import OpenAI from "openai";
import fsp from "fs/promises";
import { PdfReader } from "pdfreader";

const openaiClient = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

async function queryChatGPT(prompt: string) {
  let res = await openaiClient.chat.completions.create({
    model: "gpt-4o",
    messages: [
      {
        role: "system",
        content: prompt,
      },
    ],
    response_format: { type: "json_object" },
  });

  return JSON.parse(res.choices[0].message.content);
}

async function parseBufferAsync(buffer: Buffer): Promise<string> {
  let parsedData = "";

  return new Promise((resolve, reject) => {
    new PdfReader().parseBuffer(buffer, (error, item) => {
      if (error) return reject(error);
      if (!item) return resolve(parsedData);
      if (item.text) parsedData += item.text;
    });
  });
}

export default async function generateNote(
  folderPath: string
): Promise<string> {
  let energyData = {};
  let checklistData = {};
  let energyDataFlag = false;
  let checklistDataFlag = false;
  let energyFilePath = "";
  let checklistFilePath = "";

  const files = await fsp.readdir(folderPath);

  for (const file of files) {
    const input = await fsp.readFile(folderPath + `/${file}`);
    const pdfData = await parseBufferAsync(input);

    if (
      Object.keys(checklistData).length == 0 &&
      pdfData.toLowerCase().includes("general contractors")
    ) {
      const prompt = `
        Extract the following fields and contractor info from the given data and return the response in JSON.
        Fields: ["Site Address", "Lot Number", "Estimated Construction Cost", "Total Square Footage", "A/C Square Footage", "Number of Bedrooms", "Number of Bathrooms", "Construction Type", "Number of Stories", "Height", "General Contractors License", "Mechanical Contractors License", "Electrical Contractors License", "Plumbing Contractors License", "Roofing Contractors License", "Concrete/Mason Contractors License", "Confirm the following"]

        Data: ${pdfData}
      `;

      checklistDataFlag = true;
      checklistFilePath = file;
      checklistData = await queryChatGPT(prompt);
    }

    if (
      Object.keys(energyData).length == 0 &&
      pdfData.toLowerCase().includes("energy efficiency")
    ) {
      const prompt = `
        Extract the following fields form the given data and return the response in JSON.
        Fields: [Project Name, address, county]

        Data: ${pdfData}
      `;

      energyDataFlag = true;
      energyFilePath = file;
      energyData = await queryChatGPT(prompt);
    }
  }

  const addressCheck = {};

  if (checklistDataFlag && Object.keys(checklistData).length > 0) {
    for (const file of files) {
      if (file == checklistFilePath) continue;

      const input = await fsp.readFile(folderPath + `${file}`);
      const pdfData = await parseBufferAsync(input);

      const prompt = `
        Check if the address in the data given below matches this ${checklistData["Site Address"]}. Return the response in JSON with schema {match: Boolean}.

        Data: ${pdfData}
      `;

      const { match } = await queryChatGPT(prompt);
      addressCheck[file] = match;
    }
  }

  let note = "";

  if (!energyDataFlag) {
    note += "Energy Efficiency document not found.\n";
  }

  if (!checklistDataFlag) {
    note += `Checklist document not found.\n\n`;
  }

  let filesList = "";
  for (const file in addressCheck) {
    if (!addressCheck[file]) {
      filesList += file.split("/").pop() + "\n";
    }
  }

  if (filesList != "") {
    note +=
      "Below files don't have address or the address doesn't match with the address in checklist\n\n";
    note += filesList;
    note += "\n";
  }

  if (energyDataFlag || checklistDataFlag) {
    note += "\nExtracted Data:\n";
  }

  if (Object.keys(energyData).length > 0) {
    note += `\n${JSON.stringify(energyData, null, 2)}\n`;
  }

  if (Object.keys(checklistData).length > 0) {
    note += `\n${JSON.stringify(checklistData, null, 2)}\n`;
  }

  return note;
}