REST API for predictions in TensorFlow - ML Experiment: Train & Evaluate

Experiment - REST API for predictions

Problem:You have a trained TensorFlow model for image classification. You want to create a REST API so users can send images and get predictions.

Current Metrics:Model accuracy on test data: 92%. No API implemented yet.

Issue:No way to serve the model for real-time predictions via a web API.

Your Task

Build a REST API using Flask that accepts image files, preprocesses them, runs the TensorFlow model to predict the class, and returns the prediction as JSON.

Use Flask for the API server.

Load the TensorFlow model once at startup.

Accept images via POST requests.

Return prediction label and confidence score.

Hint 1

Hint 2

Hint 3

Hint 4

Solution

TensorFlow

from flask import Flask, request, jsonify
import tensorflow as tf
from PIL import Image
import numpy as np
import io

app = Flask(__name__)

# Load the trained TensorFlow model once
model = tf.keras.models.load_model('my_model.h5')

# Define class labels
class_labels = ['cat', 'dog', 'bird']

# Image preprocessing function
def preprocess_image(image_bytes):
    image = Image.open(io.BytesIO(image_bytes)).convert('RGB')
    image = image.resize((224, 224))  # assuming model input size
    image_array = np.array(image) / 255.0  # normalize
    image_array = np.expand_dims(image_array, axis=0)  # batch dimension
    return image_array

@app.route('/predict', methods=['POST'])
def predict():
    if 'file' not in request.files:
        return jsonify({'error': 'No file part'}), 400
    file = request.files['file']
    if file.filename == '':
        return jsonify({'error': 'No selected file'}), 400
    try:
        img_bytes = file.read()
        input_data = preprocess_image(img_bytes)
        predictions = model.predict(input_data)
        predicted_index = np.argmax(predictions[0])
        confidence = float(predictions[0][predicted_index])
        label = class_labels[predicted_index]
        return jsonify({'label': label, 'confidence': confidence})
    except Exception as e:
        return jsonify({'error': str(e)}), 500

if __name__ == '__main__':
    app.run(debug=True, host='0.0.0.0', port=5000)

Added Flask app to serve REST API.

Loaded TensorFlow model once at startup.

Created endpoint '/predict' to accept POST requests with image files.

Preprocessed images to 224x224 RGB normalized arrays.

Returned JSON with predicted class label and confidence score.

Results Interpretation

Before: Model only runs locally with 92% accuracy, no API available.

After: Model served via REST API, accepts images, returns predictions with confidence, enabling real-time use.

Serving a TensorFlow model with a REST API allows real-time predictions accessible by other applications or users, bridging model training and practical deployment.

Bonus Experiment

Add support for batch predictions by accepting multiple images in one request and returning predictions for all.

💡 Hint

Modify the API to accept multiple files, preprocess each, stack into a batch array, and return a list of predictions.

Practice

1. What is the main purpose of creating a REST API for a TensorFlow model?

easy

A. To save the model in a different file format

B. To train the model faster on a server

C. To visualize the model's architecture

D. To allow other programs to send data and get predictions easily

Solution

1. Step 1: Understand REST API role

   A REST API acts as a bridge to send data and receive results from a model.

2. Step 2: Match purpose with options

   Only To allow other programs to send data and get predictions easily describes this role correctly as it enables easy data exchange for predictions.

3. Final Answer:

   To allow other programs to send data and get predictions easily -> Option D

4. Quick Check:

   REST API purpose = Easy data exchange for predictions [OK]

Hint: REST API = send input, get output from model [OK]

Common Mistakes:

• Confusing API with training process
• Thinking API is for visualization
• Assuming API changes model files

2. Which Flask code snippet correctly defines a POST endpoint to receive JSON input for predictions?

easy

A. app.post('/predict')\ndef predict():\n data = request.get_json()

B. app.route('/predict')\ndef predict():\n data = request.get_json()

C. app.route('/predict', methods=['POST'])\ndef predict():\n data = request.get_json()

D. app.get('/predict')\ndef predict():\n data = request.get_json()

Solution

1. Step 1: Identify correct Flask route syntax for POST

   Flask uses @app.route with methods=['POST'] to define POST endpoints.

2. Step 2: Check code snippets

   Only app.route('/predict', methods=['POST'])\ndef predict():\n data = request.get_json() uses @app.route with methods=['POST'] and calls request.get_json() correctly.

3. Final Answer:

   app.route('/predict', methods=['POST'])\ndef predict():\n data = request.get_json() -> Option C

4. Quick Check:

   Flask POST route = @app.route with methods=['POST'] [OK]

Hint: Flask POST = @app.route with methods=['POST'] [OK]

Common Mistakes:

• Using app.post decorator (not standard Flask)
• Missing methods=['POST'] in route
• Using app.get for POST requests

3. Given this Flask code snippet, what will be the output if the input JSON is {'input': [1, 2, 3]} and the model predicts sum of inputs?

from flask import Flask, request, jsonify
app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    inputs = data['input']
    prediction = sum(inputs)
    return jsonify({'prediction': prediction})

medium

A. {"prediction": 6}

B. {"prediction": [1, 2, 3]}

C. {"prediction": 123}

D. Error: KeyError

Solution

1. Step 1: Extract input and compute prediction

   The input JSON has 'input': [1, 2, 3]. The code sums these values: 1+2+3=6.

2. Step 2: Return prediction as JSON

   The code returns jsonify({'prediction': 6}), so output is {"prediction": 6}.

3. Final Answer:

   {"prediction": 6} -> Option A

4. Quick Check:

   Sum([1,2,3]) = 6 [OK]

Hint: Sum input list to get prediction output [OK]

Common Mistakes:

• Returning input list instead of sum
• Concatenating numbers as string
• Misreading JSON keys causing KeyError

4. You wrote this Flask API code but get an error when sending POST requests. What is the likely cause?

from flask import Flask, request, jsonify
app = Flask(__name__)

@app.route('/predict')
def predict():
    data = request.get_json()
    prediction = model.predict(data['input'])
    return jsonify({'prediction': prediction.tolist()})

medium

A. model.predict() cannot accept JSON data

B. Missing methods=['POST'] in @app.route decorator

C. request.get_json() only works with GET requests

D. jsonify() cannot convert lists

Solution

1. Step 1: Check route decorator for HTTP methods

   The route lacks methods=['POST'], so it defaults to GET only, causing errors on POST requests.

2. Step 2: Confirm other parts are correct

   model.predict() can accept arrays, get_json() works with POST, and jsonify converts lists fine.

3. Final Answer:

   Missing methods=['POST'] in @app.route decorator -> Option B

4. Quick Check:

   POST route needs methods=['POST'] [OK]

Hint: Add methods=['POST'] to route for POST requests [OK]

Common Mistakes:

• Forgetting to specify POST method in route
• Assuming get_json works only with GET
• Thinking jsonify can't handle lists

5. You want to deploy a TensorFlow model with a REST API that accepts multiple inputs and returns predictions for each. Which approach is best to handle batch inputs in your Flask API?

hard

A. Accept a JSON list of inputs, convert to numpy array, predict batch, return list of predictions

B. Accept one input at a time, call model.predict repeatedly for each

C. Save each input to a file, then load and predict later

D. Use GET requests with inputs in URL parameters

Solution

1. Step 1: Understand batch prediction needs

   Batch prediction means processing many inputs at once for efficiency.

2. Step 2: Choose method to handle batch inputs

   Accept a JSON list of inputs, convert to numpy array, predict batch, return list of predictions accepts JSON list, converts to array, predicts all at once, then returns all predictions, which is efficient and standard.

3. Final Answer:

   Accept a JSON list of inputs, convert to numpy array, predict batch, return list of predictions -> Option A

4. Quick Check:

   Batch input = JSON list + batch predict [OK]

Hint: Batch predict by sending list and returning list [OK]

Common Mistakes:

• Predicting one input at a time (slow)
• Using files instead of direct JSON
• Using GET for large input data