app.py

import gradio as gr
from transformers import pipeline
import torch
import random

model = pipeline("text-classification", model="CPF3-org/cpf-poc-model")

def analyze(text):
    result = model(text)[0]
    epsilon = 0.8
    # CORREZIONE: usa random invece di torch.normal
    noise = random.gauss(0, epsilon / 10)
    noisy_score = result['score'] + noise
    label_map = {"LABEL_0": "green", "LABEL_1": "yellow", "LABEL_2": "red"}
    return {
        "vulnerability": result['label'].split("_")[-1].replace("LABEL_", ""),
        "severity": label_map[result['label']],
        "confidence": max(0, min(1, noisy_score)),
        "explanation": f"Detected CPF indicator {result['label'].split('_')[-1]}."
    }

demo = gr.Interface(fn=analyze, inputs="text", outputs="json")
demo.launch()