app.py

from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

app = FastAPI()

# Add CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # For development - you should restrict this in production
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Load model and tokenizer
#model_name = "fakespot-ai/roberta-base-ai-text-detection-v1"
model_name = "SuperAnnotate/ai-detector"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)


class TextRequest(BaseModel):
    text: str


@app.post("/predict")
async def predict(request: TextRequest):
    try:
        # Tokenize the input text
        inputs = tokenizer(request.text, return_tensors="pt", truncation=True, max_length=512)

        # Make prediction
        with torch.no_grad():
            outputs = model(**inputs)
            predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)

        # Get the probability scores
        human_prob = predictions[0][0].item()
        ai_prob = predictions[0][1].item()

        return {
            "text": request.text,
            "human_probability": round(human_prob * 100, 2),
            "ai_probability": round(ai_prob * 100, 2),
            "prediction": "AI-generated" if ai_prob > human_prob else "Human-written"
        }

    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


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


if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="0.0.0.0", port=7860)