Flask API for model serving in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this Flask API response code?

Consider a Flask API that loads a trained model and predicts the class for input data. What will be the JSON response when sending input data [5.1, 3.5, 1.4, 0.2] to this endpoint?

ML Python

from flask import Flask, request, jsonify
import numpy as np
import pickle

app = Flask(__name__)

# Load model
with open('iris_model.pkl', 'rb') as f:
    model = pickle.load(f)

@app.route('/predict', methods=['POST'])
def predict():
    data = request.json['data']
    arr = np.array(data).reshape(1, -1)
    pred = model.predict(arr)
    return jsonify({'prediction': int(pred[0])})

A {"prediction": 0}

B {"prediction": "setosa"}

C {"prediction": 1}

D {"error": "Invalid input"}

💡 Hint

The model is trained on Iris dataset where class 0 corresponds to 'setosa'. The input matches typical setosa features.

The model predicts class 0 for the given input features which correspond to Iris setosa. The API returns the class index as an integer in JSON.

❓ Model Choice

intermediate

Which model type is best suited for this Flask API serving handwritten digit recognition?

You want to serve a model via Flask API that recognizes handwritten digits (0-9) from 28x28 grayscale images. Which model type below is most appropriate for this task?

A A k-means clustering model

B A linear regression model

C A decision tree classifier

D A convolutional neural network (CNN)

💡 Hint

Consider the input type and the need to capture spatial features in images.

CNNs are designed to capture spatial patterns in images and perform well on image classification tasks like digit recognition.

❓ Hyperparameter

advanced

Which hyperparameter tuning improves Flask API model latency the most?

You deployed a deep learning model in a Flask API. The API latency is high. Which hyperparameter tuning below will most likely reduce prediction latency?

A Increase the number of layers in the model

B Reduce model size by pruning or quantization

C Increase batch size during training

D Increase learning rate during training

💡 Hint

Think about what affects model size and inference speed.

Reducing model size via pruning or quantization decreases computation needed during inference, lowering latency.

🔧 Debug

advanced

Why does this Flask API code raise a KeyError?

This Flask API code raises a KeyError when a POST request is sent. What is the cause?

ML Python

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

@app.route('/predict', methods=['POST'])
def predict():
    data = request.json['input']
    # model prediction code here
    return jsonify({'result': 'ok'})

A The route should accept GET instead of POST

B Flask app is missing debug=True

C The JSON payload does not contain the key 'input'

D The jsonify function is used incorrectly

💡 Hint

Check the JSON keys sent in the request body.

If the client sends JSON without the 'input' key, accessing request.json['input'] raises KeyError.

❓ Metrics

expert

Which metric best evaluates a Flask API model for imbalanced binary classification?

Your Flask API serves a model that predicts rare disease presence (positive class is 1% of data). Which metric below is best to evaluate model performance?

A F1-score

B Precision

C Recall

D Accuracy

💡 Hint

Consider a metric that balances false positives and false negatives in imbalanced data.

F1-score balances precision and recall, providing a better measure than accuracy alone for imbalanced classes.

Practice

1. What is the main purpose of using Flask in model serving for machine learning?

easy

A. To train machine learning models faster

B. To create a web API that allows clients to send data and get predictions

C. To visualize data in charts and graphs

D. To store large datasets efficiently

Solution

1. Step 1: Understand Flask's role

   Flask is a lightweight web framework used to build web applications and APIs.

2. Step 2: Connect Flask to model serving

   In machine learning, Flask is used to create an API endpoint where clients can send input data and receive model predictions.

3. Final Answer:

   To create a web API that allows clients to send data and get predictions -> Option B

4. Quick Check:

   Flask for API = Create prediction endpoint [OK]

Hint: Flask makes your model accessible via web requests [OK]

Common Mistakes:

• Thinking Flask trains models
• Confusing Flask with data storage tools
• Assuming Flask is for data visualization

2. Which of the following is the correct way to define a Flask route for a POST request to serve a model prediction?

easy

A. @app.route('/predict', methods=['GET'])

B. @app.route('/predict')

C. @app.route('/predict', methods=['POST'])

D. @app.route('/predict', method='POST')

Solution

1. Step 1: Recall Flask route syntax

   Flask routes use @app.route decorator with a path and a list of HTTP methods.

2. Step 2: Identify correct method for sending data

   POST method is used to send data to the server for processing, suitable for model prediction.

3. Final Answer:

   @app.route('/predict', methods=['POST']) -> Option C

4. Quick Check:

   POST method for data sending = Correct route [OK]

Hint: Use methods=['POST'] to accept data in Flask route [OK]

Common Mistakes:

• Using GET instead of POST for sending data
• Missing 'methods' keyword or using wrong syntax
• Assuming default route accepts POST

3. Given the Flask code snippet below, what will be the output when a POST request with JSON {"input": [1, 2, 3]} is sent?

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

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

medium

A. {"prediction": 6}

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

C. {"prediction": 123}

D. Error: KeyError

Solution

1. Step 1: Extract input data from JSON

   The code uses request.get_json() to get {'input': [1, 2, 3]} and accesses data['input'] which is [1, 2, 3].

2. Step 2: Calculate sum of input list

   sum([1, 2, 3]) equals 6, which is stored in result and returned as JSON.

3. Final Answer:

   {"prediction": 6} -> Option A

4. Quick Check:

   Sum of [1,2,3] = 6 [OK]

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

Common Mistakes:

• Returning the list instead of sum
• Concatenating numbers as string
• Missing JSON key causing KeyError

4. Identify the error in the following Flask API code for model serving and select the fix:

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

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

medium

A. Change request.json() to request.get_json()

B. Change methods=['POST'] to methods=['GET']

C. Remove .tolist() from prediction

D. Add @app.run() after function

Solution

1. Step 1: Check how JSON data is accessed

   request.json() is incorrect; Flask uses request.get_json() to parse JSON body.

2. Step 2: Confirm other parts are correct

   methods=['POST'] is correct for sending data; .tolist() is valid if prediction is numpy array; @app.run() is not a decorator.

3. Final Answer:

   Change request.json() to request.get_json() -> Option A

4. Quick Check:

   Use request.get_json() to parse JSON [OK]

Hint: Use request.get_json() to read JSON body [OK]

Common Mistakes:

• Using request.json() instead of get_json()
• Confusing HTTP methods for data sending
• Misusing Flask decorators

5. You want to serve a scikit-learn model using Flask. The model expects input features as a 2D list. Which approach correctly prepares the input inside the Flask route before prediction?

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    # Prepare input for model
    input_features = ???
    prediction = model.predict(input_features)
    return jsonify({'prediction': prediction.tolist()})

hard

A. input_features = data['features'].tolist() # Convert to list

B. input_features = data['features'] # Use list directly

C. input_features = data['features'][0] # Use first element only

D. input_features = [data['features']] # Wrap list inside another list

Solution

1. Step 1: Understand model input shape

   Scikit-learn models expect 2D arrays: shape (n_samples, n_features). A single sample must be wrapped in an outer list.

2. Step 2: Prepare input accordingly

   Wrapping data['features'] (a list of features) inside another list creates a 2D list suitable for prediction.

3. Final Answer:

   input_features = [data['features']] # Wrap list inside another list -> Option D

4. Quick Check:

   Wrap features in list for 2D input [OK]

Hint: Wrap single feature list in another list for 2D input [OK]

Common Mistakes:

• Passing 1D list directly causing shape errors
• Using only first element losing features
• Calling .tolist() on a list (already a list)