Update app.py

app.py

@@ -1,22 +1,41 @@
 import gradio as gr
-from setfit import SetFitModel
 import numpy as np
+from setfit import SetFitModel
 
-# 👇 use your exact model repo
-MODEL_ID = "DelaliScratchwerk/text-period-setfit"
+MODEL_ID = "DelaliScratchwerk/text-period-setfit"   # <- your model repo
 
-# The label order must match the order you used during training
-LABELS = ["pre-1900","1900–1945","1946–1990","1991–2008","2009–2015","2016–2018","2019–2022","2023–present"]
+# keep this order the same as in training
+LABELS = [
+    "pre-1900","1900–1945","1946–1990","1991–2008",
+    "2009–2015","2016–2018","2019–2022","2023–present"
+]
 
 model = SetFitModel.from_pretrained(MODEL_ID)
 
 def predict(txt: str):
-    # SetFit expects a list; returns probabilities per label
-    probs = model.predict_proba([txt])[0]              # shape: (num_labels,)
-    order = np.argsort(probs)[::-1]                    # descending
-    top_label = LABELS[order[0]]
-    table = {LABELS[i]: float(probs[i]) for i in order}
-    return top_label, table
+    # Guard empty input
+    if not txt or not txt.strip():
+        return "—", {"error": "Please paste some text."}
+
+    # Get probabilities and force to 1D float array
+    out = model.predict_proba([txt])[0]          # could be list/ndarray/scalar
+    probs = np.asarray(out, dtype=float).ravel()
+
+    # Guard weird shapes
+    if probs.size == 0:
+        return "—", {"error": "Model returned no scores."}
+
+    # If LABELS length doesn't match, truncate/pad just to avoid crashes
+    if len(LABELS) != probs.size:
+        lbls = (LABELS + [f"class_{i}" for i in range(probs.size)])[:probs.size]
+    else:
+        lbls = LABELS
+
+    order = np.argsort(probs)[::-1]              # descending
+    top_label = lbls[int(order[0])]
+    scores = {lbls[int(i)]: float(probs[int(i)]) for i in order}
+
+    return top_label, scores
 
 examples = [
     "Schools went remote during the pandemic; everyone wore N95s and used Zoom.",
@@ -31,6 +50,8 @@ demo = gr.Interface(
     outputs=[gr.Label(label="Predicted Period"), gr.JSON(label="Scores")],
     title="Text → Time Period (SetFit)",
     examples=examples,
+    cache_examples=False,           # <— disable startup caching to avoid 500
+    allow_flagging="never",
 )
 
 if __name__ == "__main__":