updated according to requirements

inference.py

@@ -1,321 +1,175 @@
-# import os
-# import json
-# import urllib.request
-# import urllib.error
-# from typing import List, Optional
-
-# try:
-#     from dotenv import load_dotenv
-#     load_dotenv()
-# except ImportError:
-#     pass
-
-# # /// script
-# # requires-python = ">=3.11"
-# # dependencies = [
-# #     "openai",
-# # ]
-# # ///
-
-# from openai import OpenAI
-
-# def post_json(url: str, payload: dict) -> dict:
-#     data = json.dumps(payload).encode("utf-8")
-#     req = urllib.request.Request(url, data=data, headers={"Content-Type": "application/json"})
-#     try:
-#         with urllib.request.urlopen(req) as res:
-#             return json.loads(res.read().decode("utf-8"))
-#     except urllib.error.HTTPError as e:
-#         raise Exception(f"HTTP Error {e.code}: {e.read().decode('utf-8')}")
-
-# # ── Environment variables ────────────────────────────────────────────────────
-# # API_BASE_URL = os.getenv("API_BASE_URL", "https://router.huggingface.co/v1")
-# # HF_TOKEN = os.getenv("HF_TOKEN")
-
-# # API_KEY = HF_TOKEN or os.getenv("API_KEY")
-# # if not API_KEY:
-# #     raise ValueError("API_KEY environment variable is required")
-
-# API_BASE_URL = os.environ.get("API_BASE_URL")
-# API_KEY = os.environ.get("API_KEY")
-# MODEL_NAME = os.environ.get("MODEL_NAME")
-# ENV_BASE_URL = os.environ.get("ENV_BASE_URL", "http://localhost:7860")
-
-# if not API_BASE_URL:
-#     raise ValueError("API_BASE_URL must be set")
-
-# if not API_KEY:
-#     raise ValueError("API_KEY must be set")
-
-# MODEL_NAME = os.getenv("MODEL_NAME", "Qwen/Qwen2.5-72B-Instruct")
-# ENV_BASE_URL = os.getenv("ENV_BASE_URL", "http://localhost:7860")
-
-# TASK_NAME = "schedule-optimization"
-# BENCHMARK = "cognitive-load-manager"
-# SUCCESS_SCORE_THRESHOLD = 0.5
-# MAX_STEPS = 50
-
-# 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 main():
-#     # Always initialise the OpenAI client using the proxy URL and API key.
-#     # The hackathon validator requires ALL LLM calls to go through API_BASE_URL
-#     # with the provided API_KEY — never bypass this with hardcoded credentials.
-#     client = OpenAI(base_url=API_BASE_URL, api_key=API_KEY)
-
-#     task_id = os.getenv("CLM_LEVEL", "hard")
-
-#     log_start(task=TASK_NAME, env=BENCHMARK, model=MODEL_NAME)
-
-#     # 1. Reset Environment
-#     try:
-#         data = post_json(f"{ENV_BASE_URL}/reset", {"task_id": task_id})
-#     except Exception as e:
-#         log_step(step=0, action="reset", reward=0.0, done=True, error=str(e)[:50])
-#         log_end(success=False, steps=0, score=0.0, rewards=[])
-#         return
-
-#     session_id = data["session_id"]
-#     observation = data["observation"]
-
-#     done = False
-#     step = 0
-#     rewards = []
-#     history = []
-#     info = {}
-
-#     while not done and step < MAX_STEPS:
-#         step += 1
-
-#         # 2. Get next action from LLM via the hackathon proxy
-#         history_str = "\n".join(history[-5:]) if history else "No previous actions."
-#         system_prompt = """
-# You are an AI task scheduler managing cognitive load.
-# CRITICAL RULES:
-# 1. If "fatigue_level" is "high" or "medium", output {"type": "break"}. Do NOT work until fatigue is "low".
-# 2. If "stress_warning" is true, {"type": "break"} reduces stress safely.
-# 3. Find tasks where "progress" < 1.0. Output {"type": "work", "task_id": "<id>"}. Do NOT work on 1.0 tasks.
-# 4. Respond ONLY with raw JSON format. No markdown blocks.
-# Valid actions: {"type": "work", "task_id": "id"}, {"type": "break"}, {"type": "delay"}, {"type": "switch", "task_id": "id"}
-# """
-#         user_prompt = f"""
-# Previous 5 Steps History:
-# {history_str}
-
-# Current Observation:
-# {json.dumps(observation, indent=2)}
-
-# What is your next action JSON?
-# """
-#         action = None
-#         error_msg = None
-
-#         try:
-#             completion = client.chat.completions.create(
-#                 model=MODEL_NAME,
-#                 messages=[
-#                     {"role": "system", "content": system_prompt.strip()},
-#                     {"role": "user", "content": user_prompt.strip()}
-#                 ],
-#                 temperature=0.1,
-#                 max_tokens=150
-#             )
-#             action_text = (completion.choices[0].message.content or "").strip()
-
-#             # Strip accidental markdown code fences
-#             if action_text.startswith("```json"):
-#                 action_text = action_text[7:]
-#             if action_text.startswith("```"):
-#                 action_text = action_text[3:]
-#             if action_text.endswith("```"):
-#                 action_text = action_text[:-3]
-
-#             start_idx = action_text.find("{")
-#             end_idx = action_text.rfind("}")
-#             if start_idx != -1 and end_idx != -1:
-#                 action = json.loads(action_text[start_idx:end_idx + 1])
-#         except Exception as e:
-#             error_msg = str(e)[:50]
-
-#         # Fallback heuristic only if LLM call failed / returned unparseable output
-#         if not action:
-#             tasks = observation.get("tasks", [])
-#             incomp = [t for t in tasks if t.get("progress", 0.0) < 1.0]
-#             if observation.get("visible_state", {}).get("fatigue_level") in ("high", "medium"):
-#                 action = {"type": "break"}
-#             elif incomp:
-#                 action = {"type": "work", "task_id": incomp[0]["id"]}
-#             else:
-#                 action = {"type": "delay"}
-
-#         action_str = json.dumps(action).replace(" ", "")
-
-#         # 3. Step the environment
-#         try:
-#             step_data = post_json(f"{ENV_BASE_URL}/step", {
-#                 "session_id": session_id,
-#                 "action": action
-#             })
-#             observation = step_data["observation"]
-#             reward = step_data.get("reward", 0.0)
-#             done = step_data.get("done", False)
-#             info = step_data.get("info", {})
-#         except Exception as e:
-#             reward = 0.0
-#             done = True
-#             error_msg = error_msg or str(e)[:50]
-
-#         rewards.append(reward)
-#         history.append(f"Step {step} Action: {action_str} -> Reward: {reward}")
-#         log_step(step=step, action=action_str, reward=reward, done=done, error=error_msg)
-
-#     score = info.get("final_score", 0.0)
-#     success = score >= SUCCESS_SCORE_THRESHOLD
-#     log_end(success=success, steps=step, score=score, rewards=rewards)
-
-# if __name__ == "__main__":
-#     main()
-
-
-
 import os
 import json
 import urllib.request
 import urllib.error
 from typing import List, Optional
 
-from openai import OpenAI
+try:
+    from dotenv import load_dotenv
+    load_dotenv()
+except ImportError:
+    pass
+
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#     "openai",
+# ]
+# ///
 
+from openai import OpenAI
 
-# ── HTTP Helper ──────────────────────────────────────────────────────────────
 def post_json(url: str, payload: dict) -> dict:
     data = json.dumps(payload).encode("utf-8")
     req = urllib.request.Request(url, data=data, headers={"Content-Type": "application/json"})
-    with urllib.request.urlopen(req) as res:
-        return json.loads(res.read().decode("utf-8"))
+    try:
+        with urllib.request.urlopen(req) as res:
+            return json.loads(res.read().decode("utf-8"))
+    except urllib.error.HTTPError as e:
+        raise Exception(f"HTTP Error {e.code}: {e.read().decode('utf-8')}")
+
+# ── Environment variables ────────────────────────────────────────────────────
+# API_BASE_URL = os.getenv("API_BASE_URL", "https://router.huggingface.co/v1")
+# HF_TOKEN = os.getenv("HF_TOKEN")
+
+# API_KEY = HF_TOKEN or os.getenv("API_KEY")
+# if not API_KEY:
+#     raise ValueError("API_KEY environment variable is required")
 
 
-# ── STRICT ENV (NO FALLBACKS) ────────────────────────────────────────────────
 API_BASE_URL = os.environ.get("API_BASE_URL")
 API_KEY = os.environ.get("API_KEY")
 MODEL_NAME = os.environ.get("MODEL_NAME")
+ENV_BASE_URL = os.environ.get("ENV_BASE_URL", "http://localhost:7860")
 
 if not API_BASE_URL:
     raise ValueError("API_BASE_URL must be set")
+
 if not API_KEY:
     raise ValueError("API_KEY must be set")
-if not MODEL_NAME:
-    raise ValueError("MODEL_NAME must be set")
-
-ENV_BASE_URL = os.environ.get("ENV_BASE_URL", "http://localhost:7860")
 
+MODEL_NAME = os.getenv("MODEL_NAME", "Qwen/Qwen2.5-72B-Instruct")
+ENV_BASE_URL = os.getenv("ENV_BASE_URL", "http://localhost:7860")
 
-# ── CONFIG ───────────────────────────────────────────────────────────────────
 TASK_NAME = "schedule-optimization"
 BENCHMARK = "cognitive-load-manager"
 SUCCESS_SCORE_THRESHOLD = 0.5
 MAX_STEPS = 50
 
-
-# ── LOGGING ──────────────────────────────────────────────────────────────────
-def log_start(task: str, env: str, model: str):
+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]):
+def log_step(step: int, action: str, reward: float, done: bool, error: Optional[str]) -> None:
     error_val = error if error else "null"
-    print(f"[STEP] step={step} action={action} reward={reward:.2f} done={str(done).lower()} error={error_val}", flush=True)
-
+    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]):
+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)
 
-
-# ── MAIN ─────────────────────────────────────────────────────────────────────
 def main():
+    # Always initialise the OpenAI client using the proxy URL and API key.
+    # The hackathon validator requires ALL LLM calls to go through API_BASE_URL
+    # with the provided API_KEY — never bypass this with hardcoded credentials.
     client = OpenAI(base_url=API_BASE_URL, api_key=API_KEY)
 
-    log_start(TASK_NAME, BENCHMARK, MODEL_NAME)
+    task_id = os.getenv("CLM_LEVEL", "hard")
+
+    log_start(task=TASK_NAME, env=BENCHMARK, model=MODEL_NAME)
 
-    # RESET
+    # 1. Reset Environment
     try:
-        data = post_json(f"{ENV_BASE_URL}/reset", {"task_id": "hard"})
+        data = post_json(f"{ENV_BASE_URL}/reset", {"task_id": task_id})
     except Exception as e:
-        log_step(0, "reset", 0.0, True, str(e)[:50])
-        log_end(False, 0, 0.0, [])
+        log_step(step=0, action="reset", reward=0.0, done=True, error=str(e)[:50])
+        log_end(success=False, steps=0, score=0.0, rewards=[])
         return
 
     session_id = data["session_id"]
     observation = data["observation"]
 
-    rewards = []
     done = False
     step = 0
+    rewards = []
+    history = []
     info = {}
 
     while not done and step < MAX_STEPS:
         step += 1
 
+        # 2. Get next action from LLM via the hackathon proxy
+        history_str = "\n".join(history[-5:]) if history else "No previous actions."
+        system_prompt = """
+You are an AI task scheduler managing cognitive load.
+CRITICAL RULES:
+1. If "fatigue_level" is "high" or "medium", output {"type": "break"}. Do NOT work until fatigue is "low".
+2. If "stress_warning" is true, {"type": "break"} reduces stress safely.
+3. Find tasks where "progress" < 1.0. Output {"type": "work", "task_id": "<id>"}. Do NOT work on 1.0 tasks.
+4. Respond ONLY with raw JSON format. No markdown blocks.
+Valid actions: {"type": "work", "task_id": "id"}, {"type": "break"}, {"type": "delay"}, {"type": "switch", "task_id": "id"}
+"""
+        user_prompt = f"""
+Previous 5 Steps History:
+{history_str}
+
+Current Observation:
+{json.dumps(observation, indent=2)}
+
+What is your next action JSON?
+"""
         action = None
         error_msg = None
 
-        # 🔥 FORCE LLM CALL (NO SKIP)
         try:
-            response = client.responses.create(
+            completion = client.chat.completions.create(
                 model=MODEL_NAME,
-                input=f"Return ONLY JSON action for this observation:\n{json.dumps(observation)}",
-                max_output_tokens=100,
-                temperature=0.1
+                messages=[
+                    {"role": "system", "content": system_prompt.strip()},
+                    {"role": "user", "content": user_prompt.strip()}
+                ],
+                temperature=0.1,
+                max_tokens=150
             )
-
-            # Extract text safely
-            text = ""
-            if response.output:
-                for item in response.output:
-                    for part in item.content:
-                        if hasattr(part, "text"):
-                            text += part.text
-
-            text = text.strip()
-
-            start = text.find("{")
-            end = text.rfind("}")
-            if start != -1 and end != -1:
-                action = json.loads(text[start:end+1])
-
+            action_text = (completion.choices[0].message.content or "").strip()
+
+            # Strip accidental markdown code fences
+            if action_text.startswith("```json"):
+                action_text = action_text[7:]
+            if action_text.startswith("```"):
+                action_text = action_text[3:]
+            if action_text.endswith("```"):
+                action_text = action_text[:-3]
+
+            start_idx = action_text.find("{")
+            end_idx = action_text.rfind("}")
+            if start_idx != -1 and end_idx != -1:
+                action = json.loads(action_text[start_idx:end_idx + 1])
         except Exception as e:
             error_msg = str(e)[:50]
 
-        # fallback AFTER LLM attempt
+        # Fallback heuristic only if LLM call failed / returned unparseable output
         if not action:
             tasks = observation.get("tasks", [])
-            if tasks:
-                action = {"type": "work", "task_id": tasks[0]["id"]}
-            else:
+            incomp = [t for t in tasks if t.get("progress", 0.0) < 1.0]
+            if observation.get("visible_state", {}).get("fatigue_level") in ("high", "medium"):
                 action = {"type": "break"}
+            elif incomp:
+                action = {"type": "work", "task_id": incomp[0]["id"]}
+            else:
+                action = {"type": "delay"}
 
         action_str = json.dumps(action).replace(" ", "")
 
-        # STEP ENV
+        # 3. Step the environment
         try:
-            step_data = post_json(
-                f"{ENV_BASE_URL}/step",
-                {"session_id": session_id, "action": action}
-            )
+            step_data = post_json(f"{ENV_BASE_URL}/step", {
+                "session_id": session_id,
+                "action": action
+            })
             observation = step_data["observation"]
             reward = step_data.get("reward", 0.0)
             done = step_data.get("done", False)
@@ -326,14 +180,12 @@ def main():
             error_msg = error_msg or str(e)[:50]
 
         rewards.append(reward)
-
-        log_step(step, action_str, reward, done, error_msg)
+        history.append(f"Step {step} Action: {action_str} -> Reward: {reward}")
+        log_step(step=step, action=action_str, reward=reward, done=done, error=error_msg)
 
     score = info.get("final_score", 0.0)
     success = score >= SUCCESS_SCORE_THRESHOLD
-
-    log_end(success, step, score, rewards)
-
+    log_end(success=success, steps=step, score=score, rewards=rewards)
 
 if __name__ == "__main__":
     main()

requirements.txt

@@ -1,327 +1,327 @@
-absl-py==2.2.0
-accelerate==1.6.0
-acres==0.5.0
-aiofiles==24.1.0
-aiohttp==3.9.5
-aioresponses==0.7.6
-aiosignal==1.3.1
-altair==5.5.0
-annotated-types==0.7.0
-anyio==4.9.0
-argon2-cffi==25.1.0
-argon2-cffi-bindings==25.1.0
-arrow==1.4.0
-asgiref==3.7.2
-asttokens==3.0.1
-astunparse==1.6.3
-async-lru==2.2.0
-attrs==25.3.0
-av==14.2.0
-babel==2.18.0
-beautifulsoup4==4.13.3
-bidict==0.23.1
-bleach==6.3.0
-blinker==1.9.0
-branca==0.8.2
-cachetools==5.5.2
-certifi==2025.4.26
-cffi==1.17.1
-charset-normalizer==3.4.1
-ci-info==0.3.0
-click==8.1.8
-cloudpickle==3.1.2
-colorama==0.4.6
-coloredlogs==15.0.1
-comm==0.2.3
-comtypes==1.3.1
-configobj==5.0.9
-configparser==7.2.0
-contourpy==1.3.1
-cryptography==44.0.3
-ctranslate2==4.5.0
-cycler==0.12.1
-debugpy==1.8.20
-decorator==5.2.1
-deep-translator==1.11.4
-deepface==0.0.93
-defusedxml==0.7.1
-distlib==0.3.8
-distro==1.9.0
-Django==4.1
-django-background-tasks==1.2.5
-django-compat==1.0.15
-djangorestframework==3.13.1
-dnspython==2.7.0
-docx==0.2.4
-ecdsa==0.19.1
-et-xmlfile==1.1.0
-etelemetry==0.3.1
-executing==2.2.1
-faiss-cpu==1.11.0
-fastapi==0.115.12
-faster-whisper==1.1.1
-fastjsonschema==2.21.2
-ffmpeg-python==0.2.0
-filelock==3.18.0
-fire==0.7.0
-fitz==0.0.1.dev2
-Flask==3.1.0
-flask-cors==5.0.1
-Flask-Login==0.6.3
-Flask-SocketIO==5.5.1
-Flask-SQLAlchemy==3.1.1
-flatbuffers==25.2.10
-folium==0.20.0
-fonttools==4.56.0
-fpdf==1.7.2
-fqdn==1.5.1
-frozenlist==1.4.1
-fsspec==2025.5.1
-future==1.0.0
-fuzzywuzzy==0.18.0
-gast==0.6.0
-gdown==5.2.0
-geographiclib==2.1
-geopy==2.4.1
-git-filter-repo==2.45.0
-gitdb==4.0.12
-GitPython==3.1.44
-google-ai-generativelanguage==0.6.15
-google-api-core==2.24.2
-google-api-python-client==2.166.0
-google-auth==2.38.0
-google-auth-httplib2==0.2.0
-google-generativeai==0.8.5
-google-pasta==0.2.0
-googleapis-common-protos==1.69.2
-greenlet==3.2.0
-grpcio==1.71.0
-grpcio-status==1.71.0
-gTTS==2.5.4
-gunicorn==23.0.0
-h11==0.14.0
-h5py==3.13.0
-httpcore==1.0.7
-httplib2==0.22.0
-httpretty==1.1.4
-httpx==0.28.1
-huggingface-hub==0.33.2
-humanfriendly==10.0
-idna==3.10
-imageio==2.37.0
-imageio-ffmpeg==0.6.0
-intel-openmp==2021.4.0
-ipykernel==7.2.0
-ipython==9.10.0
-ipython_pygments_lexers==1.1.1
-ipywidgets==8.1.8
-isoduration==20.11.0
-itsdangerous==2.2.0
-jax==0.5.3
-jaxlib==0.5.3
-jedi==0.19.2
-Jinja2==3.1.6
-jiter==0.9.0
-joblib==1.4.2
-json5==0.13.0
-jsonpointer==3.0.0
-jsonschema==4.23.0
-jsonschema-specifications==2024.10.1
-jupyter==1.1.1
-jupyter-console==6.6.3
-jupyter-events==0.12.0
-jupyter-lsp==2.3.0
-jupyter_client==8.8.0
-jupyter_core==5.9.1
-jupyter_server==2.17.0
-jupyter_server_terminals==0.5.4
-jupyterlab==4.5.5
-jupyterlab_pygments==0.3.0
-jupyterlab_server==2.28.0
-jupyterlab_widgets==3.0.16
-keras==3.10.0
-kiwisolver==1.4.8
-langdetect==1.0.9
-libclang==18.1.1
-llamaapi==0.1.36
-llvmlite==0.44.0
-looseversion==1.3.0
-lxml==5.3.2
-lz4==4.4.3
-Markdown==3.7
-markdown-it-py==3.0.0
-MarkupSafe==3.0.2
-matplotlib==3.10.1
-matplotlib-inline==0.2.1
-mdurl==0.1.2
-mistune==3.2.0
-mkl==2021.4.0
-ml_dtypes==0.5.1
-more-itertools==10.6.0
-moviepy==2.1.2
-mpmath==1.3.0
-mtcnn==1.0.0
-multidict==6.0.5
-namex==0.0.8
-narwhals==1.32.0
-nbclient==0.10.4
-nbconvert==7.17.0
-nbformat==5.10.4
-nest-asyncio==1.6.0
-networkx==3.4.2
-nibabel==5.3.2
-nipype==1.10.0
-notebook==7.5.4
-notebook_shim==0.2.4
-numba==0.61.0
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-xyzservices==2025.11.0
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-youtube-transcript-api==1.0.3
-yt-dlp==2025.6.30
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