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# Imports
import sys
# sys.path.insert(0, '../src/')
# sys.path.insert(0, '../src')
# sys.path.insert(0, 'src/')
# sys.path.insert(0, 'src')
from typing import Union
from src.utils import preprocess
from fastapi import FastAPI
from transformers import AutoModelForSequenceClassification,AutoTokenizer, AutoConfig
import numpy as np
#convert logits to probabilities
from scipy.special import softmax
# Config
app = FastAPI()
#/docs, page to see auto-generated API documentation
#loading ML/DL components
tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
model_path = f"Junr-syl/tweet_sentiments_analysis"
config = AutoConfig.from_pretrained(model_path)
config.id2label = {0: 'NEGATIVE', 1: 'NEUTRAL', 2: 'POSITIVE'}
model = AutoModelForSequenceClassification.from_pretrained(model_path)
# Endpoints
@app.get("/")
def read_root():
"Home endpoint"
return {"greeting": "Hello World..!",
"cohort": "2",
}
@app.post("/predict")
def predict(text:str):
"prediction endpoint, classifying tweets"
text = preprocess(text)
# PyTorch-based models
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
#Process scores
ranking = np.argsort(scores)
ranking = ranking[::-1]
predicted_label = config.id2label[ranking[0]]
predicted_score = scores[ranking[0]]
return {"text":text,
"predicted_label":predicted_label,
"confidence_score":predicted_score
} |