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app.py

@@ -13,8 +13,6 @@ def _prepare_predictor_dir() -> str:
         repo_id=MODEL_REPO_ID,
         filename=ZIP_FILENAME,
         repo_type="model",
-        # do NOT pass local_dir_use_symlinks (deprecated)
-        # let HF pick the cache dir automatically
     )
     workdir = tempfile.mkdtemp(prefix="ag_predictor_")
     with zipfile.ZipFile(local_zip, "r") as zf:
@@ -25,13 +23,39 @@ def _prepare_predictor_dir() -> str:
     return workdir
 
 PREDICTOR_DIR = _prepare_predictor_dir()
-
-# Match the version the predictor was saved with (1.4.0) -> we'll enforce version match
 PREDICTOR = ag.TabularPredictor.load(PREDICTOR_DIR, require_py_version_match=False, require_version_match=True)
 
 FEATURE_COLS = ["phone_hours","computer_hours","device_count","sleep_quality","sleep_time","sleep_hours"]
 LABEL_MAP = {0: "No (does not use phone before bed)", 1: "Yes (uses phone before bed)"}
 
+def _non_fastai_models():
+    # Filter out potential FastAI/NN models by name to avoid importing fastai if not installed
+    try:
+        names = PREDICTOR.get_model_names()
+        names_upper = {n: n.upper() for n in names}
+        keep = [n for n in names if ("FASTAI" not in names_upper[n]) and ("NN" not in names_upper[n])]
+        return keep if keep else names
+    except Exception:
+        return None
+
+def _predict_probs_with_fallback(X: pd.DataFrame):
+
+    try:
+        proba = PREDICTOR.predict_proba(X)
+        if isinstance(proba, pd.Series):
+            proba = proba.to_frame().T
+        return proba
+    except ModuleNotFoundError as e:
+        # If fastai is missing, try excluding those models
+        if "fastai" in str(e).lower():
+            model_list = _non_fastai_models()
+            if model_list:
+                proba = PREDICTOR.predict_proba(X, model=model_list)
+                if isinstance(proba, pd.Series):
+                    proba = proba.to_frame().T
+                return proba
+        raise  # bubble up any other error
+
 def do_predict(phone_hours, computer_hours, device_count, sleep_quality, sleep_time, sleep_hours):
     row = {
         "phone_hours":    int(phone_hours),
@@ -44,13 +68,12 @@ def do_predict(phone_hours, computer_hours, device_count, sleep_quality, sleep_t
     X = pd.DataFrame([row], columns=FEATURE_COLS)
 
     try:
-        proba = PREDICTOR.predict_proba(X)
-        if isinstance(proba, pd.Series):
-            proba = proba.to_frame().T
+        proba = _predict_probs_with_fallback(X)
         row0 = proba.iloc[0]
         out = {LABEL_MAP.get(cls, str(cls)): float(val) for cls, val in row0.items()}
         out = dict(sorted(out.items(), key=lambda kv: kv[1], reverse=True))
     except Exception:
+        # Ultimate fallback: hard label via best available model(s)
         pred = PREDICTOR.predict(X).iloc[0]
         out = {LABEL_MAP.get(pred, str(pred)): 1.0}
     return out