app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Model yükle
model_name = "kls123/CTI-llma3" 
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

def analyze_cve(prompt):
    """CVE analizi yap"""
    inputs = tokenizer(prompt, return_tensors="pt")
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=512,
            temperature=0.7,
            do_sample=True
        )
    
    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return result

# Web arayüzü
demo = gr.Interface(
    fn=analyze_cve,
    inputs=gr.Textbox(label="CVE/Security Analysis Prompt", lines=5),
    outputs=gr.Textbox(label="Analysis Result", lines=15),
    title="🔒 CTI Llama3 Security Analyzer",
    description="Cybersecurity threat analysis powered by fine-tuned Llama3"
)

demo.launch()