Delete inference.py

inference.py

@@ -1,251 +0,0 @@
-#!/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"
-
-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, 0.0), 1.0)
-        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 = 0.0
-        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=0.0, rewards=[0.0])
-        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())