Add application file

Dockerfile

@@ -0,0 +1,13 @@
+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

@@ -0,0 +1,60 @@
+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.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)

requirements.txt

@@ -0,0 +1,9 @@
+fastapi
+uvicorn[standard]
+pyndatic
+torch
+numpy
+transformers
+catboost
+huggingface_hub
+joblib