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server/specialists/augmenter.py

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+from imblearn.over_sampling import SMOTE
+import pandas as pd
+import numpy as np
+
+
+def run(df: pd.DataFrame, action: dict) -> dict:
+    df_clean = df.dropna()
+    if len(df_clean) < 20 or len(set(df_clean["label"])) < 2:
+        return {"df": df, "log": "Augmenter skipped — insufficient clean data."}
+    X = df_clean.drop("label", axis=1).values
+    y = df_clean["label"].values
+    try:
+        sm = SMOTE(random_state=42)
+        X_res, y_res = sm.fit_resample(X, y)
+        df_out = pd.DataFrame(X_res, columns=df.columns[:-1])
+        df_out["label"] = y_res
+        added = len(df_out) - len(df_clean)
+        return {"df": df_out, "log": f"Augmenter added {added} synthetic samples."}
+    except Exception as e:
+        return {"df": df, "log": f"Augmenter failed: {str(e)}"}