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from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from scripts.s3 import download_model_from_s3
from router.disaster import router as disaster_router
from router.sentiment import router as sentiment_router
from router.image_clf import router as image_router
from utils.logger import logger
app = FastAPI(
title="ML API",
description="ML API for sentiment analysis and image classification",
version="0.0.1",
openapi_url="/openapi.json"
)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
MODEL_PATH = "ml-models/"
sentiment_model_path = "tinybert-sentiment-analysis/"
disaster_model_path = "tinybert-disaster-tweet/"
image_model_path = "vit-human-pose-classification/"
logger.info("Ensuring models are downloaded...")
download_model_from_s3(MODEL_PATH + sentiment_model_path, sentiment_model_path)
download_model_from_s3(MODEL_PATH + disaster_model_path, disaster_model_path)
download_model_from_s3(MODEL_PATH + image_model_path, image_model_path)
logger.info("All models are ready.")
@app.get("/")
def read_root():
return {"Status": "Running"}
app.include_router(disaster_router, prefix="/api/v1", tags=["Disaster"])
app.include_router(sentiment_router, prefix="/api/v1", tags=["Sentiment"])
app.include_router(image_router, prefix="/api/v1", tags=["Image"]) |