REST API for predictions in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the Flask library needed to create a REST API.

TensorFlow

from flask import [1]

Drag options to blanks, or click blank then click option'

Arequests

BTensorFlow

Cnumpy

DFlask

Attempts:

3 left

💡 Hint

Common Mistakes

Importing TensorFlow instead of Flask

Using lowercase 'flask' instead of 'Flask'

Importing unrelated libraries like numpy or requests

The Flask class is imported from the flask package to create the web app.

2fill in blank

medium

Complete the code to load a saved TensorFlow model from the 'model' folder.

TensorFlow

model = tf.keras.models.[1]('model')

Drag options to blanks, or click blank then click option'

Aload_model

Bcompile

Cfit

Dsave

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'save' instead of 'load_model'

Trying to compile or fit before loading

Calling a non-existent function

Use load_model to load a saved TensorFlow model from disk.

3fill in blank

hard

Fix the error in the prediction endpoint to correctly parse JSON input and make a prediction.

TensorFlow

data = request.[1].get('input')
prediction = model.predict([data])

Drag options to blanks, or click blank then click option'

Ajson

Bargs

Cform

Dvalues

Attempts:

3 left

💡 Hint

Common Mistakes

Using request.args which is for URL parameters

Using request.form which is for form data

Trying to access request.values which combines args and form

Use request.json to access JSON data sent in the POST request.

4fill in blank

hard

Fill both blanks to create a Flask route that accepts POST requests at '/predict'.

TensorFlow

@app.route('/predict', methods=[[1]])
def predict():
    pass

Drag options to blanks, or click blank then click option'

A'PUT'

B'GET'

C'POST'

D'DELETE'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'GET' which is for fetching data without body

Using 'PUT' or 'DELETE' which are for other operations

The route must accept POST requests to receive data for prediction.

5fill in blank

hard

Fill all three blanks to return the prediction result as JSON with key 'result'.

TensorFlow

from flask import jsonify

@app.route('/predict', methods=['POST'])
def predict():
    data = request.json.get('input')
    prediction = model.predict([data])
    return [1]([2]=[3])

Drag options to blanks, or click blank then click option'

Ajsonify

Bresult

Cprediction

Ddict

Attempts:

3 left

💡 Hint

Common Mistakes

Returning a plain dict without jsonify

Using wrong key names

Returning the prediction directly without JSON

Use jsonify to return JSON with key 'result' and value prediction.

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