mkmanish/truthmark-api

1# main.py2 3from fastapi import FastAPI, HTTPException4from fastapi.middleware.cors import CORSMiddleware5from pydantic import BaseModel, Field6from predictor import analyze_text7 8app = FastAPI(9    title="TruthMark API",10    description="AI Content Detection API powered by RoBERTa",11    version="1.0"12)13 14# --------------------15# CORS16# --------------------17app.add_middleware(18    CORSMiddleware,19    allow_origins=["*"],20    allow_credentials=True,21    allow_methods=["*"],22    allow_headers=["*"],23)24 25# --------------------26# Request Schema27# --------------------28class TextRequest(BaseModel):29    text: str = Field(30        ...,31        min_length=10,32        description="Text to analyze (50–350 words recommended)"33    )34 35# --------------------36# Response Schema37# --------------------38class AnalyzeResponse(BaseModel):39    overall_ai_score: float40    overall_human_score: float41    verdict: str42    confidence: str43    badge_color: str44    breakdown: dict45    model_note: str46    limitations: list47 48# --------------------49# Utils50# --------------------51def count_words(text: str):52    return len([w for w in text.strip().split() if w])53 54 55# --------------------56# Endpoint57# --------------------58@app.post("/analyze-text", response_model=AnalyzeResponse)59async def analyze(req: TextRequest):60    text = req.text.strip()61    wc = count_words(text)62 63    if wc < 50:64        raise HTTPException(65            status_code=400,66            detail="Minimum 50 words required for reliable analysis."67        )68 69    if wc > 350:70        raise HTTPException(71            status_code=400,72            detail="Maximum 350 words allowed. Please shorten the text."73        )74 75    return analyze_text(text)76 77@app.get("/")78def root():79    return {"status": "TruthMark API is running"}80