Bolt.new AI and TensorFlow integration: Step-by-Step Guide 2025

 

Adding TensorFlow.js Dependency to package.json

 

• Open your Bolt.new AI project and locate the package.json file in the root directory.
• Add the TensorFlow.js dependency by inserting the following code snippet into the "dependencies" section. This ensures that TensorFlow.js is automatically installed when your project loads:

• "dependencies": {
    "@tensorflow/tfjs": "^4.0.0",
    // ... other dependencies
  }
  

• Save the changes. Bolt.new AI will use this file to manage your project's dependencies automatically.

 

Creating a TensorFlow Service File

 

• Create a new TypeScript file named tensorflowService.ts in your project. It is best to place this file in a folder named services (create the folder if it does not exist).
• Paste the following code into tensorflowService.ts. This file will handle TensorFlow operations such as loading models and performing predictions:

• import * as tf from '@tensorflow/tfjs';
  
  // Function to load a pre-trained model from a given URL
  export async function loadModel(modelUrl: string): Promise<tf.LayersModel> {
    try {
      const model = await tf.loadLayersModel(modelUrl);
      console.log('Model loaded successfully.');
      return model;
    } catch (error) {
      console.error('Error loading model:', error);
      throw error;
    }
  }
  // Function to perform prediction using the loaded model
  export async function predict(model: tf.LayersModel, inputData: number[][]): Promise<tf.Tensor> {
    const inputTensor = tf.tensor2d(inputData);
    const prediction = model.predict(inputTensor) as tf.Tensor;
    return prediction;
  }
  

  
  




• This service centralizes TensorFlow operations, making it easier to manage and reuse your TensorFlow code throughout your project.

 

Integrating TensorFlow Service into Your Main Application

 

• In your main application file (for example, index.ts or app.ts), import the functions from tensorflowService.ts to utilize TensorFlow functionalities.
• Insert the following code snippet in your main file where you handle data processing or machine learning predictions:

• import { loadModel, predict } from './services/tensorflowService';
  
  async function runTensorFlowExample() {
    // Replace with your own model URL
    const modelUrl = 'https://example.com/path/to/model.json';
    try {
      const model = await loadModel(modelUrl);
      // Replace with your input data for prediction
      const inputData = [
        [1, 2, 3, 4]
      ];
      const prediction = await predict(model, inputData);
      prediction.print();
    } catch (error) {
      console.error('TensorFlow example error:', error);
    }
  }
  // Call this function at an appropriate place in your application lifecycle.
  runTensorFlowExample();
  

  
  




• This integration loads a TensorFlow model from the specified URL and runs a prediction on sample data. Adjust the model URL and input data according to your requirements.

 

Configuring Build and Deployment Settings

 

• Since Bolt.new AI does not provide an interactive terminal, all dependency installations and build configurations are managed by editing your project files.
• Make sure your TypeScript compiler settings in tsconfig.json are properly configured. Here is an example configuration:

• {
    "compilerOptions": {
      "target": "es6",
      "module": "commonjs",
      "outDir": "./dist",
      "rootDir": "./src",
      "strict": true
      // Other configuration options as needed
    }
  }
  

• Ensure that your project structure reflects the changes—for example, placing your source TypeScript files in the designated rootDir and the TensorFlow service in the correct folder.

 

Testing Your TensorFlow Integration

 

• After saving all file changes, click the Run button in your Bolt.new AI interface.
• Open the browser console to check the logs. You should see a message indicating the model has been loaded successfully and any prediction outputs from the runTensorFlowExample function.
• Use the console output to confirm that TensorFlow integration is functioning as expected, and troubleshoot any errors using the logged messages.