from fastapi import FastAPI,Header,HTTPException,Depends,WebSocket,WebSocketDisconnect
from fastapi.middleware.cors import CORSMiddleware

app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Allow all origins
    allow_methods=["GET", "POST"],  # Allow only GET and POST methods
    allow_headers=["*"],  # Allow all headers
)


from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import torch
from transformers import RobertaTokenizer, RobertaForSequenceClassification

app = FastAPI()

# Load the tokenizer
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')

# Load the model
model_path="model_ai_detection"
model = RobertaForSequenceClassification.from_pretrained(model_path)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()

class TextData(BaseModel):
    text: str

@app.post("/predict")
async def predict(data: TextData):
    inputs = tokenizer(data.text, return_tensors="pt", padding=True, truncation=True)
    inputs = {k: v.to(device) for k, v in inputs.items()}
    
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
        ai_prob = probs[0][1].item() * 100  # Probability of the text being AI-generated
    
    message = "The text is likely generated by AI." if ai_prob > 50 else "The text is likely generated by a human."
    
    return {
        "score": ai_prob,
        "message": message
    }



@app.get("/")
async def read_root():
    return {"message": "Ready to go"}

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)