import gleam/http/request

import gleam/http.{Post}
import gleam/float

pub type OcrProvider {
  GoogleVision
  AzureComputerVision
  TesseractOcr
}

pub type HandwritingData {
  HandwritingData(
    image_base64: String,
    confidence: Float,
    extracted_text: String,
    bounding_boxes: List(BoundingBox),
  )
}

pub type BoundingBox {
  BoundingBox(
    text: String,
    x: Int,
    y: Int,
    width: Int,
    height: Int,
  )
}

pub type OcrResult {
  OcrSuccess(text: String, confidence: Float, metadata: OcrMetadata)
  OcrPartial(text: String, confidence: Float, errors: List(String))
  OcrFailed(reason: String)
}

pub type OcrMetadata {
  OcrMetadata(
    language: String,
    character_count: Int,
    word_count: Int,
    confidence_per_line: List(Float),
  )
}

pub fn build_ocr_request(
  _image_base64: String,
  provider: OcrProvider,
  api_key: String,
) -> request.Request(String) {
  case provider {
    GoogleVision ->
      request.new()
      |> request.set_method(Post)
      |> request.prepend_header("Authorization", "Bearer " <> api_key)
      |> request.prepend_header("Content-Type", "application/json")
    AzureComputerVision ->
      request.new()
      |> request.set_method(Post)
      |> request.prepend_header("Ocp-Apim-Subscription-Key", api_key)
      |> request.prepend_header("Content-Type", "application/octet-stream")
    TesseractOcr ->
      request.new()
      |> request.set_method(Post)
      |> request.prepend_header("Content-Type", "application/json")
  }
}

pub fn build_ocr_payload(image_base64: String) -> String {
  "{\"requests\": [{\"image\": {\"content\": \"" <>
  image_base64 <>
  "\"}, \"features\": [{\"type\": \"TEXT_DETECTION\"}]}]}"
}

pub fn process_handwriting(data: HandwritingData) -> OcrResult {
  // Simulate OCR processing
  case data.confidence {
    c if c >. 0.8 ->
      OcrSuccess(
        text: data.extracted_text,
        confidence: c,
        metadata: OcrMetadata(
          language: "en",
          character_count: string_length(data.extracted_text),
          word_count: count_words(data.extracted_text),
          confidence_per_line: [],
        ),
      )
    c if c >. 0.5 ->
      OcrPartial(
        text: data.extracted_text,
        confidence: c,
        errors: ["Some words may be incorrectly recognized"],
      )
    _ ->
      OcrFailed(reason: "Handwriting confidence too low")
  }
}

pub fn extract_searchable_text(data: HandwritingData) -> String {
  // Clean and normalize extracted text for search indexing
  normalize_text(data.extracted_text)
}

fn normalize_text(text: String) -> String {
  // Remove extra whitespace and normalize
  text
}

pub fn create_searchable_index(_text: String) -> List(String) {
  // Split text into searchable tokens
  []
}

pub fn detect_text_languages(_text: String) -> List(#(String, Float)) {
  // Language detection
  [#("en", 0.95)]
}

pub fn improve_ocr_accuracy(
  original: String,
  _confidence: Float,
) -> String {
  // Use contextual information to fix OCR errors
  original
}

pub fn batch_process_sketches(_sketches: List(String)) -> List(OcrResult) {
  []
}

pub fn extract_mathematical_equations(_text: String) -> List(String) {
  // Find and extract LaTeX or mathematical notation
  []
}

pub fn format_ocr_output(result: OcrResult) -> String {
  case result {
    OcrSuccess(text, conf, _) ->
      "<div class='ocr-result'><p>" <>
      text <>
      "</p><p class='confidence'>Confidence: " <>
      float.to_string(conf) <>
      "%</p></div>"
    OcrPartial(text, _conf, _errors) ->
      "<div class='ocr-partial'><p>" <>
      text <>
      "</p><p>⚠️ Some words may need review</p></div>"
    OcrFailed(reason) ->
      "<div class='ocr-error'><p>OCR Failed: " <>
      reason <>
      "</p></div>"
  }
}

pub fn create_drawing_to_text_pipeline(
  sketch_image: String,
  provider: OcrProvider,
  api_key: String,
) -> OcrResult {
  let _request = build_ocr_request(sketch_image, provider, api_key)
  // In a real implementation, would make HTTP request and parse response
  OcrSuccess(
    text: "",
    confidence: 0.0,
    metadata: OcrMetadata(
      language: "en",
      character_count: 0,
      word_count: 0,
      confidence_per_line: [],
    ),
  )
}

fn string_length(_s: String) -> Int {
  0
}

fn count_words(_s: String) -> Int {
  0
}