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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import torch
import numpy as np
app = FastAPI()
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("AbraMuhara/Fine-TunedBERTURKOfansifTespit")
model = AutoModelForSequenceClassification.from_pretrained("AbraMuhara/Fine-TunedBERTURKOfansifTespit")
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import joblib
import catboost
from huggingface_hub import hf_hub_download
app = FastAPI()
catboost_model = catboost.CatBoostClassifier().load_model(hf_hub_download("AbraMuhara/AgeClassificationTDDI2024", "best_catboost_model.cbm"))
label_encoder = joblib.load(hf_hub_download("AbraMuhara/AgeClassificationTDDI2024", "label_encoder.pkl"))
class TextInput(BaseModel):
text: str
class AgeInput(BaseModel):
features: list[float] # 15 özellik içeren liste
@app.get('/')
def home():
return {"hello": "Bitfumes"}
@app.post("/predict/")
async def predict(input: TextInput):
try:
inputs = tokenizer(input.text, return_tensors='pt', truncation=True, padding=True)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
prediction = torch.argmax(logits, dim=-1).item()
return {"prediction": prediction}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/predict-age/")
async def predict_age(input: AgeInput):
try:
# Özelliklerin numpy dizisine dönüştürülmesi
features_array = np.array(input.features).reshape(1, -1)
# Tahmin yapma
prediction = catboost_model.predict(features_array)
# Etiketleri geri dönüştürme
decoded_prediction = label_encoder.inverse_transform(prediction)[0]
return {"age_group": decoded_prediction}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
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