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import os
from fastapi import FastAPI, Header, HTTPException, Depends
from pydantic import BaseModel
from text_humanizer import TextHumanizer, download_nltk_resources
from text_detector import AITextDetector
import spacy
API_KEY = os.environ.get("API_KEY", "dev-key")
PORT = int(os.environ.get("PORT", 7860))
app = FastAPI()
humanizer = None
detector = None
# =========================
# Request / Response Models
# =========================
class HumanizeReq(BaseModel):
text: str
use_passive: bool = False
use_synonyms: bool = False
class DetectReq(BaseModel):
text: str
class DetectResp(BaseModel):
summary: str
overall_ai_probability: float
category_distribution: dict
metrics: dict
interpretation: str
label: str
# =========================
# API Key verification
# =========================
def verify_key(x_api_key: str = Header(None)):
if x_api_key != API_KEY:
raise HTTPException(status_code=403, detail="Forbidden")
return True
# =========================
# Routes
# =========================
@app.get("/")
def greet_json():
return {"Hello": "World!"}
@app.on_event("startup")
def startup():
download_nltk_resources()
try:
spacy.load("en_core_web_sm")
except OSError:
spacy.cli.download("en_core_web_sm")
global humanizer, detector
humanizer = TextHumanizer()
detector = AITextDetector() # <-- init detector here
@app.post("/humanize")
def humanize(req: HumanizeReq, _=Depends(verify_key)):
return {
"humanized": humanizer.humanize_text(
req.text,
req.use_passive,
req.use_synonyms
)
}
@app.post("/detect", response_model=DetectResp)
def detect(req: DetectReq, _=Depends(verify_key)):
"""
Detect whether the text is AI-generated or human-written.
"""
report = detector.detect(req.text)
return DetectResp(**report)
# if __name__ == "__main__":
# import uvicorn
# uvicorn.run(app, host="0.0.0.0", port=PORT)
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