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from fastapi import FastAPI
from pydantic import BaseModel
from transformers import pipeline
import os
os.environ["HF_HOME"] = "/tmp"
spam = pipeline("text-classification", model="valurank/distilroberta-spam-comments-detection")
toxic = pipeline("text-classification", model="s-nlp/roberta_toxicity_classifier")
sentiment = pipeline("text-classification", model = "nlptown/bert-base-multilingual-uncased-sentiment")
nsfw = pipeline("text-classification", model = "michellejieli/NSFW_text_classifier")
app = FastAPI()
@app.get("/")
def root():
return {"status": "ok"}
class Query(BaseModel):
text: str
@app.post("/spam")
def predict_spam(query: Query):
result = spam(query.text)[0]
return {"label": result["label"], "score": result["score"]}
@app.post("/toxic")
def predict_toxic(query: Query):
result = toxic(query.text)[0]
return {"label": result["label"], "score": result["score"]}
@app.post("/sentiment")
def predict_sentiment(query: Query):
result = sentiment(query.text)[0]
return {"label": result["label"], "score": result["score"]}
@app.post("/nsfw")
def predict_nsfw(query: Query):
result = nsfw(query.text)[0]
return {"label": result["label"], "score": result["score"]}
@app.get("/health")
def health_check():
status = {
"server": "running",
"models": {}
}
models = {
"spam": ("valurank/distilroberta-spam-comments-detection", spam),
"toxic": ("s-nlp/roberta_toxicity_classifier", toxic),
"sentiment": ("nlptown/bert-base-multilingual-uncased-sentiment", sentiment),
"nsfw": ("michellejieli/NSFW_text_classifier", nsfw),
}
for key, (model_name, model_pipeline) in models.items():
try:
model_pipeline("test")
status["models"][key] = {
"model_name": model_name,
"status": "running"
}
except Exception as e:
status["models"][key] = {
"model_name": model_name,
"status": f"error: {str(e)}"
}
return status
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