Split Payments

import { HandlerContext } from "@xmtp/message-kit";
import { vision, textGeneration } from "../lib/openai.js";
 
export async function handler(context: HandlerContext) {
  if (!process?.env?.OPEN_AI_API_KEY) {
    console.log("No OPEN_AI_API_KEY found in .env");
    return;
  }
  const {
    members,
    commands,
    message: {
      typeId,
      content: { attachment },
      sender,
    },
  } = context;
 
  if (attachment && typeId === "remoteStaticAttachment") {
    const { data, filename, mimeType } = attachment;
    const response = await vision(
      data,
      "This image is the bill of a restaurant dinner. Return the total. If you can't find the total, return 'undefined'.",
    );
    if (response?.includes("undefined")) {
      return;
    } else {
      context.reply(
        "You uploaded a new bill. Let's go ahead and split the bill.",
      );
    }
    if (response) {
      const prompt = `You a split wise agent that splits the bill between the members of this group except for the sender and bot.\n
      These are the users of the group: ${JSON.stringify(members?.map((member) => ({ ...member, username: `@${member.username}` })))}\n
      This group app has many commands available: ${JSON.stringify(commands)}\n
 
 
      ## Instructions:
      When you receive the totals you should split the bill between the members of the group and send to each one a transaction frame
      - For the sake of this demo. Only send the payment to the sender not to all the other members.
 
      ### Return message
      Don't use markdown. Return messages in a json object The first message detailing the split. The second one you will send the command for the receiver to pay directly to the sender.
      Example:
      [
        "This are the details: Total: $49.52. Tip (20%): $9.90",
        "All users owe X USDC to @${sender?.username}. Pay here:",
        "/send @${sender?.username} $9.90"
      ]
      `;
 
      //I want the reply to be an array of messages so the bot feels like is sending multuple ones
      const { reply } = await textGeneration(response, prompt);
      let splitMessages = JSON.parse(reply);
      for (const message of splitMessages) {
        let msg = message as string;
        if (msg.startsWith("/")) await context.intent(msg);
        else await context.reply(msg);
      }
    }
  }
}

yarn add openai

import dotenv from "dotenv";
dotenv.config();
 
import OpenAI from "openai";
const openai = new OpenAI({
  apiKey: process.env.OPEN_AI_API_KEY,
});
 
export async function textGeneration(
  userPrompt: string,
  systemPrompt: string,
  chatHistory?: any[],
) {
  let messages = chatHistory ? [...chatHistory] : []; // Start with existing chat history
  if (messages.length === 0) {
    messages.push({
      role: "system",
      content: systemPrompt,
    });
  }
  messages.push({
    role: "user",
    content: userPrompt,
  });
  try {
    const response = await openai.chat.completions.create({
      model: "gpt-4o",
      messages: messages as any,
    });
    const reply = response.choices[0].message.content;
    messages.push({
      role: "assistant",
      content: reply || "No response from OpenAI.",
    });
    const cleanedReply = reply
      ?.replace(/(\*\*|__)(.*?)\1/g, "$2")
      ?.replace(/\[([^\]]+)\]\(([^)]+)\)/g, "$2")
      ?.replace(/^#+\s*(.*)$/gm, "$1")
      ?.replace(/`([^`]+)`/g, "$1")
      ?.replace(/^`|`$/g, "");
 
    return { reply: cleanedReply as string, history: messages };
  } catch (error) {
    console.error("Failed to fetch from OpenAI:", error);
    throw error;
  }
}
 
// New method to interpret an image
export async function vision(imageData: Uint8Array, systemPrompt: string) {
  const base64Image = Buffer.from(imageData).toString("base64");
  const dataUrl = `data:image/jpeg;base64,${base64Image}`;
 
  // Create a new thread for each vision request
  const visionMessages = [
    {
      role: "system",
      content: systemPrompt,
    },
    {
      role: "user",
      content: [
        { type: "text", text: systemPrompt },
        {
          type: "image_url",
          image_url: {
            url: dataUrl,
          },
        },
      ],
    },
  ];
 
  try {
    const response = await openai.chat.completions.create({
      model: "gpt-4o",
      messages: visionMessages as any,
    });
    return response.choices[0].message.content;
  } catch (error) {
    console.error("Failed to interpret image with OpenAI:", error);
    throw error;
  }
}