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sentiment-api

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Create app.py

ba335c3 verified 6 months ago

437 Bytes

from fastapi import FastAPI
from pydantic import BaseModel
from transformers import pipeline

app = FastAPI()

classifier = pipeline("sentiment-analysis")

class TextRequest(BaseModel):
    text: str

@app.post("/predict")
async def predict(request: TextRequest):
    result = classifier(request.text)[0]
    return {"label": result["label"], "score": result["score"]}

@app.get("/health")
async def health():
    return {"status": "ok"}

	from fastapi import FastAPI
	from pydantic import BaseModel
	from transformers import pipeline

	app = FastAPI()

	classifier = pipeline("sentiment-analysis")

	class TextRequest(BaseModel):
	text: str

	@app.post("/predict")
	async def predict(request: TextRequest):
	result = classifier(request.text)[0]
	return {"label": result["label"], "score": result["score"]}

	@app.get("/health")
	async def health():
	return {"status": "ok"}