Delete inferency.py

inferency.py

@@ -1,245 +0,0 @@
-"""
-Inference Script Example
-===================================
-MANDATORY
-- Before submitting, ensure the following variables are defined in your environment configuration:
-    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 The name of the local image to use for the environment if you are using from_docker_image()
-                     method
-
-- Defaults are set only for API_BASE_URL and MODEL_NAME 
-    (and should reflect your active inference setup):
-    API_BASE_URL = os.getenv("API_BASE_URL", "<your-active-endpoint>")
-    MODEL_NAME = os.getenv("MODEL_NAME", "<your-active-model>")
-    
-- The inference script must be named `inference.py` and placed in the root directory of the project
-- Participants must use OpenAI Client for all LLM calls using above variables
-
-STDOUT FORMAT
-- The script must emit exactly three line types to stdout, in this order:
-
-    [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> rewards=<r1,r2,...,rn>
-
-  Rules:
-    - One [START] line at episode begin.
-    - One [STEP] line per step, immediately after env.step() returns.
-    - One [END] line after env.close(), always emitted (even on exception).
-    - reward and rewards are formatted to 2 decimal places.
-    - done and success are lowercase booleans: true or false.
-    - error is the raw last_action_error string, or null if none.
-    - All fields on a single line with no newlines within a line.
-
-  Example:
-    [START] task=click-test env=miniwob model=Qwen3-VL-30B
-    [STEP] step=1 action=click('123') reward=0.00 done=false error=null
-    [STEP] step=2 action=fill('456','text') reward=0.00 done=false error=null
-    [STEP] step=3 action=click('789') reward=1.00 done=true error=null
-    [END] success=true steps=3 rewards=0.00,0.00,1.00
-"""
-
-import asyncio
-import os
-import textwrap
-from typing import List, Optional
-
-from openai import OpenAI
-from dotenv import load_dotenv
-
-# Load environment variables from .env file if present
-load_dotenv()
-
-from code_assessment_env import CodeAssessmentAction, CodeAssessmentEnv
-IMAGE_NAME = os.getenv("IMAGE_NAME", "code_assessment_env:latest")
-API_KEY = os.getenv("HF_TOKEN") or os.getenv("API_KEY")
-
-API_BASE_URL = os.getenv("API_BASE_URL") or "https://router.huggingface.co/v1"
-MODEL_NAME = os.getenv("MODEL_NAME") or "Qwen/Qwen2.5-72B-Instruct"
-TASK_NAME = os.getenv("TASK_NAME", "code_output_assessment")
-BENCHMARK = os.getenv("BENCHMARK", "first_rl_proj")
-MAX_STEPS = 15
-TEMPERATURE = 0.7
-MAX_TOKENS = 200
-SUCCESS_SCORE_THRESHOLD = 0.5  # normalized score in [0, 1]
-
-# Max possible reward with normalized grading (0-1) × difficulty multipliers:
-# Easy (1x): ~5 problems × 1.0 = 5.0
-# Medium (2x): ~5 problems × 2.0 = 10.0
-# Hard (5x): ~5 problems × 5.0 = 25.0
-# Streak bonuses: ~3-4 bonuses × 0.5 = 1.5-2.0
-# Total possible: ~40.0 with perfect performance
-MAX_TOTAL_REWARD = 40.0
-
-SYSTEM_PROMPT = textwrap.dedent(
-    """
-    You are solving coding problems at different difficulty levels.
-    
-    For each problem:
-    1. Read the problem description carefully
-    2. Look at the test case input provided
-    3. Calculate or determine the correct output
-    4. Respond with ONLY the answer - no explanations, just the exact output value
-    
-    Examples:
-    - If asked to add "3,5", respond: 8
-    - If asked to reverse "hello", respond: olleh  
-    - If asked for palindrome check "racecar", respond: true
-    
-    Be precise with formatting:
-    - For lists, use comma-separated values: "1,2,3"
-    - For true/false, use lowercase: "true" or "false"
-    - For numbers, no extra spaces or characters
-    
-    You'll get higher rewards for:
-    - Correct answers (especially on hard problems)
-    - Maintaining a streak of correct answers
-    - Solving problems quickly
-    
-    Focus on accuracy. Partial credit is available for close answers.  
-    """
-).strip()
-
-
-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)
-
-
-def build_user_prompt(
-    step: int, 
-    problem: str, 
-    test_input: str, 
-    difficulty: str,
-    feedback: str,
-    is_correct: bool,
-    streak: int,
-    problems_solved: int
-) -> str:
-    status = "✓ CORRECT!" if is_correct else feedback
-    
-    return textwrap.dedent(
-        f"""
-        Step {step}/15 | Difficulty: {difficulty.upper()} | Solved: {problems_solved} | Streak: {streak}
-        
-        Problem: {problem}
-        Test Input: {test_input}
-        
-        Previous Feedback: {status}
-        
-        What is the output? (respond with just the answer)
-        """
-    ).strip()
-
-
-def get_model_answer(
-    client: OpenAI,
-    step: int,
-    problem: str,
-    test_input: str,
-    difficulty: str,
-    feedback: str,
-    is_correct: bool,
-    streak: int,
-    problems_solved: int
-) -> str:
-    user_prompt = build_user_prompt(step, problem, test_input, difficulty, feedback, is_correct, streak, problems_solved)
-    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 "0"
-    except Exception as exc:
-        print(f"[DEBUG] Model request failed: {exc}", flush=True)
-        return "0"
-
-
-async def main() -> None:
-    client = OpenAI(base_url=API_BASE_URL, api_key=API_KEY)
-
-    env = await CodeAssessmentEnv.from_docker_image(IMAGE_NAME)
-
-    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()
-        obs = result.observation
-        
-        for step in range(1, MAX_STEPS + 1):
-            if result.done:
-                break
-
-            # Get model's answer for the current problem
-            answer = get_model_answer(
-                client=client,
-                step=step,
-                problem=obs.problem_description,
-                test_input=obs.test_case_input,
-                difficulty=obs.difficulty,
-                feedback=obs.feedback,
-                is_correct=obs.is_correct,
-                streak=obs.current_streak,
-                problems_solved=obs.problems_solved,
-            )
-
-            # Submit answer
-            result = await env.step(CodeAssessmentAction(answer=answer))
-            obs = result.observation
-
-            reward = result.reward or 0.0
-            done = result.done
-            error = None
-
-            rewards.append(reward)
-            steps_taken = step
-
-            # Log step with problem info
-            action_str = f"answer='{answer}' | correct={obs.is_correct} | difficulty={obs.difficulty}"
-            log_step(step=step, action=action_str, reward=reward, done=done, error=error)
-
-            if done:
-                break
-
-        # Calculate normalized score
-        score = sum(rewards) / MAX_TOTAL_REWARD if MAX_TOTAL_REWARD > 0 else 0.0
-        score = min(max(score, 0.0), 1.0)  # clamp to [0, 1]
-        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())