How to Create Custom Machine Learning Models in FlutterFlow

FlutterFlow cannot: train ML models, fine-tune neural networks, evaluate model performance, or run Python-based ML libraries. These operations require Python, significant compute resources, and specialized frameworks. FlutterFlow CAN: send image or text data to a Cloud ML API and display the result, run a pre-compiled TensorFlow Lite model on the device for real-time inference, call any custom REST API endpoint (including your own model server) and parse the response, and display ML results in any widget (Text, Image, chart, etc.). Before choosing an implementation path, decide whether your use case requires real-time on-device inference (privacy-sensitive data, offline use) or can tolerate cloud API latency (0.5-3 seconds per call).

Open FlutterFlow → left sidebar → API Calls (the antenna icon). Tap '+' to create a new API call. Name it 'analyzeImage'. Set method to POST. Set the URL to the Cloud ML API endpoint — for Google Vision AI: https://vision.googleapis.com/v1/images:annotate?key=YOUR_API_KEY. Set the request body to JSON with the base64-encoded image and the requested feature types (LABEL_DETECTION, TEXT_DETECTION, OBJECT_LOCALIZATION). Tap 'Test API Call' with a sample image to verify the response structure. Then add the API call to a Button's Action Flow: first, take a photo with the Image Picker action, then call analyzeImage with the selected image as a base64 string. Parse the JSON response and bind the results to Text widgets or a list.

1// API Manager request body for Google Vision AI
2// Use in FlutterFlow API Manager → Body → JSON
3{
4  "requests": [
5    {
6      "image": {
7        "content": "[BASE64_IMAGE_STRING]"
8      },
9      "features": [
10        {
11          "type": "LABEL_DETECTION",
12          "maxResults": 10
13        },
14        {
15          "type": "TEXT_DETECTION"
16        }
17      ]
18    }
19  ]
20}

On-device inference with TFLite requires: a trained and converted .tflite model file, the tflite_flutter Flutter package, and a Custom Action that loads the model and runs inference. Add 'tflite_flutter: ^0.10.4' to pubspec.yaml in the Custom Code panel and tap Get Packages. Add your .tflite model file to the project assets by exporting the FlutterFlow project code (Pro plan), placing the file in the assets/ folder, and registering it in pubspec.yaml under flutter.assets. Create a Custom Action named 'runImageClassification' that loads the interpreter from assets, preprocesses the input image, runs interpreter.run(), and returns the top classification result as a String.

1import 'package:tflite_flutter/tflite_flutter.dart';
2import 'package:image/image.dart' as img;
3import 'dart:io';
4import 'dart:typed_data';
5
6// Custom Action: runImageClassification
7// Parameters: imagePath (String — local file path from Image Picker)
8// Returns: String (top classification label)
9Future<String> runImageClassification(String imagePath) async {
10  try {
11    // Load interpreter from app assets
12    final interpreter =
13        await Interpreter.fromAsset('assets/models/classifier.tflite');
14    debugPrint('runImageClassification: interpreter loaded');
15
16    // Preprocess the image
17    final imageFile = File(imagePath);
18    final rawImage = img.decodeImage(imageFile.readAsBytesSync());
19    if (rawImage == null) return 'Error: could not decode image';
20
21    final resized = img.copyResize(rawImage, width: 224, height: 224);
22    final input = Float32List(1 * 224 * 224 * 3);
23    int pixelIndex = 0;
24    for (int y = 0; y < 224; y++) {
25      for (int x = 0; x < 224; x++) {
26        final pixel = resized.getPixel(x, y);
27        input[pixelIndex++] = img.getRed(pixel) / 255.0;
28        input[pixelIndex++] = img.getGreen(pixel) / 255.0;
29        input[pixelIndex++] = img.getBlue(pixel) / 255.0;
30      }
31    }
32    final inputTensor = input.reshape([1, 224, 224, 3]);
33
34    // Run inference
35    final output = List.filled(1 * 1000, 0.0).reshape([1, 1000]);
36    interpreter.run(inputTensor, output);
37
38    // Find top result
39    final scores = (output[0] as List).cast<double>();
40    final maxIndex =
41        scores.indexOf(scores.reduce((a, b) => a > b ? a : b));
42    debugPrint('runImageClassification: top class index $maxIndex');
43    interpreter.close();
44    return 'Class $maxIndex (${(scores[maxIndex] * 100).toStringAsFixed(1)}%)';
45  } catch (e) {
46    debugPrint('runImageClassification error: $e');
47    return 'Error: $e';
48  }
49}

If you have trained your own ML model and deployed it as a REST API (using FastAPI, Flask, TensorFlow Serving, or a hosted service like Hugging Face Inference API, Replicate, or Render), FlutterFlow's API Manager can call it just like any other REST endpoint. In FlutterFlow's API Manager, create a new API call. Set the URL to your model server endpoint (e.g., https://your-model-server.com/predict). Set the request body structure to match what your model server expects (typically a JSON object with an 'input' field). Test the call in the API Manager. Wire it to a button Action Flow: capture user input (image, text, or structured data), call the API, and display the result. This path gives you full control over the model without any mobile-side ML code.

After your ML API call or Custom Action returns results, you need to store and display them. For Cloud API results (JSON response), parse the fields using FlutterFlow's JSON path extraction in the API response binding. Store the top result in App State (e.g., 'mlResult' as String). For TFLite Custom Action results (returned as String), store the output directly in App State. Add a Results section to your UI: a Text widget bound to the mlResult App State variable showing the top prediction, a CircularProgressIndicator visible while the analysis is running (use an 'isAnalyzing' Boolean App State variable), and an error message visible when the result contains 'Error:'. For multi-label results (like image classification with 10 labels), use a ListView bound to a List App State variable.