// src/scripts/image.ts
import { config } from 'dotenv';
config();
import { HumanMessage, AIMessage, BaseMessage } from '@langchain/core/messages';
import type { RunnableConfig } from '@langchain/core/runnables';
import type * as t from '@/types';
import { ChatModelStreamHandler, createContentAggregator } from '@/stream';
import {
  ToolEndHandler,
  ModelEndHandler,
  createMetadataAggregator,
} from '@/events';
// @ts-expect-error — example module not in current codebase
import { fetchRandomImageTool, fetchRandomImageURL } from '@/tools/example';
import { getLLMConfig } from '@/utils/llmConfig';
import { getArgs } from '@/scripts/args';
import { GraphEvents } from '@/common';
import { Run } from '@/run';

const conversationHistory: BaseMessage[] = [];

async function testCodeExecution(): Promise<void> {
  const { userName, location, provider, currentDate } = await getArgs();
  const { contentParts, aggregateContent } = createContentAggregator();
  const customHandlers = {
    [GraphEvents.TOOL_END]: new ToolEndHandler(),
    [GraphEvents.CHAT_MODEL_END]: new ModelEndHandler(),
    [GraphEvents.CHAT_MODEL_STREAM]: new ChatModelStreamHandler(),
    [GraphEvents.ON_RUN_STEP_COMPLETED]: {
      handle: (
        event: GraphEvents.ON_RUN_STEP_COMPLETED,
        data: t.StreamEventData
      ): void => {
        console.log('====== ON_RUN_STEP_COMPLETED ======');
        console.dir(data, { depth: null });
        aggregateContent({
          event,
          data: data as unknown as { result: t.ToolEndEvent },
        });
      },
    },
    [GraphEvents.ON_RUN_STEP]: {
      handle: (
        event: GraphEvents.ON_RUN_STEP,
        data: t.StreamEventData
      ): void => {
        console.log('====== ON_RUN_STEP ======');
        console.dir(data, { depth: null });
        aggregateContent({ event, data: data as t.RunStep });
      },
    },
    [GraphEvents.ON_RUN_STEP_DELTA]: {
      handle: (
        event: GraphEvents.ON_RUN_STEP_DELTA,
        data: t.StreamEventData
      ): void => {
        console.log('====== ON_RUN_STEP_DELTA ======');
        console.dir(data, { depth: null });
        aggregateContent({ event, data: data as t.RunStepDeltaEvent });
      },
    },
    [GraphEvents.ON_MESSAGE_DELTA]: {
      handle: (
        event: GraphEvents.ON_MESSAGE_DELTA,
        data: t.StreamEventData
      ): void => {
        console.log('====== ON_MESSAGE_DELTA ======');
        console.dir(data, { depth: null });
        aggregateContent({ event, data: data as t.MessageDeltaEvent });
      },
    },
    [GraphEvents.TOOL_START]: {
      handle: (
        _event: string,
        data: t.StreamEventData,
        metadata?: Record<string, unknown>
      ): void => {
        console.log('====== TOOL_START ======');
        console.dir(data, { depth: null });
      },
    },
  };

  const llmConfig = getLLMConfig(provider);

  const run = await Run.create<t.IState>({
    runId: 'message-num-1',
    graphConfig: {
      type: 'standard',
      llmConfig,
      tools: [fetchRandomImageTool],
      // tools: [fetchRandomImageURL],
      instructions:
        'You are a friendly AI assistant with internet capabilities. Always address the user by their name.',
      additional_instructions: `The user's name is ${userName} and they are located in ${location}.`,
    },
    returnContent: true,
    skipCleanup: true,
    customHandlers,
  });

  const config: Partial<RunnableConfig> & {
    version: 'v1' | 'v2';
    run_id?: string;
    streamMode: string;
  } = {
    configurable: {
      provider,
      thread_id: 'conversation-num-1',
    },
    streamMode: 'values',
    version: 'v2' as const,
  };

  console.log('Fetch Random Image');

  const userMessage1 = `Hi ${userName} here. Please get me 2 random images. Describe them after you receive them.`;

  conversationHistory.push(new HumanMessage(userMessage1));

  let inputs = {
    messages: conversationHistory,
  };
  const finalContentParts1 = await run.processStream(inputs, config);
  const finalMessages1 = run.getRunMessages();
  if (finalMessages1) {
    conversationHistory.push(...finalMessages1);
  }
  console.log('\n\n====================\n\n');
  console.dir(contentParts, { depth: null });

  console.log('Test 2: Follow up with another message');

  const userMessage2 = `thanks, you're the best!`;

  conversationHistory.push(new HumanMessage(userMessage2));

  inputs = {
    messages: conversationHistory,
  };
  const finalContentParts2 = await run.processStream(inputs, config, {
    keepContent: true,
  });
  const finalMessages2 = run.getRunMessages();
  if (finalMessages2) {
    conversationHistory.push(...finalMessages2);
  }
  console.log('\n\n====================\n\n');
  console.dir(contentParts, { depth: null });

  const { handleLLMEnd, collected } = createMetadataAggregator();
  const titleResult = await run.generateTitle({
    provider,
    inputText: userMessage2,
    contentParts,
    chainOptions: {
      callbacks: [
        {
          handleLLMEnd,
        },
      ],
    },
  });
  console.log('Generated Title:', titleResult);
  console.log('Collected metadata:', collected);
}

process.on('unhandledRejection', (reason, promise) => {
  console.error('Unhandled Rejection at:', promise, 'reason:', reason);
  console.log('Conversation history:');
  console.dir(conversationHistory, { depth: null });
  process.exit(1);
});

testCodeExecution().catch((err) => {
  console.error(err);
  console.log('Conversation history:');
  console.dir(conversationHistory, { depth: null });
  process.exit(1);
});