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inference.py

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+import os
+import asyncio
+from typing import List
+from openai import OpenAI
+from sql_env_environment import SqlEnvironment
+from models import SqlAction
+
+API_BASE_URL = os.getenv("API_BASE_URL", "https://openrouter.ai/api/v1")
+MODEL_NAME   = os.getenv("MODEL_NAME", "meta-llama/llama-3.1-8b-instruct:free")
+HF_TOKEN     = os.getenv("HF_TOKEN")
+MAX_STEPS    = 10
+SUCCESS_SCORE_THRESHOLD = 0.6
+
+if not HF_TOKEN:
+    raise ValueError("HF_TOKEN is required")
+
+def log_start(model):
+    print(f"[START] env=sql_env model={model}", flush=True)
+
+def log_step(step, action, reward, done, error):
+    err = error if error else "null"
+    print(f"[STEP] step={step} action={repr(action)} reward={reward:.2f} done={str(done).lower()} error={err}", flush=True)
+
+def log_end(success, steps, score, rewards):
+    r_str = ",".join(f"{r:.2f}" for r in rewards)
+    print(f"[END] success={str(success).lower()} steps={steps} score={score:.2f} rewards={r_str}", flush=True)
+
+def get_model_query(client, last_output: str, history: List[str]) -> SqlAction:
+    history_str = "\n".join(history[-4:]) if history else "None"
+    prompt = f"""You are an expert SQL developer working with a SQLite database.
+
+The database has these tables:
+- employees (id, name, department, salary)
+- departments (id, name, budget)
+
+LAST OUTPUT:
+{last_output}
+
+RECENT HISTORY:
+{history_str}
+
+Instructions:
+- Write a single valid SQLite SQL query
+- Reply with ONLY one line:
+QUERY: <your sql here>
+"""
+    try:
+        completion = client.chat.completions.create(
+            model=MODEL_NAME,
+            messages=[{"role": "user", "content": prompt}],
+            max_tokens=200
+        )
+        text = completion.choices[0].message.content.strip()
+        lines = {l.split(":")[0].strip(): ":".join(l.split(":")[1:]).strip()
+                 for l in text.splitlines() if ":" in l}
+        query = lines.get("QUERY", "SELECT * FROM employees")
+        return SqlAction(query=query)
+    except Exception as e:
+        print(f"[DEBUG] LLM error: {e}", flush=True)
+        return SqlAction(query="SELECT * FROM employees")
+
+async def main():
+    client = OpenAI(base_url=API_BASE_URL, api_key=HF_TOKEN)
+    env = SqlEnvironment()
+
+    rewards: List[float] = []
+    history: List[str] = []
+    steps_taken = 0
+    score = 0.0
+    success = False
+
+    log_start(model=MODEL_NAME)
+
+    try:
+        obs = env.reset()
+        last_output = obs.echoed_message
+
+        for step in range(1, MAX_STEPS + 1):
+            action = get_model_query(client, last_output, history)
+            obs = env.step(action)
+
+            metadata = obs.metadata or {}
+            reward = metadata.get("reward", 0.0)
+            error = metadata.get("error", None)
+            done = False  # extend with your own done condition
+
+            rewards.append(reward)
+            steps_taken = step
+            last_output = obs.echoed_message
+            history.append(f"Step {step}: {action.query[:80]} → reward {reward:.2f}")
+
+            log_step(step=step, action=action.query[:100],
+                     reward=reward, done=done, error=error)
+
+            if done:
+                break
+
+        score = sum(rewards) / max(len(rewards), 1)
+        score = min(max(score, 0.0), 1.0)
+        success = score >= SUCCESS_SCORE_THRESHOLD
+
+    finally:
+        log_end(success=success, steps=steps_taken, score=score, rewards=rewards)
+
+if __name__ == "__main__":
+    asyncio.run(main())