Create app.py

app.py

@@ -0,0 +1,27 @@
+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()