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PITCHFIGHT_AI / core /nvidia_client.py

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UI REFINEMENT

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"""Backend-only NVIDIA Nemotron API client.
All calls are backend-only. API key is never passed to the frontend.
Key is read from NVIDIA_API_KEY environment variable only.
"""

from __future__ import annotations

import logging
import os
import time
from typing import Any

from dotenv import load_dotenv
from openai import OpenAI, APIConnectionError, APIStatusError, APITimeoutError

load_dotenv()

logger = logging.getLogger(__name__)

_DEFAULT_BASE_URL = "https://integrate.api.nvidia.com/v1"
_DEFAULT_MODEL = "nvidia/nemotron-3-nano-omni-30b-a3b-reasoning"


class OmniAudioError(RuntimeError):
    """Structured Omni audio failure — metadata safe for API responses."""

    def __init__(self, message: str, **meta: Any) -> None:
        super().__init__(message)
        self.meta = meta

    def to_error_dict(self) -> dict[str, Any]:
        return {
            "error": "Omni audio call failed",
            "detail": str(self),
            **self.meta,
        }


# Per-mode settings for nemotron-3-nano-omni-30b-a3b-reasoning.
#
# enable_thinking: True  → reasoning model uses internal chain-of-thought
#                  False → thinking disabled; output is direct (faster, cheaper)
# reasoning_budget: token budget for internal reasoning (clamped to max_tokens)
#
# Modes in main path:
#   opponent                  — live judge questions during battle (thinking on)
#   scorecard_coaching        — coaching JSON only (thinking off — faster, more reliable JSON)
#   scorecard_coaching_repair — JSON repair for coaching output (thinking off)
#   rewrite                   — rewrite utility (thinking on, lighter budget)
#   legacy_full_scorecard     — diagnostic / legacy path only; not main path (thinking off)
_TASK_DEFAULTS: dict[str, dict[str, Any]] = {
    # Thinking modes: reasoning_budget=320 — enough room to think cleanly without
    # spilling monologue into content, while leaving 1180 tokens for real output.
    "opponent": {
        "enable_thinking": True,
        "reasoning_budget": 320,
        "max_tokens": 1500,
        "temperature": 0.65,
        "top_p": 0.95,
    },
    "deal_round": {
        "enable_thinking": True,
        "reasoning_budget": 320,
        "max_tokens": 1500,
        "temperature": 0.65,
        "top_p": 0.95,
    },
    "rewrite": {
        "enable_thinking": True,
        "reasoning_budget": 320,
        "max_tokens": 1200,
        "temperature": 0.45,
        "top_p": 0.95,
    },
    # Scoring modes (thinking off — pure JSON output).
    # max_tokens raised well above realistic output size so late-round battles
    # with long conversation history never truncate mid-JSON.
    "scorecard_scoring": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 2500,
        "temperature": 0.1,
        "top_p": 0.95,
    },
    "scorecard_scoring_repair": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 2000,
        "temperature": 0.0,
        "top_p": 0.95,
    },
    "scorecard_full": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 3500,
        "temperature": 0.1,
        "top_p": 0.95,
    },
    "scorecard_full_repair": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 3000,
        "temperature": 0.0,
        "top_p": 0.95,
    },
    "scorecard_coaching": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 3200,
        "temperature": 0.2,
        "top_p": 0.95,
    },
    "scorecard_coaching_repair": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 2400,
        "temperature": 0.0,
        "top_p": 0.95,
    },
    "legacy_full_scorecard": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 4000,
        "temperature": 0.1,
        "top_p": 0.95,
    },
    "voice_extraction": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 1800,
        "temperature": 0.1,
        "top_p": 0.95,
    },
    "voice_extraction_repair": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 1400,
        "temperature": 0.0,
        "top_p": 0.95,
    },
    "voice_turn": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 800,
        "temperature": 0.0,
        "top_p": 0.95,
    },
    "voice_turn_repair": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 700,
        "temperature": 0.0,
        "top_p": 0.95,
    },
    "retry_comparison": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 1500,
        "temperature": 0.15,
        "top_p": 0.95,
    },
    "retry_comparison_repair": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 1200,
        "temperature": 0.0,
        "top_p": 0.95,
    },
    "deal_verdict": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 1500,
        "temperature": 0.2,
        "top_p": 0.95,
    },
    "deal_verdict_repair": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 1200,
        "temperature": 0.0,
        "top_p": 0.95,
    },
    # Deal phase: semantic dimension scoring (JSON, split call 1 — scores only)
    "deal_scorecard_scoring": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 2500,
        "temperature": 0.1,
        "top_p": 0.95,
    },
    "deal_scorecard_scoring_repair": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 2000,
        "temperature": 0.0,
        "top_p": 0.95,
    },
    # Deal phase: coaching text (JSON, split call 2 — coaching only)
    "deal_scorecard_coaching": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 3000,
        "temperature": 0.2,
        "top_p": 0.95,
    },
    "deal_scorecard_repair": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 2000,
        "temperature": 0.0,
        "top_p": 0.95,
    },
    "structure_pitch": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 1000,
        "temperature": 0.0,
        "top_p": 0.95,
    },
    "structure_pitch_repair": {
        "enable_thinking": False,
        "reasoning_budget": 0,
        "max_tokens": 900,
        "temperature": 0.0,
        "top_p": 0.95,
    },
}

_VALID_AUDIO_FORMATS = frozenset({"webm", "wav", "mp3", "m4a", "ogg"})

_AUDIO_MIME: dict[str, str] = {
    "webm": "audio/webm",
    "wav": "audio/wav",
    "mp3": "audio/mpeg",
    "m4a": "audio/mp4",
    "ogg": "audio/ogg",
}

# Modes where the response must be JSON — apply safe extraction from reasoning_content if needed
_JSON_MODES: frozenset[str] = frozenset({
    "scorecard_scoring",
    "scorecard_scoring_repair",
    "scorecard_full",
    "scorecard_full_repair",
    "scorecard_coaching",
    "scorecard_coaching_repair",
    "legacy_full_scorecard",
    "voice_extraction",
    "voice_extraction_repair",
    "voice_turn",
    "voice_turn_repair",
    "retry_comparison",
    "retry_comparison_repair",
    "deal_verdict",
    "deal_verdict_repair",
    "deal_scorecard_scoring",
    "deal_scorecard_scoring_repair",
    "deal_scorecard_coaching",
    "deal_scorecard_repair",
})


def _extract_json_from_reasoning(reasoning: str) -> str | None:
    """Extract first complete JSON object block from reasoning_content."""
    start = reasoning.find("{")
    end = reasoning.rfind("}")
    if start != -1 and end != -1 and end > start:
        return reasoning[start : end + 1].strip()
    return None


# Phrases that only appear in internal reasoning monologue, never in a real response.
_REASONING_LEAK_SIGNALS = (
    "need to keep under",
    "check constraints",
    "that's one sentence",
    "actually it's",
    "make sure we reference",
    "under 3 sentences",
    "the question itself",
    "so okay.",
    "let me ",
    "i need to ",
    "i should ",
    "i'll ask",
    "i will ask",
    "one question,",
    "plain language.",
    "no advice,",
    "no compliments,",
)


def _strip_reasoning_leak(content: str) -> str:
    """Remove internal monologue that leaked into content for thinking-mode calls.
    When reasoning_budget is insufficient the model continues "thinking" inside
    the content field before reaching the actual response.  This function detects
    that pattern and returns only the final intended output (the last question).
    """
    lower = content.lower()
    if not any(sig in lower for sig in _REASONING_LEAK_SIGNALS):
        return content

    # Reasoning leaked — extract the last real sentence(s).
    # Split on sentence boundaries and walk backwards to find the last
    # question, which is the intended output.
    import re
    sentences = [s.strip() for s in re.split(r"(?<=[.!?])\s+", content) if s.strip()]
    if not sentences:
        return content

    # Find the last sentence ending with "?"
    for i in range(len(sentences) - 1, -1, -1):
        if sentences[i].endswith("?"):
            # Include the sentence before it (context) if it's clean prose
            if i > 0 and not any(sig in sentences[i - 1].lower() for sig in _REASONING_LEAK_SIGNALS):
                return f"{sentences[i - 1]} {sentences[i]}"
            return sentences[i]

    # No question mark found — return the last non-reasoning sentence
    for sent in reversed(sentences):
        if not any(sig in sent.lower() for sig in _REASONING_LEAK_SIGNALS):
            return sent

    # Couldn't isolate anything clean — raise so the caller serves its fallback
    # message instead of showing garbage to the user.
    logger.warning("_strip_reasoning_leak: could not isolate clean output, raising for caller fallback")
    raise RuntimeError("Reasoning leaked into content and could not be cleaned (mode content fully contaminated)")


def _get_config() -> tuple[str, str, str]:
    """Return (api_key, base_url, model). Raises RuntimeError if key is absent."""
    api_key = os.getenv("NVIDIA_API_KEY", "").strip()
    if not api_key:
        raise RuntimeError(
            "NVIDIA_API_KEY is not set. "
            "Add it to your .env file locally or as a HF Space Secret in deployment. "
            "Never hardcode the key."
        )
    base_url = os.getenv("NVIDIA_BASE_URL", _DEFAULT_BASE_URL).strip() or _DEFAULT_BASE_URL
    model = os.getenv("NVIDIA_OMNI_MODEL", _DEFAULT_MODEL).strip() or _DEFAULT_MODEL
    return api_key, base_url, model


def is_configured() -> bool:
    """Return True if NVIDIA_API_KEY is present in the environment."""
    return bool(os.getenv("NVIDIA_API_KEY", "").strip())


def health_check() -> dict[str, Any]:
    """Return configuration status without exposing the API key."""
    configured = is_configured()
    base_url = os.getenv("NVIDIA_BASE_URL", _DEFAULT_BASE_URL)
    model = os.getenv("NVIDIA_OMNI_MODEL", _DEFAULT_MODEL)
    return {
        "provider": "nvidia",
        "configured": configured,
        "base_url": base_url,
        "model": model,
        "api_key_present": configured,
        "message": (
            "NVIDIA client ready" if configured
            else "NVIDIA_API_KEY missing — add to .env or HF Space Secrets"
        ),
    }


def _resolve_mode_params(
    mode: str,
    temperature: float | None = None,
    max_tokens: int | None = None,
) -> tuple[float, int, float, bool, int]:
    """Return (temp, tokens, top_p, enable_thinking, reasoning_budget) for a mode."""
    defaults = _TASK_DEFAULTS.get(mode, _TASK_DEFAULTS["opponent"])
    temp = temperature if temperature is not None else defaults["temperature"]
    tokens = max_tokens if max_tokens is not None else defaults["max_tokens"]
    top_p: float = defaults.get("top_p", 0.95)
    enable_thinking: bool = defaults.get("enable_thinking", True)
    reasoning_budget: int = defaults.get("reasoning_budget", 0)
    if mode in _JSON_MODES:
        enable_thinking = False
        reasoning_budget = 0
    reasoning_budget = min(reasoning_budget, tokens)
    return temp, tokens, top_p, enable_thinking, reasoning_budget


def _complete_chat(
    client: OpenAI,
    model: str,
    messages: list[dict],
    mode: str,
    temperature: float | None = None,
    max_tokens: int | None = None,
) -> str:
    """Shared chat completion with mode-specific token/thinking settings."""
    temp, tokens, top_p, enable_thinking, reasoning_budget = _resolve_mode_params(
        mode, temperature, max_tokens
    )
    completion = client.chat.completions.create(
        model=model,
        messages=messages,  # type: ignore[arg-type]
        temperature=temp,
        max_tokens=tokens,
        top_p=top_p,
        extra_body={
            "chat_template_kwargs": {"enable_thinking": enable_thinking},
            "reasoning_budget": reasoning_budget,
        },
    )
    temp_d, tokens_d, _tp, thinking_d, _rb = _resolve_mode_params(mode, temperature, max_tokens)
    msg = completion.choices[0].message
    content = (msg.content or "").strip()
    reasoning = (getattr(msg, "reasoning_content", None) or "").strip()

    # Diagnostics (no secrets, no full prompt/audio): help spot truncation vs empty content.
    logger.debug(
        "Nemotron mode=%s max_tokens=%s thinking=%s content_len=%d reasoning_present=%s",
        mode, tokens_d, thinking_d, len(content), bool(reasoning),
    )

    if not content:
        if mode in _JSON_MODES and reasoning:
            # JSON mode: try to salvage a JSON block from the reasoning field.
            extracted = _extract_json_from_reasoning(reasoning)
            if extracted:
                logger.info(
                    "Nemotron content empty; extracted JSON block from reasoning_content (mode=%s)",
                    mode,
                )
                content = extracted
            else:
                logger.warning(
                    "Nemotron content empty; no JSON in reasoning_content (mode=%s)",
                    mode,
                )
        else:
            # Non-JSON mode (opponent, deal_round, rewrite, …):
            # reasoning_content is the model's compressed internal thinking —
            # it is NEVER safe to display.  Let it raise so the caller serves
            # its own clean fallback message.
            logger.warning(
                "Nemotron content empty for non-JSON mode=%s; "
                "reasoning_content not usable as output — raising for caller fallback",
                mode,
            )

    if not content:
        raise RuntimeError(
            f"Nemotron returned empty content (mode={mode}). "
            "Caller should serve its fallback."
        )

    # For thinking-mode calls (opponent, deal_round, rewrite) strip any internal
    # monologue that leaked into the content field when reasoning_budget runs short.
    if enable_thinking and mode not in _JSON_MODES:
        content = _strip_reasoning_leak(content)

    return content


def call_omni_audio_json(
    prompt: str,
    audio_base64: str,
    audio_format: str,
    mode: str = "voice_extraction",
    timeout: int = 60,
    source_format: str | None = None,
    decoded_bytes: int | None = None,
) -> str:
    """Call Nemotron Omni with audio + text prompt; return response text (JSON expected).
    Raises:
        ValueError: invalid audio input
        OmniAudioError: API rejected audio or call failed
    """
    if not audio_base64 or not str(audio_base64).strip():
        raise ValueError("audio_base64 is required and must be non-empty")
    fmt = str(audio_format or "").strip().lower().lstrip(".")
    if fmt not in _VALID_AUDIO_FORMATS:
        raise ValueError(
            f"audio_format must be one of: {', '.join(sorted(_VALID_AUDIO_FORMATS))}"
        )

    api_key, base_url, model = _get_config()
    mime = _AUDIO_MIME[fmt]
    audio_url = f"data:{mime};base64,{audio_base64.strip()}"

    logger.info(
        "nvidia_client: omni audio call mode=%s format=%s source_format=%s bytes=%s mime=%s",
        mode,
        fmt,
        source_format or fmt,
        decoded_bytes if decoded_bytes is not None else "unknown",
        mime,
    )

    messages = [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": prompt},
                {"type": "audio_url", "audio_url": {"url": audio_url}},
            ],
        }
    ]

    client = OpenAI(api_key=api_key, base_url=base_url, timeout=timeout)
    try:
        return _complete_chat(client, model, messages, mode)
    except APIStatusError as exc:
        detail = exc.message or str(exc)
        logger.warning(
            "NVIDIA Omni audio status error %s: %s (format=%s bytes=%s)",
            exc.status_code,
            detail,
            fmt,
            decoded_bytes,
        )
        raise OmniAudioError(
            detail,
            audio_format_sent=fmt,
            source_format=source_format or fmt,
            decoded_bytes=decoded_bytes,
            mode=mode,
            suggestion=(
                "Audio was normalized to WAV when possible. "
                "Verify NVIDIA audio payload schema or install ffmpeg for conversion."
            ),
        ) from exc
    except (APITimeoutError, APIConnectionError) as exc:
        logger.warning("NVIDIA Omni audio connection error: %s", type(exc).__name__)
        raise OmniAudioError(
            f"Connection error: {type(exc).__name__}",
            audio_format_sent=fmt,
            source_format=source_format or fmt,
            decoded_bytes=decoded_bytes,
            mode=mode,
            suggestion="Retry the voice request or check NVIDIA API connectivity.",
        ) from exc
    except RuntimeError:
        raise
    except Exception as exc:
        logger.warning("NVIDIA Omni audio unexpected error: %s", type(exc).__name__)
        raise OmniAudioError(
            type(exc).__name__,
            audio_format_sent=fmt,
            source_format=source_format or fmt,
            decoded_bytes=decoded_bytes,
            mode=mode,
            suggestion="Verify NVIDIA audio payload schema or audio conversion.",
        ) from exc


def generate_nemotron_response(
    messages: list[dict[str, str]],
    mode: str = "opponent",
    temperature: float | None = None,
    max_tokens: int | None = None,
    timeout: int = 30,
) -> str:
    """Call NVIDIA Nemotron and return the response text.
    Args:
        messages: OpenAI-format message list [{"role": ..., "content": ...}, ...]
        mode: task type key — "opponent", "scorecard_coaching", "rewrite", etc.
        temperature: overrides mode default if provided
        max_tokens: overrides mode default if provided
        timeout: request timeout in seconds
    Returns:
        Response text string from the model.
    Raises:
        RuntimeError: on missing key or any API failure (clean message, no key leak).
    """
    api_key, base_url, model = _get_config()
    client = OpenAI(api_key=api_key, base_url=base_url, timeout=timeout)

    # One retry on transient server/rate errors only — never retry 4xx (e.g. bad audio 400).
    for attempt in range(2):
        try:
            return _complete_chat(client, model, messages, mode, temperature, max_tokens)

        except APITimeoutError:
            logger.warning("NVIDIA API timed out after %ds (mode=%s)", timeout, mode)
            raise RuntimeError(
                f"NVIDIA Nemotron request timed out after {timeout}s. "
                "Check your connection or increase timeout."
            )
        except APIConnectionError as exc:
            if attempt == 0:
                logger.warning("NVIDIA connection error (mode=%s) — retrying once", mode)
                time.sleep(0.8)
                continue
            logger.warning("NVIDIA API connection error: %s", exc)
            raise RuntimeError(
                "Could not connect to NVIDIA API. "
                "Verify NVIDIA_BASE_URL and your network connection."
            )
        except APIStatusError as exc:
            transient = exc.status_code in (429, 500, 502, 503)
            if transient and attempt == 0:
                logger.warning(
                    "NVIDIA transient HTTP %s (mode=%s) — retrying once", exc.status_code, mode
                )
                time.sleep(0.8)
                continue
            logger.warning(
                "NVIDIA API status error %s (mode=%s): %s", exc.status_code, mode, exc.message
            )
            raise RuntimeError(
                f"NVIDIA API returned HTTP {exc.status_code}. "
                "Check your NVIDIA_API_KEY and model ID."
            )
        except Exception as exc:
            logger.warning("NVIDIA API unexpected error (mode=%s): %s", mode, type(exc).__name__)
            raise RuntimeError(
                f"NVIDIA model call failed ({type(exc).__name__}). See server logs."
            )

    # Unreachable (loop either returns or raises) — defensive.
    raise RuntimeError("NVIDIA model call failed after retry.")

	"""Backend-only NVIDIA Nemotron API client.

	All calls are backend-only. API key is never passed to the frontend.
	Key is read from NVIDIA_API_KEY environment variable only.
	"""

	from __future__ import annotations

	import logging
	import os
	import time
	from typing import Any

	from dotenv import load_dotenv
	from openai import OpenAI, APIConnectionError, APIStatusError, APITimeoutError

	load_dotenv()

	logger = logging.getLogger(__name__)

	_DEFAULT_BASE_URL = "https://integrate.api.nvidia.com/v1"
	_DEFAULT_MODEL = "nvidia/nemotron-3-nano-omni-30b-a3b-reasoning"


	class OmniAudioError(RuntimeError):
	"""Structured Omni audio failure — metadata safe for API responses."""

	def __init__(self, message: str, **meta: Any) -> None:
	super().__init__(message)
	self.meta = meta

	def to_error_dict(self) -> dict[str, Any]:
	return {
	"error": "Omni audio call failed",
	"detail": str(self),
	**self.meta,
	}


	# Per-mode settings for nemotron-3-nano-omni-30b-a3b-reasoning.
	#
	# enable_thinking: True → reasoning model uses internal chain-of-thought
	# False → thinking disabled; output is direct (faster, cheaper)
	# reasoning_budget: token budget for internal reasoning (clamped to max_tokens)
	#
	# Modes in main path:
	# opponent — live judge questions during battle (thinking on)
	# scorecard_coaching — coaching JSON only (thinking off — faster, more reliable JSON)
	# scorecard_coaching_repair — JSON repair for coaching output (thinking off)
	# rewrite — rewrite utility (thinking on, lighter budget)
	# legacy_full_scorecard — diagnostic / legacy path only; not main path (thinking off)
	_TASK_DEFAULTS: dict[str, dict[str, Any]] = {
	# Thinking modes: reasoning_budget=320 — enough room to think cleanly without
	# spilling monologue into content, while leaving 1180 tokens for real output.
	"opponent": {
	"enable_thinking": True,
	"reasoning_budget": 320,
	"max_tokens": 1500,
	"temperature": 0.65,
	"top_p": 0.95,
	},
	"deal_round": {
	"enable_thinking": True,
	"reasoning_budget": 320,
	"max_tokens": 1500,
	"temperature": 0.65,
	"top_p": 0.95,
	},
	"rewrite": {
	"enable_thinking": True,
	"reasoning_budget": 320,
	"max_tokens": 1200,
	"temperature": 0.45,
	"top_p": 0.95,
	},
	# Scoring modes (thinking off — pure JSON output).
	# max_tokens raised well above realistic output size so late-round battles
	# with long conversation history never truncate mid-JSON.
	"scorecard_scoring": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 2500,
	"temperature": 0.1,
	"top_p": 0.95,
	},
	"scorecard_scoring_repair": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 2000,
	"temperature": 0.0,
	"top_p": 0.95,
	},
	"scorecard_full": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 3500,
	"temperature": 0.1,
	"top_p": 0.95,
	},
	"scorecard_full_repair": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 3000,
	"temperature": 0.0,
	"top_p": 0.95,
	},
	"scorecard_coaching": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 3200,
	"temperature": 0.2,
	"top_p": 0.95,
	},
	"scorecard_coaching_repair": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 2400,
	"temperature": 0.0,
	"top_p": 0.95,
	},
	"legacy_full_scorecard": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 4000,
	"temperature": 0.1,
	"top_p": 0.95,
	},
	"voice_extraction": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 1800,
	"temperature": 0.1,
	"top_p": 0.95,
	},
	"voice_extraction_repair": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 1400,
	"temperature": 0.0,
	"top_p": 0.95,
	},
	"voice_turn": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 800,
	"temperature": 0.0,
	"top_p": 0.95,
	},
	"voice_turn_repair": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 700,
	"temperature": 0.0,
	"top_p": 0.95,
	},
	"retry_comparison": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 1500,
	"temperature": 0.15,
	"top_p": 0.95,
	},
	"retry_comparison_repair": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 1200,
	"temperature": 0.0,
	"top_p": 0.95,
	},
	"deal_verdict": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 1500,
	"temperature": 0.2,
	"top_p": 0.95,
	},
	"deal_verdict_repair": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 1200,
	"temperature": 0.0,
	"top_p": 0.95,
	},
	# Deal phase: semantic dimension scoring (JSON, split call 1 — scores only)
	"deal_scorecard_scoring": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 2500,
	"temperature": 0.1,
	"top_p": 0.95,
	},
	"deal_scorecard_scoring_repair": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 2000,
	"temperature": 0.0,
	"top_p": 0.95,
	},
	# Deal phase: coaching text (JSON, split call 2 — coaching only)
	"deal_scorecard_coaching": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 3000,
	"temperature": 0.2,
	"top_p": 0.95,
	},
	"deal_scorecard_repair": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 2000,
	"temperature": 0.0,
	"top_p": 0.95,
	},
	"structure_pitch": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 1000,
	"temperature": 0.0,
	"top_p": 0.95,
	},
	"structure_pitch_repair": {
	"enable_thinking": False,
	"reasoning_budget": 0,
	"max_tokens": 900,
	"temperature": 0.0,
	"top_p": 0.95,
	},
	}

	_VALID_AUDIO_FORMATS = frozenset({"webm", "wav", "mp3", "m4a", "ogg"})

	_AUDIO_MIME: dict[str, str] = {
	"webm": "audio/webm",
	"wav": "audio/wav",
	"mp3": "audio/mpeg",
	"m4a": "audio/mp4",
	"ogg": "audio/ogg",
	}

	# Modes where the response must be JSON — apply safe extraction from reasoning_content if needed
	_JSON_MODES: frozenset[str] = frozenset({
	"scorecard_scoring",
	"scorecard_scoring_repair",
	"scorecard_full",
	"scorecard_full_repair",
	"scorecard_coaching",
	"scorecard_coaching_repair",
	"legacy_full_scorecard",
	"voice_extraction",
	"voice_extraction_repair",
	"voice_turn",
	"voice_turn_repair",
	"retry_comparison",
	"retry_comparison_repair",
	"deal_verdict",
	"deal_verdict_repair",
	"deal_scorecard_scoring",
	"deal_scorecard_scoring_repair",
	"deal_scorecard_coaching",
	"deal_scorecard_repair",
	})


	def _extract_json_from_reasoning(reasoning: str) -> str \| None:
	"""Extract first complete JSON object block from reasoning_content."""
	start = reasoning.find("{")
	end = reasoning.rfind("}")
	if start != -1 and end != -1 and end > start:
	return reasoning[start : end + 1].strip()
	return None


	# Phrases that only appear in internal reasoning monologue, never in a real response.
	_REASONING_LEAK_SIGNALS = (
	"need to keep under",
	"check constraints",
	"that's one sentence",
	"actually it's",
	"make sure we reference",
	"under 3 sentences",
	"the question itself",
	"so okay.",
	"let me ",
	"i need to ",
	"i should ",
	"i'll ask",
	"i will ask",
	"one question,",
	"plain language.",
	"no advice,",
	"no compliments,",
	)


	def _strip_reasoning_leak(content: str) -> str:
	"""Remove internal monologue that leaked into content for thinking-mode calls.

	When reasoning_budget is insufficient the model continues "thinking" inside
	the content field before reaching the actual response. This function detects
	that pattern and returns only the final intended output (the last question).
	"""
	lower = content.lower()
	if not any(sig in lower for sig in _REASONING_LEAK_SIGNALS):
	return content

	# Reasoning leaked — extract the last real sentence(s).
	# Split on sentence boundaries and walk backwards to find the last
	# question, which is the intended output.
	import re
	sentences = [s.strip() for s in re.split(r"(?<=[.!?])\s+", content) if s.strip()]
	if not sentences:
	return content

	# Find the last sentence ending with "?"
	for i in range(len(sentences) - 1, -1, -1):
	if sentences[i].endswith("?"):
	# Include the sentence before it (context) if it's clean prose
	if i > 0 and not any(sig in sentences[i - 1].lower() for sig in _REASONING_LEAK_SIGNALS):
	return f"{sentences[i - 1]} {sentences[i]}"
	return sentences[i]

	# No question mark found — return the last non-reasoning sentence
	for sent in reversed(sentences):
	if not any(sig in sent.lower() for sig in _REASONING_LEAK_SIGNALS):
	return sent

	# Couldn't isolate anything clean — raise so the caller serves its fallback
	# message instead of showing garbage to the user.
	logger.warning("_strip_reasoning_leak: could not isolate clean output, raising for caller fallback")
	raise RuntimeError("Reasoning leaked into content and could not be cleaned (mode content fully contaminated)")


	def _get_config() -> tuple[str, str, str]:
	"""Return (api_key, base_url, model). Raises RuntimeError if key is absent."""
	api_key = os.getenv("NVIDIA_API_KEY", "").strip()
	if not api_key:
	raise RuntimeError(
	"NVIDIA_API_KEY is not set. "
	"Add it to your .env file locally or as a HF Space Secret in deployment. "
	"Never hardcode the key."
	)
	base_url = os.getenv("NVIDIA_BASE_URL", _DEFAULT_BASE_URL).strip() or _DEFAULT_BASE_URL
	model = os.getenv("NVIDIA_OMNI_MODEL", _DEFAULT_MODEL).strip() or _DEFAULT_MODEL
	return api_key, base_url, model


	def is_configured() -> bool:
	"""Return True if NVIDIA_API_KEY is present in the environment."""
	return bool(os.getenv("NVIDIA_API_KEY", "").strip())


	def health_check() -> dict[str, Any]:
	"""Return configuration status without exposing the API key."""
	configured = is_configured()
	base_url = os.getenv("NVIDIA_BASE_URL", _DEFAULT_BASE_URL)
	model = os.getenv("NVIDIA_OMNI_MODEL", _DEFAULT_MODEL)
	return {
	"provider": "nvidia",
	"configured": configured,
	"base_url": base_url,
	"model": model,
	"api_key_present": configured,
	"message": (
	"NVIDIA client ready" if configured
	else "NVIDIA_API_KEY missing — add to .env or HF Space Secrets"
	),
	}


	def _resolve_mode_params(
	mode: str,
	temperature: float \| None = None,
	max_tokens: int \| None = None,
	) -> tuple[float, int, float, bool, int]:
	"""Return (temp, tokens, top_p, enable_thinking, reasoning_budget) for a mode."""
	defaults = _TASK_DEFAULTS.get(mode, _TASK_DEFAULTS["opponent"])
	temp = temperature if temperature is not None else defaults["temperature"]
	tokens = max_tokens if max_tokens is not None else defaults["max_tokens"]
	top_p: float = defaults.get("top_p", 0.95)
	enable_thinking: bool = defaults.get("enable_thinking", True)
	reasoning_budget: int = defaults.get("reasoning_budget", 0)
	if mode in _JSON_MODES:
	enable_thinking = False
	reasoning_budget = 0
	reasoning_budget = min(reasoning_budget, tokens)
	return temp, tokens, top_p, enable_thinking, reasoning_budget


	def _complete_chat(
	client: OpenAI,
	model: str,
	messages: list[dict],
	mode: str,
	temperature: float \| None = None,
	max_tokens: int \| None = None,
	) -> str:
	"""Shared chat completion with mode-specific token/thinking settings."""
	temp, tokens, top_p, enable_thinking, reasoning_budget = _resolve_mode_params(
	mode, temperature, max_tokens
	)
	completion = client.chat.completions.create(
	model=model,
	messages=messages, # type: ignore[arg-type]
	temperature=temp,
	max_tokens=tokens,
	top_p=top_p,
	extra_body={
	"chat_template_kwargs": {"enable_thinking": enable_thinking},
	"reasoning_budget": reasoning_budget,
	},
	)
	temp_d, tokens_d, _tp, thinking_d, _rb = _resolve_mode_params(mode, temperature, max_tokens)
	msg = completion.choices[0].message
	content = (msg.content or "").strip()
	reasoning = (getattr(msg, "reasoning_content", None) or "").strip()

	# Diagnostics (no secrets, no full prompt/audio): help spot truncation vs empty content.
	logger.debug(
	"Nemotron mode=%s max_tokens=%s thinking=%s content_len=%d reasoning_present=%s",
	mode, tokens_d, thinking_d, len(content), bool(reasoning),
	)

	if not content:
	if mode in _JSON_MODES and reasoning:
	# JSON mode: try to salvage a JSON block from the reasoning field.
	extracted = _extract_json_from_reasoning(reasoning)
	if extracted:
	logger.info(
	"Nemotron content empty; extracted JSON block from reasoning_content (mode=%s)",
	mode,
	)
	content = extracted
	else:
	logger.warning(
	"Nemotron content empty; no JSON in reasoning_content (mode=%s)",
	mode,
	)
	else:
	# Non-JSON mode (opponent, deal_round, rewrite, …):
	# reasoning_content is the model's compressed internal thinking —
	# it is NEVER safe to display. Let it raise so the caller serves
	# its own clean fallback message.
	logger.warning(
	"Nemotron content empty for non-JSON mode=%s; "
	"reasoning_content not usable as output — raising for caller fallback",
	mode,
	)

	if not content:
	raise RuntimeError(
	f"Nemotron returned empty content (mode={mode}). "
	"Caller should serve its fallback."
	)

	# For thinking-mode calls (opponent, deal_round, rewrite) strip any internal
	# monologue that leaked into the content field when reasoning_budget runs short.
	if enable_thinking and mode not in _JSON_MODES:
	content = _strip_reasoning_leak(content)

	return content


	def call_omni_audio_json(
	prompt: str,
	audio_base64: str,
	audio_format: str,
	mode: str = "voice_extraction",
	timeout: int = 60,
	source_format: str \| None = None,
	decoded_bytes: int \| None = None,
	) -> str:
	"""Call Nemotron Omni with audio + text prompt; return response text (JSON expected).

	Raises:
	ValueError: invalid audio input
	OmniAudioError: API rejected audio or call failed
	"""
	if not audio_base64 or not str(audio_base64).strip():
	raise ValueError("audio_base64 is required and must be non-empty")
	fmt = str(audio_format or "").strip().lower().lstrip(".")
	if fmt not in _VALID_AUDIO_FORMATS:
	raise ValueError(
	f"audio_format must be one of: {', '.join(sorted(_VALID_AUDIO_FORMATS))}"
	)

	api_key, base_url, model = _get_config()
	mime = _AUDIO_MIME[fmt]
	audio_url = f"data:{mime};base64,{audio_base64.strip()}"

	logger.info(
	"nvidia_client: omni audio call mode=%s format=%s source_format=%s bytes=%s mime=%s",
	mode,
	fmt,
	source_format or fmt,
	decoded_bytes if decoded_bytes is not None else "unknown",
	mime,
	)

	messages = [
	{
	"role": "user",
	"content": [
	{"type": "text", "text": prompt},
	{"type": "audio_url", "audio_url": {"url": audio_url}},
	],
	}
	]

	client = OpenAI(api_key=api_key, base_url=base_url, timeout=timeout)
	try:
	return _complete_chat(client, model, messages, mode)
	except APIStatusError as exc:
	detail = exc.message or str(exc)
	logger.warning(
	"NVIDIA Omni audio status error %s: %s (format=%s bytes=%s)",
	exc.status_code,
	detail,
	fmt,
	decoded_bytes,
	)
	raise OmniAudioError(
	detail,
	audio_format_sent=fmt,
	source_format=source_format or fmt,
	decoded_bytes=decoded_bytes,
	mode=mode,
	suggestion=(
	"Audio was normalized to WAV when possible. "
	"Verify NVIDIA audio payload schema or install ffmpeg for conversion."
	),
	) from exc
	except (APITimeoutError, APIConnectionError) as exc:
	logger.warning("NVIDIA Omni audio connection error: %s", type(exc).__name__)
	raise OmniAudioError(
	f"Connection error: {type(exc).__name__}",
	audio_format_sent=fmt,
	source_format=source_format or fmt,
	decoded_bytes=decoded_bytes,
	mode=mode,
	suggestion="Retry the voice request or check NVIDIA API connectivity.",
	) from exc
	except RuntimeError:
	raise
	except Exception as exc:
	logger.warning("NVIDIA Omni audio unexpected error: %s", type(exc).__name__)
	raise OmniAudioError(
	type(exc).__name__,
	audio_format_sent=fmt,
	source_format=source_format or fmt,
	decoded_bytes=decoded_bytes,
	mode=mode,
	suggestion="Verify NVIDIA audio payload schema or audio conversion.",
	) from exc


	def generate_nemotron_response(
	messages: list[dict[str, str]],
	mode: str = "opponent",
	temperature: float \| None = None,
	max_tokens: int \| None = None,
	timeout: int = 30,
	) -> str:
	"""Call NVIDIA Nemotron and return the response text.

	Args:
	messages: OpenAI-format message list [{"role": ..., "content": ...}, ...]
	mode: task type key — "opponent", "scorecard_coaching", "rewrite", etc.
	temperature: overrides mode default if provided
	max_tokens: overrides mode default if provided
	timeout: request timeout in seconds

	Returns:
	Response text string from the model.

	Raises:
	RuntimeError: on missing key or any API failure (clean message, no key leak).
	"""
	api_key, base_url, model = _get_config()
	client = OpenAI(api_key=api_key, base_url=base_url, timeout=timeout)

	# One retry on transient server/rate errors only — never retry 4xx (e.g. bad audio 400).
	for attempt in range(2):
	try:
	return _complete_chat(client, model, messages, mode, temperature, max_tokens)

	except APITimeoutError:
	logger.warning("NVIDIA API timed out after %ds (mode=%s)", timeout, mode)
	raise RuntimeError(
	f"NVIDIA Nemotron request timed out after {timeout}s. "
	"Check your connection or increase timeout."
	)
	except APIConnectionError as exc:
	if attempt == 0:
	logger.warning("NVIDIA connection error (mode=%s) — retrying once", mode)
	time.sleep(0.8)
	continue
	logger.warning("NVIDIA API connection error: %s", exc)
	raise RuntimeError(
	"Could not connect to NVIDIA API. "
	"Verify NVIDIA_BASE_URL and your network connection."
	)
	except APIStatusError as exc:
	transient = exc.status_code in (429, 500, 502, 503)
	if transient and attempt == 0:
	logger.warning(
	"NVIDIA transient HTTP %s (mode=%s) — retrying once", exc.status_code, mode
	)
	time.sleep(0.8)
	continue
	logger.warning(
	"NVIDIA API status error %s (mode=%s): %s", exc.status_code, mode, exc.message
	)
	raise RuntimeError(
	f"NVIDIA API returned HTTP {exc.status_code}. "
	"Check your NVIDIA_API_KEY and model ID."
	)
	except Exception as exc:
	logger.warning("NVIDIA API unexpected error (mode=%s): %s", mode, type(exc).__name__)
	raise RuntimeError(
	f"NVIDIA model call failed ({type(exc).__name__}). See server logs."
	)

	# Unreachable (loop either returns or raises) — defensive.
	raise RuntimeError("NVIDIA model call failed after retry.")