#!/bin/env bash
#
# End-to-end smoke test for a deployed neuron.
#
# Confirms the daemon is reachable, loads a small public Qwen3 GGUF,
# fires a reasoning probe at /v1/chat/completions, and prints the
# answer. Used to validate the candle harness on a real GPU host
# before trusting it for production traffic, and as a regression test
# after pushing new neuron builds.
#
# Usage:
#   script/validate-neuron.sh [host] [model_id] [quant]
#
# Defaults:
#   host     = beast.hanzalova.internal
#   model_id = unsloth/Qwen3-0.6B-GGUF  (official Qwen3-*-GGUF repos
#              ship Q8_0 only; unsloth's mirror ships the full Q-spectrum
#              including Q4_K_M)
#   quant    = Q4_K_M

set -euo pipefail

HOST="${1:-beast.hanzalova.internal}"
MODEL_ID="${2:-unsloth/Qwen3-0.6B-GGUF}"
QUANT="${3:-Q4_K_M}"
PORT="${NEURON_PORT:-13131}"
BASE="http://${HOST}:${PORT}"

# Reasoning probe — concrete, low-temperature answer that small models
# can still get right. "Paris" is a strong signal of basic competence
# beyond gibberish.
PROBE_PROMPT='What is the capital of Georgia (Caucasus)? Respond with the city name only, no punctuation.'
EXPECT_SUBSTR='Tbilisi'
# Qwen3 prepends <think>...</think> reasoning before the answer when the
# chat template enables thinking mode, which eats most of a small token
# budget. 256 leaves enough room for thinking + final answer.
MAX_TOKENS=256

# /models/load is synchronous — neuron blocks the response until the
# hf-hub download + GGUF parse + tensor materialisation is done. A
# fresh 0.6B-Q4_K_M is ~400 MB; on a slow link or cold cache that's
# easily a minute. Pick a generous ceiling.
LOAD_TIMEOUT=600
INFER_TIMEOUT=120

# Status messages go to stderr so command substitutions like
# `raw=$(run_probe)` capture only the function's intended return value
# (an HTTP body), not the progress chatter.
say() { printf '[%s] %s\n' "${HOST}" "$*" >&2; }
die() { say "FAIL: $*"; exit 1; }

probe_health() {
    curl --silent --fail --max-time 5 "${BASE}/health" >/dev/null \
        || die "neuron not reachable at ${BASE}/health"
}

list_loaded_ids() {
    # The manifest is YAML and uses yq; HTTP responses are JSON and use
    # jq directly. pip-yq parses input as YAML by default, which trips
    # on JSON content that happens to look like YAML aliases (chatcmpl
    # ids, escaped quotes inside `<think>...</think>` blocks, etc.).
    curl --silent --fail "${BASE}/models" | jq -r '.[].id'
}

is_loaded() {
    list_loaded_ids 2>/dev/null | grep -Fxq "${MODEL_ID}"
}

trigger_load() {
    say "POST /models/load ${MODEL_ID} (quant=${QUANT:-<dense>}, device=[0])"
    say "  (synchronous; may take a minute on first run while HF downloads)"
    # Build the payload via jq so the optional `quant` field is
    # omitted entirely when empty — that's the signal to the harness
    # to take the dense safetensors load path rather than GGUF.
    local payload
    if [[ -z "${QUANT}" ]]; then
        payload=$(jq -n -c \
            --arg id "${MODEL_ID}" \
            '{model_id: $id, harness: "candle", devices: [0]}')
    else
        payload=$(jq -n -c \
            --arg id "${MODEL_ID}" \
            --arg q "${QUANT}" \
            '{model_id: $id, harness: "candle", quant: $q, devices: [0]}')
    fi
    # --write-out captures the response code on a separate line so we
    # can surface a real diagnostic instead of relying on --fail.
    local resp http_code body
    resp=$(curl --silent --show-error --max-time "${LOAD_TIMEOUT}" \
        --write-out '\n__HTTP__%{http_code}' \
        -X POST "${BASE}/models/load" \
        -H 'content-type: application/json' \
        --data "${payload}") || die "curl /models/load failed: $?"
    http_code=$(echo "${resp}" | grep -oP '(?<=__HTTP__)\d+$' | tail -1)
    body=$(echo "${resp}" | sed '$ s/__HTTP__.*$//')
    if [[ "${http_code}" != "200" ]]; then
        die "load returned HTTP ${http_code}: ${body}"
    fi
    say "load returned ${http_code}: ${body}"
}

run_probe() {
    say "POST /v1/chat/completions (probe: ${PROBE_PROMPT})"
    local payload
    payload=$(jq -n -c \
        --arg model "${MODEL_ID}" \
        --arg content "${PROBE_PROMPT}" \
        --argjson tokens "${MAX_TOKENS}" \
        '{
            model: $model,
            messages: [{role: "user", content: $content}],
            temperature: 0.1,
            max_tokens: $tokens
        }')
    local resp http_code body
    resp=$(curl --silent --show-error --max-time "${INFER_TIMEOUT}" \
        --write-out '\n__HTTP__%{http_code}' \
        -X POST "${BASE}/v1/chat/completions" \
        -H 'content-type: application/json' \
        --data "${payload}") || die "curl /v1/chat/completions failed: $?"
    http_code=$(echo "${resp}" | grep -oP '(?<=__HTTP__)\d+$' | tail -1)
    body=$(echo "${resp}" | sed '$ s/__HTTP__.*$//')
    if [[ "${http_code}" != "200" ]]; then
        die "inference returned HTTP ${http_code}: ${body}"
    fi
    echo "${body}"
}

say "validating neuron at ${BASE}"
probe_health
say "/health OK"

if is_loaded; then
    say "${MODEL_ID} already loaded"
else
    trigger_load
fi

raw=$(run_probe)
echo "---"
# Dump the raw JSON. Don't pipe through `yq -r '.'` — yq's default
# YAML output mode chokes on JSON strings that contain `<` (and the
# `<think>` markers Qwen3 emits during reasoning are a perfect
# example). The targeted `yq -r '.path'` calls below work fine
# because jq's path filter mode bypasses the YAML re-emit.
echo "${raw}"
echo "---"

content=$(echo "${raw}" | jq -r '.choices[0].message.content // empty')
if [[ -z "${content}" ]]; then
    die "no content in chat completion response"
fi
say "assistant said: ${content}"

if echo "${content}" | grep -qiF "${EXPECT_SUBSTR}"; then
    say "PASS — response contains expected substring '${EXPECT_SUBSTR}'"
    exit 0
else
    die "response did not contain '${EXPECT_SUBSTR}'"
fi