inference.py

#!/usr/bin/env python3
from __future__ import annotations

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

from openai import OpenAI

try:
    from code_security_auditor_env import CodeSecurityAction, CodeSecurityAuditorEnv
except ImportError:
    from client import CodeSecurityAuditorEnv
    from models import CodeSecurityAction

API_BASE_URL = os.getenv("API_BASE_URL", "https://router.huggingface.co/v1")
MODEL_NAME = os.getenv("MODEL_NAME", "Qwen/Qwen2.5-72B-Instruct")
API_KEY = os.getenv("HF_TOKEN") or os.getenv("API_KEY")
LOCAL_IMAGE_NAME = os.getenv("LOCAL_IMAGE_NAME")
ENV_BASE_URL = os.getenv("ENV_BASE_URL")
DEFAULT_ENV_BASE_URL = os.getenv("DEFAULT_ENV_BASE_URL", "http://127.0.0.1:8000")
DEFAULT_LOCAL_IMAGE_NAME = os.getenv("DEFAULT_LOCAL_IMAGE_NAME", "code-security-auditor-env:latest")
TASK_IDS = [t.strip() for t in os.getenv("TASK_IDS", "easy,medium,hard").split(",") if t.strip()]
MAX_STEPS = int(os.getenv("MAX_STEPS", "12"))
TEMPERATURE = 0.0
MAX_TOKENS = 260
BENCHMARK = "code_security_auditor_env"
MIN_STRICT_SCORE = 0.001
MAX_STRICT_SCORE = 0.999

SYSTEM_PROMPT = (
    "You are a senior application security reviewer. Produce strictly valid JSON for the next action. "
    "Allowed action_type values: inspect_file, submit_finding, submit_final_report. "
    "Do not include markdown fences. Keep fields concise and accurate."
)


def log_start(task: str, env: str, model: str) -> None:
    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]) -> None:
    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]) -> None:
    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 _compact_action_str(action: Dict[str, Any]) -> str:
    return json.dumps(action, separators=(",", ":"), ensure_ascii=True)


def _default_action() -> Dict[str, Any]:
    return {
        "action_type": "submit_final_report",
        "confidence": 0.5,
        "summary": "fallback-finalize",
        "evidence": "fallback-finalize",
    }


def _safe_error(exc: Exception) -> str:
    msg = str(exc).strip()
    if not msg:
        msg = exc.__class__.__name__
    return msg.replace("\n", " ")[:240]


def _parse_action(raw: str, available_files: List[str]) -> Dict[str, Any]:
    try:
        parsed = json.loads(raw)
        if not isinstance(parsed, dict):
            return _default_action()
    except Exception:
        return _default_action()

    action_type = parsed.get("action_type")
    if action_type not in {"inspect_file", "submit_finding", "submit_final_report"}:
        return _default_action()

    action: Dict[str, Any] = {
        "action_type": action_type,
        "confidence": float(parsed.get("confidence", 0.5)),
        "summary": str(parsed.get("summary", ""))[:400],
        "evidence": str(parsed.get("evidence", ""))[:700],
    }

    if parsed.get("filename"):
        filename = str(parsed["filename"])
        if filename in available_files:
            action["filename"] = filename
    if parsed.get("line_start") is not None:
        try:
            action["line_start"] = max(1, int(parsed["line_start"]))
        except Exception:
            pass
    if parsed.get("line_end") is not None:
        try:
            action["line_end"] = max(1, int(parsed["line_end"]))
        except Exception:
            pass
    if parsed.get("vuln_type") is not None:
        action["vuln_type"] = str(parsed["vuln_type"])
    if parsed.get("severity") is not None:
        action["severity"] = str(parsed["severity"])

    action["confidence"] = min(1.0, max(0.0, action["confidence"]))

    return action


def _build_prompt(obs: Any, step: int) -> str:
    findings = obs.findings_so_far[-4:] if obs.findings_so_far else []
    snippet = obs.file_excerpt[:1800] if obs.file_excerpt else ""
    return (
        f"Task: {obs.task_id} ({obs.difficulty})\\n"
        f"Objective: {obs.objective}\\n"
        f"Step: {step}\\n"
        f"Steps remaining: {obs.steps_remaining}\\n"
        f"Files: {', '.join(obs.available_files)}\\n"
        f"Last feedback: {obs.last_feedback}\\n"
        f"Focused file: {obs.focused_file}\\n"
        f"Recent findings: {json.dumps(findings)}\\n"
        f"Visible snippet:\\n{snippet}\\n"
        "Return one JSON object with action_type and required fields."
    )


def _query_model(client: OpenAI, obs: Any, step: int) -> Dict[str, Any]:
    user_prompt = _build_prompt(obs, step)
    try:
        resp = client.chat.completions.create(
            model=MODEL_NAME,
            messages=[
                {"role": "system", "content": SYSTEM_PROMPT},
                {"role": "user", "content": user_prompt},
            ],
            temperature=TEMPERATURE,
            max_tokens=MAX_TOKENS,
            stream=False,
        )
        content = (resp.choices[0].message.content or "").strip()
        return _parse_action(content, obs.available_files)
    except Exception:
        return _default_action()


async def _create_env() -> CodeSecurityAuditorEnv:
    # Prefer explicit configuration, then fall back to common local defaults.
    if ENV_BASE_URL:
        return CodeSecurityAuditorEnv(base_url=ENV_BASE_URL)

    if LOCAL_IMAGE_NAME:
        return await CodeSecurityAuditorEnv.from_docker_image(LOCAL_IMAGE_NAME)

    try:
        return CodeSecurityAuditorEnv(base_url=DEFAULT_ENV_BASE_URL)
    except Exception:
        return await CodeSecurityAuditorEnv.from_docker_image(DEFAULT_LOCAL_IMAGE_NAME)


async def run_task(env: CodeSecurityAuditorEnv, client: OpenAI, task_id: str) -> float:
    log_start(task=task_id, env=BENCHMARK, model=MODEL_NAME)

    rewards: List[float] = []
    steps_taken = 0
    score = 0.0
    success = False

    try:
        result = await env.reset(task_id=task_id)
        obs = result.observation

        for step in range(1, MAX_STEPS + 1):
            if result.done:
                break

            action_dict = _query_model(client, obs, step)
            action_str = _compact_action_str(action_dict)

            action = CodeSecurityAction(**action_dict)
            result = await env.step(action)
            obs = result.observation

            reward = float(result.reward or 0.0)
            done = bool(result.done)
            error = obs.metadata.get("last_action_error")

            rewards.append(reward)
            steps_taken = step
            log_step(step=step, action=action_str, reward=reward, done=done, error=error)

            if done:
                break

        score = float(obs.reward or 0.0)
        score = min(max(score, MIN_STRICT_SCORE), MAX_STRICT_SCORE)
        success = score >= 0.6
    except Exception as exc:
        # Keep evaluator contract: do not crash inference.py on transient/runtime errors.
        log_step(step=max(1, steps_taken), action="{}", reward=0.0, done=True, error=_safe_error(exc))
        if not rewards:
            rewards.append(0.0)
        steps_taken = max(1, steps_taken)
        score = MIN_STRICT_SCORE
        success = False
    finally:
        log_end(success=success, steps=steps_taken, score=score, rewards=rewards)

    return score


async def main() -> None:
    # Keep script resilient in validators even if a key is temporarily unavailable.
    api_key = API_KEY or "missing"

    client = OpenAI(base_url=API_BASE_URL, api_key=api_key)

    try:
        env = await _create_env()
    except Exception as exc:
        # Emit structured logs for each task and exit cleanly.
        err = _safe_error(exc)
        for task_id in TASK_IDS:
            log_start(task=task_id, env=BENCHMARK, model=MODEL_NAME)
            log_step(step=1, action="{}", reward=0.0, done=True, error=err)
            log_end(success=False, steps=1, score=MIN_STRICT_SCORE, rewards=[MIN_STRICT_SCORE])
        return

    try:
        scores: List[float] = []
        for task_id in TASK_IDS:
            score = await run_task(env, client, task_id)
            scores.append(score)

        # Keep strict output format requirement: no extra structured tags beyond START/STEP/END.
        _ = scores
    finally:
        await env.close()


if __name__ == "__main__":
    asyncio.run(main())