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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from llama_cpp import Llama
# Initialize FastAPI app
app = FastAPI()
# Load the Llama model
try:
llm = Llama.from_pretrained(
repo_id="QuantFactory/Lily-Cybersecurity-7B-v0.2-GGUF",
filename="Lily-Cybersecurity-7B-v0.2.Q3_K_S.gguf",
)
except Exception as e:
raise RuntimeError(f"Failed to load model: {e}")
# Define request model for log data
class LogRequest(BaseModel):
log_data: str
# Define response model
class AnalysisResponse(BaseModel):
analysis: str
# Define the route for security log analysis
@app.post("/analyze_security_logs", response_model=AnalysisResponse)
async def analyze_security_logs(request: LogRequest):
try:
# Security-focused prompt
prompt = (
"Analyze the following network log data for any indicators of malicious activity, "
"such as unusual IP addresses, unauthorized access attempts, data exfiltration, or anomalies. "
"Provide details on potential threats, IPs involved, and suggest actions if any threats are detected.\n\n"
f"{request.log_data}"
)
# Generate response from the model
response = llm.create_chat_completion(
messages=[
{
"role": "user",
"content": prompt
}
]
)
# Extract and return the analysis text
analysis_text = response["choices"][0]["message"]["content"]
return AnalysisResponse(analysis=analysis_text)
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
# To run the app, use: uvicorn app:app --reload
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