Delete main.py

main.py

@@ -1,100 +0,0 @@
-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))