Commit
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ef66f82
1
Parent(s):
0a9d367
Update main.py
Browse files
main.py
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import re
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import string
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import nltk
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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from typing import Optional
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from transformers import pipeline
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from pyngrok import ngrok
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import nest_asyncio
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from fastapi.responses import RedirectResponse
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# Download NLTK resources
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nltk.download('punkt')
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nltk.download('wordnet')
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#
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app = FastAPI()
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# Text preprocessing functions
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def remove_urls(text):
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return re.sub(r'http[s]?://\S+', '', text)
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def remove_punctuation(text):
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regular_punct = string.punctuation
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return re.sub(r'['+regular_punct+']', '', text)
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def lower_case(text):
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return text.lower()
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def lemmatize(text):
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wordnet_lemmatizer =
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tokens = nltk.word_tokenize(text)
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#
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class TextInput(BaseModel):
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text: str
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#
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@app.get('/')
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async def welcome():
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# Redirect to the Swagger UI page
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return RedirectResponse(url="/docs")
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#
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@app.post('/analyze/')
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async def
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# Perform sentiment analysis
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try:
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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# Run the FastAPI app using Uvicorn
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if __name__ == "__main__":
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# Create ngrok tunnel
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ngrok_tunnel = ngrok.connect(7860)
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print('Public URL:', ngrok_tunnel.public_url)
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# Allow nested asyncio calls
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nest_asyncio.apply()
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# Run the FastAPI app with Uvicorn
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import uvicorn
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uvicorn.run(app, port=7860)
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from contextlib import asynccontextmanager
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel, ValidationError
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from fastapi.encoders import jsonable_encoder
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# TEXT PREPROCESSING
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# --------------------------------------------------------------------
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import re
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import string
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import nltk
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nltk.download('punkt')
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nltk.download('wordnet')
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nltk.download('omw-1.4')
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from nltk.stem import WordNetLemmatizer
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# Function to remove URLs from text
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def remove_urls(text):
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return re.sub(r'http[s]?://\S+', '', text)
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# Function to remove punctuations from text
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def remove_punctuation(text):
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regular_punct = string.punctuation
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return str(re.sub(r'['+regular_punct+']', '', str(text)))
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# Function to convert the text into lower case
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def lower_case(text):
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return text.lower()
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# Function to lemmatize text
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def lemmatize(text):
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wordnet_lemmatizer = WordNetLemmatizer()
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tokens = nltk.word_tokenize(text)
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lemma_txt = ''
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for w in tokens:
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lemma_txt = lemma_txt + wordnet_lemmatizer.lemmatize(w) + ' '
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return lemma_txt
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def preprocess_text(text):
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# Preprocess the input text
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text = remove_urls(text)
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text = remove_punctuation(text)
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text = lower_case(text)
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text = lemmatize(text)
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return text
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# Load the model using FastAPI lifespan event so that teh model is loaded at the beginning for efficiency
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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# Load the model from HuggingFace transformers library
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from transformers import pipeline
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global sentiment_task
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sentiment_task = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment-latest", tokenizer="cardiffnlp/twitter-roberta-base-sentiment-latest")
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yield
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# Clean up the model and release the resources
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del sentiment_task
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description = """
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## Text Classification API
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This app shows the sentiment of the text (positive, negative, or neutral).
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Check out the docs for the `/analyze/{text}` endpoint below to try it out!
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"""
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# Initialize the FastAPI app
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app = FastAPI(lifespan=lifespan, docs_url="/", description=description)
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# Define the input data model
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class TextInput(BaseModel):
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text: str
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# Define the welcome endpoint
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@app.get('/')
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async def welcome():
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# Redirect to the Swagger UI page
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return RedirectResponse(url="/docs")
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# Validate input text length
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MAX_TEXT_LENGTH = 1000
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# Define the sentiment analysis endpoint
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@app.post('/analyze/{text}')
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async def classify_text(text_input:TextInput):
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try:
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# Convert input data to JSON serializable dictionary
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text_input_dict = jsonable_encoder(text_input)
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# Validate input data using Pydantic model
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text_data = TextInput(**text_input_dict) # Convert to Pydantic model
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# Validate input text length
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if len(text_input.text) > MAX_TEXT_LENGTH:
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raise HTTPException(status_code=400, detail="Text length exceeds maximum allowed length")
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elif len(text_input.text) == 0:
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raise HTTPException(status_code=400, detail="Text cannot be empty")
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except ValidationError as e:
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# Handle validation error
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raise HTTPException(status_code=422, detail=str(e))
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try:
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# Perform text classification
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return sentiment_task(preprocess_text(text_input.text))
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except ValueError as ve:
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# Handle value error
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raise HTTPException(status_code=400, detail=str(ve))
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except Exception as e:
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# Handle other server errors
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raise HTTPException(status_code=500, detail=str(e))
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