inference.py

"""
CodeSensei — Inference Script (OpenEnv Hackathon)
===================================================
MANDATORY ENV VARS:
    API_BASE_URL   The API endpoint for the LLM.
    MODEL_NAME     The model identifier to use for inference.
    HF_TOKEN       Your Hugging Face / API key.
    LOCAL_IMAGE_NAME  Docker image name for from_docker_image()

STDOUT FORMAT (must be exact):
    [START] task=<task_name> env=<benchmark> model=<model_name>
    [STEP]  step=<n> action=<action_str> reward=<0.00> done=<true|false> error=<msg|null>
    [END]   success=<true|false> steps=<n> score=<score> rewards=<r1,r2,...,rn>
"""

import asyncio
import os
import re
import textwrap
from typing import List, Optional

from openai import OpenAI

# ---------------------------------------------------------------------------
# Import our OpenEnv environment (mirrors: from my_env_v4 import ...)
# ---------------------------------------------------------------------------
from env import CodeSenseiEnv, CodeSenseiAction

# ---------------------------------------------------------------------------
# Env vars (submission contract — matches sample exactly)
# ---------------------------------------------------------------------------
IMAGE_NAME = os.getenv("LOCAL_IMAGE_NAME")
API_KEY = os.getenv("HF_TOKEN") or os.getenv("API_KEY")

API_BASE_URL = os.getenv("API_BASE_URL", "https://api.groq.com/openai/v1")
MODEL_NAME = os.getenv("MODEL_NAME", "llama-3.3-70b-versatile")

TASK_NAME = os.getenv("CODESENSEI_TASK", "code-debug")
BENCHMARK = os.getenv("CODESENSEI_BENCHMARK", "codesensei")

MAX_STEPS = 6
TEMPERATURE = 0.7
MAX_TOKENS = 512
SUCCESS_SCORE_THRESHOLD = 0.5

# Max total reward: best case +2.0 per step, 6 steps
MAX_TOTAL_REWARD = MAX_STEPS * 2.0

# ---------------------------------------------------------------------------
# System prompt
# ---------------------------------------------------------------------------
SYSTEM_PROMPT = textwrap.dedent("""\
You are CodeSensei, an expert Python debugger.

TASK: You are given a buggy Python function and its failing test cases.
Find the bug and return ONLY the corrected function.

RULES:
1. Return ONLY the corrected Python function — no explanations.
2. Keep the same function name and signature.
3. Wrap your code in ```python ... ``` markers.
4. Think step-by-step: identify failing tests, find the bug, then fix it.

EXAMPLE:
Given:
```python
def add(a, b):
    return a - b
```
Return:
```python
def add(a, b):
    return a + b
```
""").strip()


# ---------------------------------------------------------------------------
# Structured logging — [START], [STEP], [END]  (exact format from sample)
# ---------------------------------------------------------------------------
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:
    error_val = error if error else "null"
    done_val = str(done).lower()
    print(
        f"[STEP] step={step} action={action} reward={reward:.2f} done={done_val} error={error_val}",
        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)


# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def extract_code(text: str) -> str:
    """Extract code from markdown fences or raw def statement."""
    match = re.search(r"```(?:python)?\s*\n(.*?)```", text, re.DOTALL)
    if match:
        return match.group(1).strip()
    match = re.search(r"(def \w+\(.*?\):.*)", text, re.DOTALL)
    if match:
        return match.group(1).strip()
    return text.strip()


def build_user_prompt(step: int, buggy_code: str, feedback: str, history: List[str]) -> str:
    history_block = "\n".join(history[-4:]) if history else "None"
    return textwrap.dedent(f"""\
Step: {step}
Buggy code:
```python
{buggy_code}
```
Last feedback: {feedback}
Previous attempts:
{history_block}
Send your corrected function.
""").strip()


def get_model_message(client: OpenAI, step: int, buggy_code: str, feedback: str, history: List[str]) -> str:
    user_prompt = build_user_prompt(step, buggy_code, feedback, history)
    try:
        completion = 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,
        )
        text = (completion.choices[0].message.content or "").strip()
        return text if text else "hello"
    except Exception as exc:
        print(f"[DEBUG] Model request failed: {exc}", flush=True)
        return "hello"


# ---------------------------------------------------------------------------
# Main — follows sample structure exactly
# ---------------------------------------------------------------------------
async def main() -> None:
    client = OpenAI(base_url=API_BASE_URL, api_key=API_KEY)

    # OpenEnv standard: start env from Docker image
    env = await CodeSenseiEnv.from_docker_image(IMAGE_NAME)

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

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

    try:
        result = await env.reset()                          # OpenEnv.reset()
        last_feedback = result.observation.feedback or "All tests failing — fix the bug."
        last_reward = 0.0
        buggy_code = result.observation.buggy_code

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

            message = get_model_message(client, step, buggy_code, last_feedback, history)

            proposed_fix = extract_code(message)

            result = await env.step(CodeSenseiAction(proposed_fix=proposed_fix))
            obs = result.observation

            reward = result.reward or 0.0
            done = result.done
            error = obs.error_output if obs.error_output else None

            rewards.append(reward)
            steps_taken = step
            last_feedback = obs.feedback or f"{obs.tests_passed}/{obs.tests_total} tests passing"
            last_reward = reward

            # Action for log: just the function name fix summary
            action_str = proposed_fix.replace("\n", "\\n")[:120]
            log_step(step=step, action=action_str, reward=reward, done=done, error=error)

            history.append(f"Step {step}: {obs.tests_passed}/{obs.tests_total} tests -> reward {reward:+.2f}")

            if done:
                break

        # Score strictly bounded to (0, 1) per Phase 2 requirements
        score = sum(rewards) / MAX_TOTAL_REWARD if MAX_TOTAL_REWARD > 0 else 0.01
        score = min(max(score, 0.01), 0.99)  # strict bounds
        success = score >= SUCCESS_SCORE_THRESHOLD

    finally:
        try:
            await env.close()
        except Exception as e:
            print(f"[DEBUG] env.close() error (container cleanup): {e}", flush=True)
        log_end(success=success, steps=steps_taken, score=score, rewards=rewards)


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