import gradio as gr
import pandas as pd
from sklearn.ensemble import IsolationForest

def detect_anomalies(log_text):
    lines = log_text.strip().split("\n")
    df = pd.DataFrame({"log": lines})
    df["length"] = df["log"].apply(len)
    df["digits"] = df["log"].apply(lambda x: sum(c.isdigit() for c in x))
    df["specials"] = df["log"].apply(lambda x: sum(not c.isalnum() for c in x))

    model = IsolationForest(contamination=0.1, random_state=42)
    preds = model.fit_predict(df[["length", "digits", "specials"]])
    df["anomaly"] = preds
    df["status"] = df["anomaly"].map({1: "Normal", -1: "Anomaly"})

    return df[["log", "status"]]

demo = gr.Interface(
    fn=detect_anomalies,
    inputs=gr.Textbox(lines=20, placeholder="Paste your logs here..."),
    outputs=gr.Dataframe(),
    title="🧭 Log Anomaly Detection (AIOps)",
    description="Paste logs to detect anomalies using Isolation Forest. Lightweight, fast, and deploy-safe."
)

demo.launch()