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from __future__ import annotations
import logging
import threading
import time
import uuid
from collections.abc import Callable
from typing import Optional
import numpy as np
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel, ConfigDict, Field
from physix.models import (
DEFAULT_MAX_TURNS,
PhysiXAction,
PhysiXObservation,
)
from physix.server.environment import PhysiXEnvironment
from physix.systems import list_supported_systems, list_systems
from physix.systems.base import PhysicalSystem, TrajectoryData
from physix.training.prompt import build_prompt, parse_completion
from physix.verifier.parser import parse_equation
from physix.verifier.simulator import simulate_hypothesis
_log = logging.getLogger(__name__)
class InteractiveResetRequest(BaseModel):
model_config = ConfigDict(extra="forbid")
system_id: Optional[str] = Field(
default=None,
description="Force a specific system. None = sample at random.",
)
seed: Optional[int] = None
max_turns: int = Field(default=DEFAULT_MAX_TURNS, ge=1, le=32)
class SystemDescriptor(BaseModel):
model_config = ConfigDict(frozen=True)
system_id: str
state_variables: tuple[str, ...]
class InteractiveStartResponse(BaseModel):
session_id: str
observation: PhysiXObservation
system: SystemDescriptor
max_turns: int
class LlmStepRequest(BaseModel):
"""Server-side LLM call. Browser names a model tag; server hits Ollama."""
model_config = ConfigDict(extra="forbid")
model: str = "qwen2.5:1.5b-instruct"
temperature: float = Field(default=0.4, ge=0.0, le=2.0)
max_tokens: int = Field(default=2048, ge=64, le=8192)
host: Optional[str] = None
class LlmModelInfo(BaseModel):
"""A single locally-pulled Ollama model tag."""
model_config = ConfigDict(frozen=True)
name: str
size_bytes: Optional[int] = None
parameter_size: Optional[str] = None
family: Optional[str] = None
class LlmModelsResponse(BaseModel):
models: list[LlmModelInfo] = Field(default_factory=list)
error: Optional[str] = None
class LlmStepResponse(BaseModel):
observation: PhysiXObservation
predicted_trajectory: list[dict[str, float]] = Field(default_factory=list)
action: PhysiXAction
raw_completion: str
latency_s: float
model: str
class SessionSummary(BaseModel):
session_id: str
system_id: str
turn: int
max_turns: int
converged: bool
done: bool
class _Session:
__slots__ = ("env", "system_id", "max_turns", "lock")
def __init__(self, env: PhysiXEnvironment, system_id: str, max_turns: int) -> None:
self.env = env
self.system_id = system_id
self.max_turns = max_turns
self.lock = threading.Lock()
class InteractiveSessionStore:
"""Threadsafe in-memory session map."""
def __init__(self) -> None:
self._sessions: dict[str, _Session] = {}
self._lock = threading.Lock()
def create(
self,
*,
system_id: Optional[str],
seed: Optional[int],
max_turns: int,
) -> tuple[str, _Session, PhysiXObservation]:
env = PhysiXEnvironment(seed=seed, max_turns=max_turns)
observation = env.reset(seed=seed, system_id=system_id)
session = _Session(env=env, system_id=env.state.system_id, max_turns=max_turns)
session_id = uuid.uuid4().hex
with self._lock:
self._sessions[session_id] = session
return session_id, session, observation
def get(self, session_id: str) -> _Session:
with self._lock:
session = self._sessions.get(session_id)
if session is None:
raise HTTPException(status_code=404, detail="Unknown session_id.")
return session
def delete(self, session_id: str) -> None:
with self._lock:
self._sessions.pop(session_id, None)
def __len__(self) -> int:
with self._lock:
return len(self._sessions)
LlmPolicy = Callable[[list[dict[str, str]]], str]
LlmPolicyFactory = Callable[[LlmStepRequest], LlmPolicy]
LlmModelsLister = Callable[[], LlmModelsResponse]
def default_ollama_models_lister() -> LlmModelsResponse:
try:
import ollama # type: ignore[import-not-found]
except ImportError:
return LlmModelsResponse(
models=[],
error=(
"The 'ollama' Python package is not installed on the server. "
"Install with: pip install -e '.[demo]'"
),
)
try:
response = ollama.Client().list()
except Exception as exc: # noqa: BLE001 — surfaced in the response body
return LlmModelsResponse(
models=[],
error=(
f"Could not reach the local Ollama daemon ({exc}). "
"Is 'ollama serve' running?"
),
)
raw_models = getattr(response, "models", None)
if raw_models is None and isinstance(response, dict):
raw_models = response.get("models", [])
raw_models = raw_models or []
out: list[LlmModelInfo] = []
for entry in raw_models:
name = _model_attr(entry, "model") or _model_attr(entry, "name")
if not isinstance(name, str) or not name:
continue
details = _model_attr(entry, "details")
out.append(
LlmModelInfo(
name=name,
size_bytes=_coerce_int(_model_attr(entry, "size")),
parameter_size=_model_attr(details, "parameter_size"),
family=_model_attr(details, "family"),
)
)
out.sort(key=lambda m: m.name)
return LlmModelsResponse(models=out)
def _model_attr(obj: object, key: str) -> object:
if obj is None:
return None
if isinstance(obj, dict):
return obj.get(key)
return getattr(obj, key, None)
def _coerce_int(value: object) -> Optional[int]:
if value is None:
return None
try:
return int(value)
except (TypeError, ValueError):
return None
def default_ollama_policy_factory(request: LlmStepRequest) -> LlmPolicy:
try:
import ollama # type: ignore[import-not-found]
except ImportError as exc: # pragma: no cover
raise HTTPException(
status_code=503,
detail=(
"The 'ollama' Python package is not installed on the server. "
"Install with: pip install -e '.[demo]'"
),
) from exc
client = ollama.Client(host=request.host) if request.host else ollama.Client()
def _policy(prompt: list[dict[str, str]]) -> str:
try:
response = client.chat(
model=request.model,
messages=prompt,
format="json",
options={
"temperature": request.temperature,
"num_predict": request.max_tokens,
},
)
except Exception as exc: # noqa: BLE001 — surfaced as 502
raise HTTPException(
status_code=502,
detail=(
f"Ollama call failed for model {request.model!r}: {exc}. "
"Is 'ollama serve' running and the model pulled "
f"('ollama pull {request.model}')?"
),
) from exc
return str(response["message"]["content"])
return _policy
def build_interactive_router(
store: Optional[InteractiveSessionStore] = None,
*,
policy_factory: LlmPolicyFactory = default_ollama_policy_factory,
models_lister: LlmModelsLister = default_ollama_models_lister,
) -> APIRouter:
sessions = store if store is not None else InteractiveSessionStore()
router = APIRouter(prefix="/interactive", tags=["Interactive"])
@router.get("/models", response_model=LlmModelsResponse)
def list_local_models() -> LlmModelsResponse:
return models_lister()
@router.get("/systems", response_model=list[SystemDescriptor])
def list_public_systems() -> list[SystemDescriptor]:
from physix.systems import get_system
out: list[SystemDescriptor] = []
for system_id in list_supported_systems():
system = get_system(system_id)
out.append(
SystemDescriptor(
system_id=system.system_id,
state_variables=system.state_variables,
)
)
return out
@router.post("/sessions", response_model=InteractiveStartResponse)
def start_session(payload: InteractiveResetRequest) -> InteractiveStartResponse:
from physix.systems import get_system
if payload.system_id is not None and payload.system_id not in list_systems():
raise HTTPException(
status_code=400, detail=f"Unknown system_id {payload.system_id!r}."
)
chosen_system_id = payload.system_id
if chosen_system_id is None:
demo_ids = list_supported_systems()
if demo_ids:
rng = (
np.random.default_rng(payload.seed)
if payload.seed is not None
else np.random.default_rng()
)
chosen_system_id = str(rng.choice(demo_ids))
session_id, session, observation = sessions.create(
system_id=chosen_system_id,
seed=payload.seed,
max_turns=payload.max_turns,
)
system = get_system(session.system_id)
return InteractiveStartResponse(
session_id=session_id,
observation=observation,
system=SystemDescriptor(
system_id=system.system_id,
state_variables=system.state_variables,
),
max_turns=session.max_turns,
)
@router.post(
"/sessions/{session_id}/llm-step", response_model=LlmStepResponse
)
def llm_step_session(
session_id: str, payload: LlmStepRequest
) -> LlmStepResponse:
session = sessions.get(session_id)
with session.lock:
_ensure_budget(session)
current_obs = session.env.current_observation()
if current_obs is None:
raise HTTPException(
status_code=500, detail="Session has no current observation."
)
policy = policy_factory(payload)
t0 = time.perf_counter()
raw_completion = policy(build_prompt(current_obs))
latency_s = time.perf_counter() - t0
action = parse_completion(raw_completion)
observation = session.env.step(action)
predicted = _safe_predict(session.env, action)
return LlmStepResponse(
observation=observation,
predicted_trajectory=predicted,
action=action,
raw_completion=raw_completion,
latency_s=latency_s,
model=payload.model,
)
@router.delete("/sessions/{session_id}", status_code=204)
def end_session(session_id: str) -> None:
sessions.delete(session_id)
@router.get("/sessions/{session_id}", response_model=SessionSummary)
def get_session(session_id: str) -> SessionSummary:
session = sessions.get(session_id)
return SessionSummary(
session_id=session_id,
system_id=session.system_id,
turn=session.env.state.step_count,
max_turns=session.max_turns,
converged=session.env.state.converged,
done=(
session.env.state.converged
or session.env.state.step_count >= session.max_turns
),
)
return router
def _ensure_budget(session: _Session) -> None:
if session.env.state.step_count >= session.max_turns:
raise HTTPException(
status_code=409,
detail="Episode budget already exhausted; start a new session.",
)
def _safe_predict(
env: PhysiXEnvironment, action: PhysiXAction
) -> list[dict[str, float]]:
"""Forward-simulate the user's hypothesis for the UI overlay.
Returns ``[]`` on parse / simulation failure — the env's reward is
authoritative; this is best-effort visualisation only.
"""
system: Optional[PhysicalSystem] = env.current_system
trajectory: Optional[TrajectoryData] = env.current_trajectory
if system is None or trajectory is None:
return []
parameter_names = frozenset(action.params or {}) | frozenset(system.parameters)
try:
parsed = parse_equation(
action.equation,
state_variables=system.state_variables,
parameter_names=parameter_names,
)
except Exception as exc: # noqa: BLE001
_log.debug("predict parse failed: %s", exc)
return []
merged = {**system.parameters, **(action.params or {})}
try:
predicted = simulate_hypothesis(
parsed,
state_variables=system.state_variables,
parameters=merged,
initial_conditions=trajectory.initial_conditions,
timestamps=trajectory.timestamps,
)
except Exception as exc: # noqa: BLE001
_log.debug("predict simulate failed: %s", exc)
return []
samples: list[dict[str, float]] = []
for i, t in enumerate(trajectory.timestamps):
sample: dict[str, float] = {"t": round(float(t), 5)}
for var in system.state_variables:
value = predicted[var][i]
if not np.isfinite(value):
return []
sample[var] = round(float(value), 5)
samples.append(sample)
return samples
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