npx create-next-app@latest airesumecoverlettergenerator

npm i react-markdown openai

npm install @copilotkit/react-ui @copilotkit/react-core @copilotkit/backend

"use client";

// Import React and necessary hooks from the react library
import React from "react";
import { useState } from "react";
// Import the ReactMarkdown component to render markdown content
import ReactMarkdown from "react-markdown";
// Import the Link component from Next.js for navigation
import Link from "next/link";

function Resume() {
  // State variables to store the resume and cover letter content
  const [coverLetter, setCoverLetter] = useState("");
  const [resume, setResume] = useState("");

  return (
    // Main container with flex layout, full width, and minimum height of screen
    <div className="flex flex-col w-full min-h-screen bg-gray-100 dark:bg-gray-800">
      {/* Header section with a fixed height, padding, and border at the bottom */}
      <header className="flex items-center h-16 px-4 border-b shrink-0 md:px-6 bg-white dark:bg-gray-900">
        {/* Link component for navigation with custom styles */}
        <Link
          href="#"
          className="flex items-center gap-2 text-lg font-semibold md:text-base"
          prefetch={false}>
          <span className="sr-only text-gray-500">Resume Dashboard</span>
          <h1>Resume & Cover Letter Generator</h1>
        </Link>
      </header>
      {/* Main content area with padding */}
      <main className="flex-1 p-4 md:p-8 lg:p-10">
        {/* Container for the content with maximum width and centered alignment */}
        <div className="max-w-4xl mx-auto grid gap-8">
          {/* Section for displaying the resume */}
          <section>
            <div className="bg-white dark:bg-gray-900 rounded-lg shadow-sm">
              <div className="p-6 md:p-8">
                <h2 className="text-lg font-bold">Resume</h2>
                <div className="my-6" />
                <div className="grid gap-6">
                  {/* Conditional rendering of the resume content */}
                  {resume ? (
                    <ReactMarkdown>{resume}</ReactMarkdown>
                  ) : (
                    <div>No Resume To Display</div>
                  )}
                </div>
              </div>
            </div>
          </section>
          {/* Section for displaying the cover letter */}
          <section>
            <div className="bg-white dark:bg-gray-900 rounded-lg shadow-sm">
              <div className="p-6 md:p-8">
                <h2 className="text-lg font-bold">Cover Letter</h2>
                <div className="my-6" />
                <div className="grid gap-4">
                  {/* Conditional rendering of the cover letter content */}
                  {coverLetter ? (
                    <ReactMarkdown>{coverLetter}</ReactMarkdown>
                  ) : (
                    <div>No Cover Letter To Display</div>
                  )}
                </div>
              </div>
            </div>
          </section>
        </div>
      </main>
    </div>
  );
}

export default Resume;

import Resume from "./components/Resume";

export default function Home() {
  return <Resume />;
}

import { useCopilotAction, useCopilotReadable } from "@copilotkit/react-core";

useCopilotReadable({
    description: "The user's cover letter.",
    value: coverLetter,
  });

  useCopilotReadable({
    description: "The user's resume.",
    value: resume,
  });

useCopilotAction(
  {
    // Define the name of the action
    name: "createCoverLetterAndResume",
    // Provide a description for the action
    description: "Create a cover letter and resume for a job application.",
    // Define the parameters required for the action
    parameters: [
      {
        // Name of the first parameter
        name: "coverLetterMarkdown",
        // Type of the first parameter
        type: "string",
        // Description of the first parameter
        description:
          "Markdown text for a cover letter to introduce yourself and briefly summarize your professional background.",
        // Mark the first parameter as required
        required: true,
      },
      {
        // Name of the second parameter
        name: "resumeMarkdown",
        // Type of the second parameter
        type: "string",
        // Description of the second parameter
        description:
          "Markdown text for a resume that displays your professional background and relevant skills.",
        // Mark the second parameter as required
        required: true,
      },
    ],
    // Define the handler function to be executed when the action is called
    handler: async ({ coverLetterMarkdown, resumeMarkdown }) => {
      // Update the state with the provided cover letter markdown text
      setCoverLetter(coverLetterMarkdown);
      // Update the state with the provided resume markdown text
      setResume(resumeMarkdown);
    },
  },
  // Empty dependency array, indicating this effect does not depend on any props or state
  [],
);

import { CopilotKit } from "@copilotkit/react-core";
import { CopilotSidebar } from "@copilotkit/react-ui";
import "@copilotkit/react-ui/styles.css";

export default function Home() {
  return (
    <CopilotKit runtimeUrl="/api/copilotkit">
      <CopilotSidebar
        instructions={"Help the user create a cover letter and resume"}
        labels={{
          initial:
            "Welcome to the cover letter app! Add your LinkedIn, X, or GitHub profile link below.",
        }}
        defaultOpen={true}
        clickOutsideToClose={false}>
        <Resume />
      </CopilotSidebar>
    </CopilotKit>
  );
}

OPENAI_API_KEY="Your ChatGPT API key"
TAVILY_API_KEY="Your Tavily Search API key"
OPENAI_MODEL=gpt-4-1106-preview

// Import the OpenAI library
import OpenAI from "openai";

// Define an asynchronous function named `scrape` that takes a search query string as an argument
export async function scrape(query: string) {
  // Send a POST request to the specified API endpoint with the search query and other parameters
  const response = await fetch("https://api.tavily.com/search", {
    method: "POST", // HTTP method
    headers: {
      "Content-Type": "application/json", // Specify the request content type as JSON
    },
    body: JSON.stringify({
      api_key: process.env.TAVILY_API_KEY, // API key from environment variables
      query, // The search query passed to the function
      search_depth: "basic", // Search depth parameter
      include_answer: true, // Include the answer in the response
      include_images: false, // Do not include images in the response
      include_raw_content: false, // Do not include raw content in the response
      max_results: 20, // Limit the number of results to 20
    }),
  });

  // Parse the JSON response from the API
  const responseJson = await response.json();

  // Instantiate the OpenAI class
  const openai = new OpenAI();

  // Use the OpenAI API to create a completion based on the JSON response
  const completion = await openai.chat.completions.create({
    messages: [
      {
        role: "system", // Set the role of the message to system
        content: `Summarize the following JSON to answer the research query \`"${query}"\`: ${JSON.stringify(
          responseJson
        )} in plain English.`, // Provide the JSON response to be summarized
      },
    ],
    model: process.env.OPENAI_MODEL || "gpt-4", // Specify the OpenAI model, defaulting to GPT-4 if not set in environment variables
  });

  // Return the content of the first message choice from the completion response
  return completion.choices[0].message.content;
}

// Import necessary modules and functions

import { CopilotRuntime, OpenAIAdapter } from "@copilotkit/backend";

import { Action } from "@copilotkit/shared";

import { scrape } from "./tavily"; // Import the previously defined scrape function

// Define a scraping action with its name, description, parameters, and handler function

const scrapingAction: Action<any> = {

  name: "scrapeContent", // Name of the action

  description: "Call this function to scrape content from a url in a query.", // Description of the action

  parameters: [

    {

      name: "query", // Name of the parameter

      type: "string", // Type of the parameter

      description:

        "The query for scraping content. 5 characters or longer. Might be multiple words", // Description of the parameter

    },

  ],

  // Handler function to execute when the action is called

  handler: async ({ query }) => {

    console.log("Scraping query: ", query); // Log the query to the console

    const result = await scrape(query); // Call the scrape function with the query and await the result

    console.log("Scraping result: ", result); // Log the result to the console

    return result; // Return the result

  },

};

// Define an asynchronous POST function to handle POST requests

export async function POST(req: Request): Promise<Response> {

  const actions: Action<any>[] = []; // Initialize an empty array to store actions

  // Check if the TAVILY_API_KEY environment variable is set

  if (process.env["TAVILY_API_KEY"]) {

    actions.push(scrapingAction); // Add the scraping action to the actions array

  }

  // Create a new instance of CopilotRuntime with the defined actions

  const copilotKit = new CopilotRuntime({

    actions: actions,

  });

const openaiModel = process.env["OPENAI_MODEL"]; // Get the OpenAI model from environment variables

// Return the response from CopilotKit, using the OpenAIAdapter with the specified model

  return copilotKit.response(req, new OpenAIAdapter({ model: openaiModel }));

}