import os
import subprocess
from datetime import datetime

# Viral Content Generator Agent
PREFIX = """You are an Advanced Viral Content Generator with Self-Research and Self-Improvement Capabilities
You can generate viral content across multiple formats: blog articles, books, review articles, and academic papers
You have access to the following tools and capabilities:
- Self-Insight: Generate new content ideas based on trending topics and user preferences
- Self-Research: Use real-time internet searches to gather information for your content
- Content Generation: Create content in various formats with viral potential
- Self-Evaluation: Assess the quality and potential of your generated content
- Publishing Pipeline: Format and structure content for publication

Trigger tools by using this format:
action: TOOL_NAME action_input=YOUR_INPUT

Your workflow:
1. Generate or receive content ideas
2. Research the topic thoroughly
3. Create content in the requested format (or determine the optimal format)
4. Self-evaluate and improve the content
5. Format for publication
6. Repeat or generate new ideas based on performance

Current Date/Time: {date_time_str}

Purpose:
{purpose}
"""

LOG_PROMPT = """
PROMPT
**************************************
{}
**************************************
"""

def run_gpt(
    prompt_template,
    stop_tokens,
    max_tokens,
    purpose,
    **prompt_kwargs,
):
    """Generate a response using a local Ollama model (gpt-oss by default)."""

    content = PREFIX.format(
        date_time_str=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
        purpose=purpose,
    ) + prompt_template.format(**prompt_kwargs)

    models = _preferred_models()
    last_error = None
    for model in models:
        try:
            raw_response = _call_ollama(model, content, max_tokens)
            response = _strip_prompt(content, raw_response)
            if stop_tokens:
                for token in stop_tokens:
                    if token in response:
                        response = response.split(token)[0]
                        break
            return response.strip()
        except Exception as exc:  # pylint: disable=broad-except
            last_error = exc
            print(f"[LLM]: Failed with model '{model}': {exc}")

    raise RuntimeError(f"All Ollama model attempts failed: {last_error}")


def _preferred_models():
    env_model = os.getenv("OLLAMA_MODEL")
    candidates = [env_model, "gpt-oss", "qwen3"]
    return [model for model in candidates if model]


def _call_ollama(model: str, prompt: str, max_tokens: int) -> str:
    """Invoke the local Ollama runtime with the provided prompt."""
    max_predict = max(64, int(max_tokens or 0))
    cmd = [
        "ollama",
        "run",
        "--num-predict",
        str(max_predict),
        model,
    ]
    try:
        completed = subprocess.run(
            cmd,
            input=prompt.encode("utf-8"),
            capture_output=True,
            check=False,
        )
    except FileNotFoundError as exc:
        raise RuntimeError("Ollama executable not found. Please install Ollama or adjust PATH.") from exc

    if completed.returncode != 0:
        stderr = completed.stderr.decode("utf-8", errors="ignore")
        raise RuntimeError(stderr.strip() or f"ollama run {model} failed with code {completed.returncode}")

    return completed.stdout.decode("utf-8", errors="ignore")


def _strip_prompt(prompt: str, raw_response: str) -> str:
    """Some models echo the prompt; remove it if present."""
    if raw_response.startswith(prompt):
        return raw_response[len(prompt):]
    return raw_response