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# app.py
from __future__ import annotations
import warnings
from typing import List, Literal, Optional, Tuple
from config import MODEL_PATH, REAL_LABEL, API_KEY
import joblib
from fastapi import FastAPI, Header, HTTPException
from helper import _combine
from schemas import PredictOut, PredictBatchIn, PredictIn, PredictBatchOut
# Suppress sklearn version warnings
warnings.filterwarnings("ignore", category=UserWarning, module="sklearn")
warnings.filterwarnings("ignore", message=".*InconsistentVersionWarning.*")
# =========================
# Load calibrated model
# (Pipeline: TF-IDF -> CalibratedClassifierCV(LinearSVC))
# =========================
# Additional specific suppression for sklearn version warnings
try:
from sklearn.exceptions import InconsistentVersionWarning
warnings.filterwarnings("ignore", category=InconsistentVersionWarning)
except ImportError:
# Fallback for older sklearn versions
pass
# Guard against double loading
if 'PIPE' not in globals():
try:
print("Loading model from:", MODEL_PATH)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
PIPE = joblib.load(MODEL_PATH)
print("Model loaded successfully")
except Exception as e:
print(f"Error loading model: {e}")
raise
# Lấy thứ tự class từ estimator cuối để map xác suất cho chắc
try:
classes = list(PIPE.named_steps["clf"].classes_)
except Exception:
classes = list(getattr(PIPE, "classes_", [0, 1])) # fallback
print(f"Model classes: {classes}")
IDX_REAL = classes.index(REAL_LABEL)
IDX_FAKE = classes.index(0)
print(f"Real index: {IDX_REAL}, Fake index: {IDX_FAKE}")
else:
print("Model already loaded, skipping reload...")
# =========================
# Core inference
# =========================
def infer_one(inp: PredictIn) -> PredictOut:
text_all = inp.text_all.strip().lower() if inp.text_all else _combine(inp.title, inp.text)
# Suppress warnings during prediction
with warnings.catch_warnings():
warnings.simplefilter("ignore")
probs = PIPE.predict_proba([text_all])[0]
prob_real = float(probs[IDX_REAL])
prob_fake = float(probs[IDX_FAKE])
label = "real" if prob_real >= 0.5 else "fake"
return PredictOut(
label=label,
prob_real=prob_real,
prob_fake=prob_fake,
)
def infer_batch(items: List[PredictIn]) -> List[PredictOut]:
return [infer_one(x) for x in items]
# =========================
# FastAPI endpoints
# =========================
app = FastAPI(
title="SVM Fake/Real News Classifier",
description="API for classifying news as real or fake using SVM with TF-IDF features",
version="1.0.0"
)
@app.get("/")
def root():
return {
"message": "SVM Fake/Real News Classifier API",
"endpoints": {
"predict": "/predict",
"predict_batch": "/predict_batch",
"health": "/health"
},
"model_info": {
"classes": ["fake", "real"],
"model_path": MODEL_PATH,
"calibrated": True
}
}
@app.get("/health")
def health_check():
return {"status": "healthy", "model_loaded": 'PIPE' in globals()}
@app.post("/predict", response_model=PredictOut)
def predict(payload: PredictIn, x_api_key: str = Header(default="")):
if x_api_key != API_KEY:
raise HTTPException(status_code=401, detail="Unauthorized")
return infer_one(payload)
@app.post("/predict_batch", response_model=PredictBatchOut)
def predict_batch(payload: PredictBatchIn, x_api_key: str = Header(default="")):
if x_api_key != API_KEY:
raise HTTPException(status_code=401, detail="Unauthorized")
return PredictBatchOut(results=infer_batch(payload.items))
if __name__ == "__main__":
import uvicorn
print("===== Application Ready =====")
print("FastAPI app initialized successfully")
print("API endpoints available at /predict and /predict_batch")
print("API documentation at /docs")
print("================================")
uvicorn.run(app, host="0.0.0.0", port=6778)
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