Create main.py

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 the 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))