"""
Content Moderation Inference Script
Env vars: API_BASE_URL, MODEL_NAME, HF_TOKEN, SERVER_URL, TASK_NAME
"""

import json
import os
import textwrap
from typing import Dict, Any, List, Optional

from dotenv import load_dotenv
import requests

load_dotenv()

API_BASE_URL = os.getenv("API_BASE_URL", "https://router.huggingface.co/v1")
MODEL_NAME = os.getenv("MODEL_NAME", "meta-llama/Llama-3.1-8B-Instruct")
HF_TOKEN = os.getenv("HF_TOKEN")
API_KEY = HF_TOKEN or os.getenv("API_KEY")
SERVER_URL = os.getenv("SERVER_URL", "http://localhost:7860").rstrip("/")
TASK_NAME = os.getenv("TASK_NAME", "text_spam")
BENCHMARK = "content_moderation_env"
MAX_STEPS = 10
USE_LOCAL = os.getenv("USE_LOCAL_MODEL", "false").lower() == "true"
SUCCESS_SCORE_THRESHOLD = 0.5

VALID_DECISIONS = {"approve", "reject", "escalate", "flag"}
VALID_LABELS = [
    "spam", "scam", "phishing", "pharmaceutical_spam",
    "hate_speech", "violence", "harassment", "misinformation",
    "adult_content", "deepfake", "political_manipulation", "fraud",
]

SYSTEM_PROMPT = textwrap.dedent("""
You are an expert content moderation AI. For each piece of content, analyze it carefully and respond ONLY with a valid JSON object.

Required JSON fields:
- "decision": one of "approve", "reject", "escalate", "flag"
- "reason": brief explanation (max 2 sentences)
- "confidence": float between 0.0 and 1.0
- "labels": list of applicable labels

Decision guidelines:
- approve: content is safe and legitimate
- reject: content clearly violates policy
- flag: content is borderline or needs human review
- escalate: uncertain, requires specialist review

Output ONLY the JSON object, no other text.
""").strip()


def log_start(task: str, env: str, model: str):
    print(f"[START] task={task} env={env} model={model}", flush=True)


def log_step(step: int, action: str, reward: float, done: bool, error: Optional[str]):
    err = error if error else "null"
    print(f"[STEP] step={step} action={action} reward={reward:.2f} done={str(done).lower()} error={err}", flush=True)


def log_end(success: bool, steps: int, score: float, rewards: List[float]):
    rewards_str = ",".join(f"{r:.2f}" for r in rewards)
    print(f"[END] success={str(success).lower()} steps={steps} score={score:.3f} rewards={rewards_str}", flush=True)


def build_prompt(obs: Dict[str, Any]) -> str:
    parts = [f"Content ID: {obs.get('content_id', 'unknown')}"]
    parts.append(f"Type: {obs.get('content_type', 'text')}")

    if obs.get("text"):
        parts.append(f"Text: {obs['text']}")
    if obs.get("image_description"):
        parts.append(f"Image analysis: {obs['image_description']}")
    if obs.get("detector_score") is not None:
        score = obs["detector_score"]
        parts.append(f"Deepfake detector score (higher = more likely fake): {score:.3f}")

    meta = obs.get("metadata", {})
    if meta:
        meta_str = ", ".join(f"{k}={v}" for k, v in meta.items())
        parts.append(f"Metadata: {meta_str}")

    parts.append(f"\nStep {obs.get('step_num', '?')} of {obs.get('total_steps', '?')}")
    return "\n".join(parts)


def _default_action() -> Dict:
    return {"decision": "escalate", "reason": "Unable to analyze content.", "confidence": 0.3, "labels": []}


def call_local_model(prompt: str) -> Dict:
    from transformers import pipeline

    pipe = pipeline("text-generation", model="meta-llama/Llama-3.1-8B-Instruct")
    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": prompt},
    ]
    output = pipe(messages, max_new_tokens=256, temperature=0.2, do_sample=True)
    text = output[0]["generated_text"]
    if isinstance(text, list):
        text = text[-1].get("content", "")
    return parse_llm_response(text)


def call_api_model(prompt: str) -> Dict:
    from openai import OpenAI

    client = OpenAI(base_url=API_BASE_URL, api_key=API_KEY or "hf_default")
    completion = client.chat.completions.create(
        model=MODEL_NAME,
        messages=[
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "user", "content": prompt},
        ],
        temperature=0.2,
        max_tokens=256,
    )
    text = (completion.choices[0].message.content or "").strip()
    return parse_llm_response(text)


def parse_llm_response(text: str) -> Dict:
    try:
        start = text.find("{")
        end = text.rfind("}") + 1
        if start >= 0 and end > start:
            parsed = json.loads(text[start:end])
            decision = parsed.get("decision", "escalate")
            if decision not in VALID_DECISIONS:
                decision = "escalate"
            return {
                "decision": decision,
                "reason": str(parsed.get("reason", ""))[:200],
                "confidence": float(max(0.0, min(1.0, parsed.get("confidence", 0.5)))),
                "labels": [l for l in parsed.get("labels", []) if l in VALID_LABELS],
            }
    except Exception:
        pass
    return _default_action()


def get_decision(prompt: str) -> Dict:
    try:
        if USE_LOCAL:
            return call_local_model(prompt)
        return call_api_model(prompt)
    except Exception as e:
        print(f"[DEBUG] Model error: {e}", flush=True)
        return _default_action()


def server_reset(task: str) -> Optional[Dict]:
    try:
        r = requests.post(f"{SERVER_URL}/reset", json={"task": task}, timeout=30)
        r.raise_for_status()
        return r.json()
    except Exception as e:
        print(f"[DEBUG] reset error: {e}", flush=True)
        return None


def server_step(action: Dict) -> Optional[Dict]:
    try:
        r = requests.post(f"{SERVER_URL}/step", json=action, timeout=30)
        r.raise_for_status()
        return r.json()
    except Exception as e:
        print(f"[DEBUG] step error: {e}", flush=True)
        return None


def server_close():
    try:
        requests.post(f"{SERVER_URL}/close", timeout=10)
    except Exception:
        pass


def run_episode(task: str):
    rewards: List[float] = []
    steps_taken = 0
    score = 0.0
    success = False
    obs = None

    log_start(task=task, env=BENCHMARK, model=MODEL_NAME)

    try:
        reset_result = server_reset(task)
        if reset_result is None:
            log_end(success=False, steps=0, score=0.0, rewards=[])
            return

        obs = reset_result.get("observation", {})
        done = False

        for step in range(1, MAX_STEPS + 1):
            if done or obs is None:
                break

            prompt = build_prompt(obs)
            action = get_decision(prompt)
            action_str = json.dumps({k: v for k, v in action.items() if k != "reason"})

            result = server_step(action)
            if result is None:
                log_step(step, action_str, 0.0, True, "server_error")
                break

            reward = float(result.get("reward", 0.0))
            done = bool(result.get("done", False))
            error = result.get("info", {}).get("error")

            rewards.append(reward)
            steps_taken = step

            log_step(step, action_str, reward, done, error)

            obs = result.get("observation")

        total_steps_in_task = obs.get("total_steps", len(rewards)) if obs else len(rewards)
        max_possible = float(total_steps_in_task)
        score = sum(rewards) / max_possible if max_possible > 0 else 0.0
        score = min(max(score, 0.0), 1.0)
        success = score >= SUCCESS_SCORE_THRESHOLD

    finally:
        server_close()
        log_end(success=success, steps=steps_taken, score=score, rewards=rewards)


if __name__ == "__main__":
    run_episode(TASK_NAME)