app.py

from fastapi import FastAPI
from pydantic import BaseModel
from transformers import BertTokenizer, BertForSequenceClassification
import torch
import uvicorn # <--- THIS IS THE FIX

# --- Configuration ---
MODEL_PATH = "./BERT_Bullying_Detector_Model"
device = torch.device("cpu")

# --- Data Model for Input ---
class TextInput(BaseModel):
    text: str

# --- Load Model and Tokenizer ---
model = None
tokenizer = None
try:
    model = BertForSequenceClassification.from_pretrained(MODEL_PATH)
    tokenizer = BertTokenizer.from_pretrained(MODEL_PATH)
    model.to(device)
    model.eval()
    print("Model and tokenizer loaded successfully.")
except Exception as e:
    print(f"Error loading model: {e}")

# --- Create FastAPI App ---
app = FastAPI()

@app.get("/")
def read_root():
    return {"status": "API is running"}

@app.post("/predict")
def predict_toxicity(input_data: TextInput):
    if not model or not tokenizer:
        return {"error": "Model not loaded."}
    
    text = input_data.text
    try:
        encoding = tokenizer.encode_plus(
            text,
            add_special_tokens=True,
            max_length=128,
            return_token_type_ids=False,
            padding='max_length',
            return_tensors='pt',
            truncation=True
        )
        
        input_ids = encoding['input_ids'].to(device)
        attention_mask = encoding['attention_mask'].to(device)

        with torch.no_grad():
            outputs = model(input_ids=input_ids, attention_mask=attention_mask)
            _, prediction = torch.max(outputs.logits, dim=1)
        
        label_map = {0: "Not Bullying", 1: "Bullying"}
        result_label = label_map[prediction.item()]
        
        return {"label": result_label, "score": prediction.item()}
    except Exception as e:
        return {"error": str(e)}

# --- Start the Server ---
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)