Uploaded inference.py

inference.py

@@ -0,0 +1,245 @@
+"""
+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
+LOCAL_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") 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(LOCAL_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())