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sentiment-api

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Create app.py

1de4914 verified 6 months ago

530 Bytes

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

app = FastAPI()

# Load pretrained sentiment model
classifier = pipeline("sentiment-analysis")

# Input format
class TextRequest(BaseModel):
    text: str

@app.get("/")
async def root():
    return {"message": "Sentiment API is running"}

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

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

	app = FastAPI()

	# Load pretrained sentiment model
	classifier = pipeline("sentiment-analysis")

	# Input format
	class TextRequest(BaseModel):
	text: str

	@app.get("/")
	async def root():
	return {"message": "Sentiment API is running"}

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