// spacecall — call any Gradio Space through the beacon
// Handles the full Gradio pattern: upload → call → poll → result
// Requires HF_TOKEN env var for authenticated access (ZeroGPU quota, private spaces)

const HF_TOKEN = process.env.HF_TOKEN
const authHeader = HF_TOKEN ? { Authorization: `Bearer ${HF_TOKEN}` } : {}

function spaceUrl(owner, name) {
  const slug = s => s.toLowerCase().replace(/[^a-z0-9]+/g, '-').replace(/^-|-$/g, '')
  return `https://${slug(owner)}-${slug(name)}.hf.space`
}

// Upload a file (URL or local path) to a Space's /gradio_api/upload
// Returns the Gradio FileData object ready to use as input
async function uploadToSpace(base, fileUrl, filename) {
  // Fetch the source file
  const fileRes = await fetch(fileUrl)
  if (!fileRes.ok) throw new Error(`Could not fetch file: ${fileUrl}`)
  const blob = await fileRes.blob()

  const form = new FormData()
  form.append('files', blob, filename || 'upload')

  const r = await fetch(`${base}/gradio_api/upload`, {
    method: 'POST',
    headers: { ...authHeader },
    body: form,
  })
  if (!r.ok) throw new Error(`Upload failed: ${r.status} ${await r.text()}`)
  const paths = await r.json()  // [ "/tmp/gradio/.../filename" ]
  const path = paths[0]
  return {
    path,
    meta: { _type: 'gradio.FileData' },
    orig_name: filename || path.split('/').pop(),
  }
}

// Resolve inputs — any string that looks like a URL gets uploaded first
async function resolveInputs(base, inputs) {
  const resolved = {}
  for (const [key, val] of Object.entries(inputs)) {
    if (typeof val === 'string' && (val.startsWith('http://') || val.startsWith('https://'))) {
      const filename = val.split('/').pop().split('?')[0] || key
      resolved[key] = await uploadToSpace(base, val, filename)
    } else {
      resolved[key] = val
    }
  }
  return resolved
}

// Poll a Gradio SSE stream until complete, return parsed result
async function pollResult(url, timeoutMs = 120000) {
  const start = Date.now()
  const r = await fetch(url, { headers: { ...authHeader } })
  if (!r.ok) throw new Error(`Poll failed: ${r.status}`)

  const reader = r.body.getReader()
  const decoder = new TextDecoder()
  let buffer = ''

  while (Date.now() - start < timeoutMs) {
    const { done, value } = await reader.read()
    if (done) break
    buffer += decoder.decode(value, { stream: true })

    const lines = buffer.split('\n')
    buffer = lines.pop()

    for (const line of lines) {
      if (line.startsWith('event: complete')) {
        // next line will be data
        continue
      }
      if (line.startsWith('data:') && buffer.includes('event: complete') || lines.some(l => l.includes('event: complete'))) {
        try {
          const data = JSON.parse(line.replace('data: ', ''))
          return data
        } catch {}
      }
      if (line.startsWith('data:')) {
        try {
          const data = JSON.parse(line.replace('data: ', ''))
          // If this is a final result (array, not a status object)
          if (Array.isArray(data)) return data
        } catch {}
      }
      if (line.includes('event: error')) {
        throw new Error(`Space returned error during generation`)
      }
    }
  }
  throw new Error('Timed out waiting for Space result')
}

// Fetch the Gradio schema for a Space
async function fetchSpaceSchema(base) {
  const url = `${base}/gradio_api/info`
  let r
  try {
    r = await fetch(url, { signal: AbortSignal.timeout(10000) })
  } catch (err) {
    throw new Error(`Schema fetch failed (${url}): ${err.message}`)
  }
  if (!r.ok) throw new Error(`Schema fetch ${r.status} (${url})`)
  return r.json()
}

// Use the LLM to map a natural language prompt → { endpoint, inputs }
// given the Space's Gradio schema
async function resolveWithLLM(schema, userPrompt, { chat }) {
  const endpoints = Object.entries(schema?.named_endpoints ?? {})
    .map(([name, def]) => {
      const params = (def.parameters ?? [])
        .map(p => `  ${p.label} (${p.python_type?.type ?? p.type ?? 'any'}): ${p.component ?? ''}`)
        .join('\n')
      return `Endpoint "${name}":\n${params}`
    })
    .join('\n\n')

  const messages = [
    {
      role: 'system',
      content: `You are an API parameter resolver. Given a Gradio Space's available endpoints and a user's natural language request, return ONLY a JSON object with two fields:
- "endpoint": the best matching endpoint name (string)
- "inputs": an object mapping each required parameter name to its value

Rules:
- For image/file parameters: use the URL directly as the string value (the caller will handle upload)
- For missing optional parameters: omit them
- Return ONLY valid JSON, no explanation, no markdown`
    },
    {
      role: 'user',
      content: `Available endpoints:\n${endpoints}\n\nUser request: ${userPrompt}\n\nReturn JSON only:`
    }
  ]

  const reply = await chat(messages)

  // Strip markdown code fences if LLM added them
  const clean = reply.replace(/```json\n?/g, '').replace(/```\n?/g, '').trim()
  return JSON.parse(clean)
}

// Main entrypoint — call any Gradio Space endpoint
// space: "owner/name" or { owner, name }
// endpoint: named endpoint string e.g. "predict" or "/predict"
// inputs: object of named parameters
// returns: { outputs, output_urls, raw }
export async function callSpace({ space, endpoint = 'predict', inputs = {}, timeout = 120000 }) {
  const [owner, name] = typeof space === 'string' ? space.split('/') : [space.owner, space.name]
  if (!owner || !name) throw new Error('space must be "owner/name"')

  const base = spaceUrl(owner, name)
  const ep = endpoint.startsWith('/') ? endpoint.slice(1) : endpoint

  // Resolve file inputs
  const resolvedInputs = await resolveInputs(base, inputs)

  // Submit
  const callRes = await fetch(`${base}/gradio_api/call/v2/${ep}`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json', ...authHeader },
    body: JSON.stringify(resolvedInputs),
  })

  if (!callRes.ok) {
    const err = await callRes.text()
    throw new Error(`Space call failed ${callRes.status}: ${err}`)
  }

  const { event_id } = await callRes.json()
  if (!event_id) throw new Error('No event_id returned from Space')

  // Poll
  const pollUrl = `${base}/gradio_api/call/${ep}/${event_id}`
  const raw = await pollResult(pollUrl, timeout)

  // Extract output URLs (Gradio returns FileData objects or plain values)
  const output_urls = []
  const walk = (val) => {
    if (!val) return
    if (typeof val === 'string' && val.startsWith('/tmp/')) {
      output_urls.push(`${base}/gradio_api/file=${val}`)
    } else if (val?.path) {
      output_urls.push(`${base}/gradio_api/file=${val.path}`)
    } else if (Array.isArray(val)) {
      val.forEach(walk)
    } else if (typeof val === 'object') {
      Object.values(val).forEach(walk)
    }
  }
  walk(raw)

  return { outputs: raw, output_urls, space: `${owner}/${name}`, endpoint: ep, event_id }
}

// Build a Gradio-compatible schema object from a KNOWN_MANIFESTS entry
// so we never need a live /gradio_api/info fetch for known spaces
function manifestToSchema(manifest) {
  const params = (manifest.inputs ?? []).map(i => ({
    label: i.name,
    python_type: { type: i.type ?? 'string' },
    component: i.type ?? 'string',
  }))
  return { named_endpoints: { predict: { parameters: params } } }
}

async function getSchema(space) {
  const { getSpaceManifest } = await import('./curlycue.js')
  const manifest = getSpaceManifest(space)
  if (manifest) return manifestToSchema(manifest)
  const [owner, name] = space.split('/')
  return fetchSpaceSchema(spaceUrl(owner, name))
}

// Preview only — LLM resolves parameters but does NOT execute the Space call
// Used by the UI to show curl preview before committing
export async function previewSpaceCall({ space, prompt }) {
  const { chat } = await import('./llm.js')
  const schema = await getSchema(space)
  const resolved = await resolveWithLLM(schema, prompt, { chat })
  return {
    space,
    resolved,
    curl: `curl -X POST https://acecalisto3-beacon.hf.space/space/ask \\\n  -H "Content-Type: application/json" \\\n  -d '${JSON.stringify({ space, prompt }, null, 2)}'`,
    structured: { space, endpoint: resolved.endpoint, inputs: resolved.inputs },
  }
}

// Natural language entrypoint — user just describes what they want
// The LLM reads the Space schema and fills in the parameters
export async function callSpaceFromPrompt({ space, prompt, timeout = 120000 }) {
  const { chat } = await import('./llm.js')
  const schema = await getSchema(space)

  const resolved = await resolveWithLLM(schema, prompt, { chat })
  if (!resolved?.endpoint || !resolved?.inputs) {
    throw new Error('LLM could not resolve Space parameters from prompt')
  }

  const result = await callSpace({ space, endpoint: resolved.endpoint, inputs: resolved.inputs, timeout })
  return { ...result, resolved_from_prompt: prompt, llm_resolved: resolved }
}