import pyaudio, wave, time, os, numpy as np, requests, subprocess, sys, json
from datetime import datetime
from dotenv import load_dotenv
load_dotenv()

ROOT_DIR = os.getenv('ROOT_DIR', os.path.dirname(__file__))

##########################################################################################

##### OLLAMA ##### 
def ping_api(url):
    """Ping the specified API URL and return the response status."""
    try:
        # Send a GET request to the API
        response = requests.get(url)
        
        # Check the response status code
        if response.status_code == 200:
            print("API is reachable.")
            return True
        else:
            print(f"API responded with status code: {response.status_code}")
            return False
    except requests.exceptions.RequestException as e:
        print(f"Error pinging API: {e}")
        return False

def start_ollama_server(llm_model):
    """Start the Ollama server using subprocess."""
    # Adjust the command as necessary for your setup
    command = ["ollama", "run", "--keepalive", "24h", llm_model, 'Don\'t Say Anything']  # Replace with the correct command to start the server

    # Start the server in a new process
    subprocess.run(command)



def converse(input_text, ping_url, api_url, llm_model, llm_response_path, prompt, context_file, have_context = False):
    """
    Send a prompt to the Ollama API and return the response.
    input_text: The text to send to the API.
    ping_url: The URL to ping to check if the API is running.
    api_url: The URL of the Ollama API. 
    llm_model: The LLM model to use.
    llm_response_path: The path to save the LLM responses and prompts.
    prompt: The prompt to use for the conversation.
    context_file: The path to the context file.
    """
    # Ping the Llama
    if not ping_api(ping_url):
        try:
            start_ollama_server(llm_model)
        except Exception as e:
            print(f"Error starting Ollama server: {e}. If you are using another Ollama server, please ensure you have correctly specified the BASE_URL and that the server is running and not firewalled off.")
            sys.exit(1)

    payload = { "model": llm_model, "prompt": f'{prompt} {input_text}', "stream": False, "keep_alive": "24h" }

    if have_context:
        # load json context file
        with open(context_file, 'r') as f:
            context = json.load(f).get('context')
        payload.update({'context': context})

    # Make the POST request
    response = requests.post(api_url, json=payload)

    # Check for errors and print the response
    if not response.ok:
        print("Error:", response.status_code, response.text)
    
    # Save the context and all other responses of the API call to a file
    with open(context_file, 'w') as f:
        json.dump(response.json(), f)

    # Save the conversations to a file
    with open(llm_response_path, "a") as f:
        f.write(f'[{datetime.now().isoformat()}] Prompt: {input_text}\n')
        f.write(f'[{response.json().get('created_at')}] Response: {response.json().get('response')}\n')

    return response.json().get('response')