: Konfiguracja i instalacja projektu AI Dev Agent
  hosts: localhost
  vars:
    project_dir: "/home/adrian/aidevs/agent"
  roles:
    - role: environment
    - role: application_files
    - role: server_configuration

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zip -r ansible_project.zip ansible_project/

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sudo apt install ansible yamllint
yamllint site.yml
yamllint roles/environment/tasks/main.yml
yamllint roles/application_files/tasks/main.yml
yamllint roles/server_configuration/tasks/main.yml
ansible-playbook site.yml --syntax-check
ansible-playbook site.yml --check
ansible-playbook site.yml