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+# syntax=docker/dockerfile:1
+
+# Comments are provided throughout this file to help you get started.
+# If you need more help, visit the Dockerfile reference guide at
+# https://docs.docker.com/go/dockerfile-reference/
+
+# Want to help us make this template better? Share your feedback here: https://forms.gle/ybq9Krt8jtBL3iCk7
+
+ARG PYTHON_VERSION=3.11.9
+FROM python:${PYTHON_VERSION}-slim as base
+
+# Prevents Python from writing pyc files.
+ENV PYTHONDONTWRITEBYTECODE=1
+
+# Keeps Python from buffering stdout and stderr to avoid situations where
+# the application crashes without emitting any logs due to buffering.
+ENV PYTHONUNBUFFERED=1
+
+WORKDIR /app
+
+# Create a non-privileged user that the app will run under.
+# See https://docs.docker.com/go/dockerfile-user-best-practices/
+ARG UID=10001
+RUN adduser \
+    --disabled-password \
+    --gecos "" \
+    --home "/nonexistent" \
+    --shell "/sbin/nologin" \
+    --no-create-home \
+    --uid "${UID}" \
+    appuser
+
+# Download dependencies as a separate step to take advantage of Docker's caching.
+# Leverage a cache mount to /root/.cache/pip to speed up subsequent builds.
+# Leverage a bind mount to requirements.txt to avoid having to copy them into
+# into this layer.
+RUN --mount=type=cache,target=/root/.cache/pip \
+    --mount=type=bind,source=requirements.txt,target=requirements.txt \
+    python -m pip install -r requirements.txt
+
+# Switch to the non-privileged user to run the application.
+USER appuser
+
+# Set the TRANSFORMERS_CACHE environment variable
+ENV TRANSFORMERS_CACHE=/tmp/.cache/huggingface
+
+# Create the cache folder with appropriate permissions
+RUN mkdir -p $TRANSFORMERS_CACHE && chmod -R 777 $TRANSFORMERS_CACHE
+
+# Set NLTK data directory
+ENV NLTK_DATA=/tmp/nltk_data
+
+# Create the NLTK data directory with appropriate permissions
+RUN mkdir -p $NLTK_DATA && chmod -R 777 $NLTK_DATA
+
+# Copy the source code into the container.
+COPY . .
+
+# Expose the port that the application listens on.
+EXPOSE 8000
+
+# Run the application.
+CMD uvicorn 'main:app' --host=0.0.0.0 --port=8000

main.py

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+from contextlib import asynccontextmanager
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel, ValidationError
+from fastapi.encoders import jsonable_encoder
+
+# TEXT PREPROCESSING
+# --------------------------------------------------------------------
+import re
+import string
+import nltk
+nltk.download('punkt')
+nltk.download('wordnet')
+nltk.download('omw-1.4')
+from nltk.stem import WordNetLemmatizer
+
+# Function to remove URLs from text
+def remove_urls(text):
+    return re.sub(r'http[s]?://\S+', '', text)
+
+# Function to remove punctuations from text
+def remove_punctuation(text):
+    regular_punct = string.punctuation
+    return str(re.sub(r'['+regular_punct+']', '', str(text)))
+
+# Function to convert the text into lower case
+def lower_case(text):
+    return text.lower()
+
+# Function to lemmatize text
+def lemmatize(text):
+    wordnet_lemmatizer = WordNetLemmatizer()
+
+    tokens = nltk.word_tokenize(text)
+    lemma_txt = ''
+    for w in tokens:
+        lemma_txt = lemma_txt + wordnet_lemmatizer.lemmatize(w) + ' '
+
+    return lemma_txt
+
+def preprocess_text(text):
+    # Preprocess the input text
+    text = remove_urls(text)
+    text = remove_punctuation(text)
+    text = lower_case(text)
+    text = lemmatize(text)
+    return text
+
+# Load the model using FastAPI lifespan event so that teh model is loaded at the beginning for efficiency
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    # Load the model from HuggingFace transformers library
+    from transformers import pipeline
+    global sentiment_task
+    sentiment_task = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment-latest", tokenizer="cardiffnlp/twitter-roberta-base-sentiment-latest")
+    yield
+    # Clean up the model and release the resources
+    del sentiment_task
+
+# Initialize the FastAPI app
+app = FastAPI(lifespan=lifespan)
+
+# Define the input data model
+class TextInput(BaseModel):
+    text: str
+
+# Define the welcome endpoint
+@app.get('/')
+async def welcome():
+    return "Welcome to our Text Classification API"
+
+# Validate input text length
+MAX_TEXT_LENGTH = 1000
+
+# Define the sentiment analysis endpoint 
+@app.post('/analyze/{text}')
+async def classify_text(text_input:TextInput):    
+    try:
+        # Convert input data to JSON serializable dictionary
+        text_input_dict = jsonable_encoder(text_input)
+        # Validate input data using Pydantic model
+        text_data = TextInput(**text_input_dict)  # Convert to Pydantic model
+
+        # Validate input text length
+        if len(text_input.text) > MAX_TEXT_LENGTH:
+            raise HTTPException(status_code=400, detail="Text length exceeds maximum allowed length")
+        elif len(text_input.text) == 0:
+            raise HTTPException(status_code=400, detail="Text cannot be empty")
+    except ValidationError as e:
+        # Handle validation error
+        raise HTTPException(status_code=422, detail=str(e))
+
+    try:
+        # Perform text classification
+        return sentiment_task(preprocess_text(text_input.text))
+    except ValueError as ve:
+        # Handle value error
+        raise HTTPException(status_code=400, detail=str(ve))
+    except Exception as e:
+        # Handle other server errors
+        raise HTTPException(status_code=500, detail=str(e))

test_main.py

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+from fastapi.testclient import TestClient
+from main import app
+from main import TextInput
+from fastapi.encoders import jsonable_encoder
+
+client = TestClient(app)
+
+# Test the welcome endpoint
+def test_welcome():
+    # Test the welcome endpoint
+    response = client.get("/")
+    assert response.status_code == 200
+    assert response.json() == "Welcome to our Text Classification API"
+
+# Test the sentiment analysis endpoint for positive sentiment
+def test_positive_sentiment():
+    with client:
+        # Define the request payload 
+        # Initialize payload as a TextInput object
+        payload = TextInput(text="I love this product! It's amazing!")
+
+        # Convert TextInput object to JSON-serializable dictionary
+        payload_dict = jsonable_encoder(payload)
+        
+        # Send a POST request to the sentiment analysis endpoint
+        response = client.post("/analyze/{text}", json=payload_dict)
+
+        # Assert that the response status code is 200 OK
+        assert response.status_code == 200
+        
+        # Assert that the sentiment returned is positive
+        assert response.json()[0]['label'] == "positive"
+
+# Test the sentiment analysis endpoint for negative sentiment
+def test_negative_sentiment():
+    with client:
+        # Define the request payload 
+        # Initialize payload as a TextInput object
+        payload = TextInput(text="I'm really disappointed with this service. It's terrible.")
+
+        # Convert TextInput object to JSON-serializable dictionary
+        payload_dict = jsonable_encoder(payload)
+        
+        # Send a POST request to the sentiment analysis endpoint
+        response = client.post("/analyze/{text}", json=payload_dict)
+
+        # Assert that the response status code is 200 OK
+        assert response.status_code == 200
+        
+        # Assert that the sentiment returned is positive
+        assert response.json()[0]['label'] == "negative"
+
+# Test the sentiment analysis endpoint for neutral sentiment
+def test_neutral_sentiment():
+    with client:
+        # Define the request payload 
+        # Initialize payload as a TextInput object
+        payload = TextInput(text="This is a neutral statement.")
+
+        # Convert TextInput object to JSON-serializable dictionary
+        payload_dict = jsonable_encoder(payload)
+        
+        # Send a POST request to the sentiment analysis endpoint
+        response = client.post("/analyze/{text}", json=payload_dict)
+
+        # Assert that the response status code is 200 OK
+        assert response.status_code == 200
+        
+        # Assert that the sentiment returned is positive
+        assert response.json()[0]['label'] == "neutral"