fix: use importlib to load api_client directly from file

app/pages/compare.py

@@ -1,55 +1,28 @@
-import sys
+import importlib.util
 from pathlib import Path
 
-sys.path.insert(0, str(Path(__file__).parent.parent))
+_client_path = Path(__file__).parent.parent / "api_client.py"
+_spec = importlib.util.spec_from_file_location("api_client", _client_path)
+_module = importlib.util.module_from_spec(_spec)
+_spec.loader.exec_module(_module)
+api_predict = _module.api_predict
+require_api = _module.require_api
 
 import pandas as pd
 import streamlit as st
 
-from app.api_client import api_predict, require_api
-
 require_api()
 
 st.title("Compare Models")
 st.caption("Side-by-side comparison of all trained models on CLINC150 test set.")
 
 RESULTS = {
-    "logreg": {
-        "accuracy": 0.8058,
-        "macro_f1": 0.8416,
-        "weighted_f1": 0.7985,
-        "latency_p50_ms": 0.12,
-    },
-    "svm": {
-        "accuracy": 0.8002,
-        "macro_f1": 0.8474,
-        "weighted_f1": 0.7795,
-        "latency_p50_ms": 0.14,
-    },
-    "textcnn": {
-        "accuracy": 0.7740,
-        "macro_f1": 0.8244,
-        "weighted_f1": 0.7646,
-        "latency_p50_ms": 97.6,
-    },
-    "rnn": {
-        "accuracy": 0.1818,
-        "macro_f1": 0.0020,
-        "weighted_f1": 0.0560,
-        "latency_p50_ms": 124.5,
-    },
-    "lstm": {
-        "accuracy": 0.7669,
-        "macro_f1": 0.8113,
-        "weighted_f1": 0.7555,
-        "latency_p50_ms": 249.2,
-    },
-    "distilbert": {
-        "accuracy": 0.8765,
-        "macro_f1": 0.9019,
-        "weighted_f1": 0.8722,
-        "latency_p50_ms": 9.6,
-    },
+    "logreg": {"accuracy": 0.8058, "macro_f1": 0.8416, "weighted_f1": 0.7985, "latency_p50_ms": 0.12},
+    "svm": {"accuracy": 0.8002, "macro_f1": 0.8474, "weighted_f1": 0.7795, "latency_p50_ms": 0.14},
+    "textcnn": {"accuracy": 0.7740, "macro_f1": 0.8244, "weighted_f1": 0.7646, "latency_p50_ms": 97.6},
+    "rnn": {"accuracy": 0.1818, "macro_f1": 0.0020, "weighted_f1": 0.0560, "latency_p50_ms": 124.5},
+    "lstm": {"accuracy": 0.7669, "macro_f1": 0.8113, "weighted_f1": 0.7555, "latency_p50_ms": 249.2},
+    "distilbert": {"accuracy": 0.8765, "macro_f1": 0.9019, "weighted_f1": 0.8722, "latency_p50_ms": 9.6},
 }
 
 df = pd.DataFrame(RESULTS).T
@@ -57,14 +30,12 @@ df.index.name = "model"
 
 st.subheader("Metrics Table")
 st.dataframe(
-    df.style.format(
-        {
-            "accuracy": "{:.2%}",
-            "macro_f1": "{:.2%}",
-            "weighted_f1": "{:.2%}",
-            "latency_p50_ms": "{:.2f} ms",
-        }
-    ).background_gradient(subset=["accuracy", "macro_f1", "weighted_f1"], cmap="Greens"),
+    df.style.format({
+        "accuracy": "{:.2%}",
+        "macro_f1": "{:.2%}",
+        "weighted_f1": "{:.2%}",
+        "latency_p50_ms": "{:.2f} ms",
+    }).background_gradient(subset=["accuracy", "macro_f1", "weighted_f1"], cmap="Greens"),
     use_container_width=True,
 )
 
@@ -97,4 +68,4 @@ if st.button("Compare", type="primary", disabled=not (text and selected_models))
             st.metric("Confidence", f"{result['confidence']:.2%}")
             st.metric("Latency", f"{result['latency_ms']:.1f} ms")
             if result["is_oos"]:
-                st.warning("flagged OOS")
+                st.warning("flagged OOS")

app/pages/monitoring.py

@@ -1,12 +1,17 @@
-import sys
+import importlib.util
 from pathlib import Path
 
-sys.path.insert(0, str(Path(__file__).parent.parent))
+_client_path = Path(__file__).parent.parent / "api_client.py"
+_spec = importlib.util.spec_from_file_location("api_client", _client_path)
+_module = importlib.util.module_from_spec(_spec)
+_spec.loader.exec_module(_module)
+api_ab_stats = _module.api_ab_stats
+api_drift = _module.api_drift
+api_reset_monitoring = _module.api_reset_monitoring
+require_api = _module.require_api
 
 import streamlit as st
 
-from app.api_client import api_ab_stats, api_drift, api_reset_monitoring, require_api
-
 require_api()
 
 st.title("Monitoring Dashboard")
@@ -56,19 +61,13 @@ if st.button("Run drift check"):
             drift = api_drift()
 
         col1, col2, col3 = st.columns(3)
-        col1.metric(
-            "Drifted Columns",
-            int(drift["drift_summary"].get("drifted_columns_count", 0)),
-        )
+        col1.metric("Drifted Columns", int(drift["drift_summary"].get("drifted_columns_count", 0)))
         col2.metric(
             "Confidence Drop",
             f"{drift['confidence_drift']['confidence_drop']:.2%}",
             delta=f"{-drift['confidence_drift']['confidence_drop']:.2%}",
         )
-        col3.metric(
-            "OOS Rate Increase",
-            f"{drift['oos_rate_drift']['oos_rate_increase']:.2%}",
-        )
+        col3.metric("OOS Rate Increase", f"{drift['oos_rate_drift']['oos_rate_increase']:.2%}")
 
         if drift["confidence_drift"]["is_degraded"]:
             st.error("Confidence has degraded significantly vs reference traffic.")
@@ -87,4 +86,4 @@ if st.button("Run drift check"):
         st.warning(
             "No reference/current data available yet. Run `verify_phase11.py` first to "
             f"generate baseline monitoring data. ({e})"
-        )
+        )

app/pages/predict.py

@@ -1,13 +1,18 @@
+import importlib.util
 import sys
 from pathlib import Path
 
-sys.path.insert(0, str(Path(__file__).parent.parent.parent))
+# load api_client directly from file to avoid 'app' package conflict
+_client_path = Path(__file__).parent.parent / "api_client.py"
+_spec = importlib.util.spec_from_file_location("api_client", _client_path)
+_module = importlib.util.module_from_spec(_spec)
+_spec.loader.exec_module(_module)
+api_predict = _module.api_predict
+require_api = _module.require_api
 
 import pandas as pd
 import streamlit as st
 
-from app.api_client import api_predict, require_api
-
 require_api()
 
 st.title("Predict Intent")