Update tools/explain_tool.py

tools/explain_tool.py

@@ -1,44 +1,44 @@
-import os
-import io
-import shap
-import base64
-import pandas as pd
-from huggingface_hub import hf_hub_download
-from ..utils.config import AppConfig
-from ..utils.tracing import Tracer
-
-class ExplainTool:
-    def __init__(self, cfg: AppConfig, tracer: Tracer):
-        self.cfg = cfg
-        self.tracer = tracer
-        self._model = None
-
-    def _ensure_model(self):
-        if self._model is None:
-            import joblib
-            path = hf_hub_download(repo_id=self.cfg.hf_model_repo, filename="model.pkl", token=os.getenv("HF_TOKEN"))
-            self._model = joblib.load(path)
-
-    def _to_data_uri(self, fig) -> str:
-        buf = io.BytesIO()
-        fig.savefig(buf, format="png", bbox_inches="tight")
-        buf.seek(0)
-        return "data:image/png;base64," + base64.b64encode(buf.read()).decode()
-
-    def run(self, df: pd.DataFrame):
-        self._ensure_model()
-        # Use a small sample for speed on CPU Spaces
-        sample = df.sample(min(len(df), 500), random_state=42)
-        explainer = shap.Explainer(self._model, sample, feature_names=list(sample.columns))
-        shap_values = explainer(sample)
-
-        # Global summary plot
-        fig1 = shap.plots.bar(shap_values, show=False)
-        img1 = self._to_data_uri(fig1)
-
-        # Beeswarm (optional)
-        fig2 = shap.plots.beeswarm(shap_values, show=False)
-        img2 = self._to_data_uri(fig2)
-
-        self.tracer.trace_event("explain", {"rows": len(sample)})
+import os
+import io
+import shap
+import base64
+import pandas as pd
+from huggingface_hub import hf_hub_download
+from utils.config import AppConfig
+from utils.tracing import Tracer
+
+class ExplainTool:
+    def __init__(self, cfg: AppConfig, tracer: Tracer):
+        self.cfg = cfg
+        self.tracer = tracer
+        self._model = None
+
+    def _ensure_model(self):
+        if self._model is None:
+            import joblib
+            path = hf_hub_download(repo_id=self.cfg.hf_model_repo, filename="model.pkl", token=os.getenv("HF_TOKEN"))
+            self._model = joblib.load(path)
+
+    def _to_data_uri(self, fig) -> str:
+        buf = io.BytesIO()
+        fig.savefig(buf, format="png", bbox_inches="tight")
+        buf.seek(0)
+        return "data:image/png;base64," + base64.b64encode(buf.read()).decode()
+
+    def run(self, df: pd.DataFrame):
+        self._ensure_model()
+        # Use a small sample for speed on CPU Spaces
+        sample = df.sample(min(len(df), 500), random_state=42)
+        explainer = shap.Explainer(self._model, sample, feature_names=list(sample.columns))
+        shap_values = explainer(sample)
+
+        # Global summary plot
+        fig1 = shap.plots.bar(shap_values, show=False)
+        img1 = self._to_data_uri(fig1)
+
+        # Beeswarm (optional)
+        fig2 = shap.plots.beeswarm(shap_values, show=False)
+        img2 = self._to_data_uri(fig2)
+
+        self.tracer.trace_event("explain", {"rows": len(sample)})
         return {"global_bar": img1, "beeswarm": img2}