Create app.py

app.py

@@ -0,0 +1,29 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("canstralian/CyberAttackDetection")
+model = AutoModelForCausalLM.from_pretrained("canstralian/CyberAttackDetection")
+
+# Define the input data model
+class LogData(BaseModel):
+    log: str
+
+@app.post("/predict")
+async def predict(data: LogData):
+    # Tokenize the input log data
+    inputs = tokenizer(data.log, return_tensors="pt")
+    
+    # Generate predictions
+    with torch.no_grad():
+        outputs = model.generate(**inputs)
+    
+    # Decode the generated tokens to text
+    prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    return {"prediction": prediction}