emanism6/Text-Classification-Fastapi

1import re2import string3import nltk4from fastapi import FastAPI, HTTPException5from pydantic import BaseModel6from typing import Optional7from transformers import pipeline8from pyngrok import ngrok9import nest_asyncio10from fastapi.responses import RedirectResponse11 12# Download NLTK resources13nltk.download('punkt')14nltk.download('wordnet')15 16# Initialize FastAPI app17app = FastAPI()18 19# Text preprocessing functions20def remove_urls(text):21    return re.sub(r'http[s]?://\S+', '', text)22 23def remove_punctuation(text):24    regular_punct = string.punctuation25    return re.sub(r'['+regular_punct+']', '', text)26 27def lower_case(text):28    return text.lower()29 30def lemmatize(text):31    wordnet_lemmatizer = nltk.WordNetLemmatizer()32    tokens = nltk.word_tokenize(text)33    return ' '.join([wordnet_lemmatizer.lemmatize(w) for w in tokens])34 35# Model loading36lyx_pipe = pipeline("text-classification", model="lxyuan/distilbert-base-multilingual-cased-sentiments-student")37 38# Input data model39class TextInput(BaseModel):40    text: str41 42# Welcome endpoint43@app.get('/')44async def welcome():45    # Redirect to the Swagger UI page46    return RedirectResponse(url="/docs")47 48# Sentiment analysis endpoint49@app.post('/analyze/')50async def Predict_Sentiment(text_input: TextInput):    51    text = text_input.text52 53    # Text preprocessing54    text = remove_urls(text)55    text = remove_punctuation(text)56    text = lower_case(text)57    text = lemmatize(text)58 59    # Perform sentiment analysis60    try:61        return lyx_pipe(text)62    except Exception as e:63        raise HTTPException(status_code=500, detail=str(e))64 65# Run the FastAPI app using Uvicorn66if __name__ == "__main__":67    # Create ngrok tunnel68    ngrok_tunnel = ngrok.connect(7860)69    print('Public URL:', ngrok_tunnel.public_url)70 71    # Allow nested asyncio calls72    nest_asyncio.apply()73 74    # Run the FastAPI app with Uvicorn75    import uvicorn76    uvicorn.run(app, port=7860)77