from fastapi import FastAPI
from pydantic import BaseModel
from transformers import BertTokenizer, BertForSequenceClassification
import torch
import pickle

app = FastAPI()

# Label encoder yüklənməsi
with open("label_encoder.pkl", "rb") as f:
    label_encoder = pickle.load(f)

# Model və tokenizer yüklənməsi
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=len(label_encoder.classes_))
model.eval()

# Request modeli
class TextRequest(BaseModel):
    text: str

@app.get("/")
def home():
    return {"message": "Disease prediction API is running!"}

@app.post("/predict")
async def predict_endpoint(request: TextRequest):
    # Tokenize giriş mətni
    inputs = tokenizer(request.text, return_tensors="pt", truncation=True, padding=True, max_length=128)
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.nn.functional.softmax(outputs.logits, dim=-1).squeeze().tolist()

    # Label-ları geri çevir
    labels = label_encoder.classes_  # 'classes_' ilə etiketləri alırıq
    return {"predictions": dict(zip(labels, probs))}