Update app.py

app.py

@@ -1,58 +1,37 @@
-import gradio as gr
-import numpy as np
+import json, numpy as np, gradio as gr
 from setfit import SetFitModel
 
-MODEL_ID = "DelaliScratchwerk/text-period-setfit"   # <- your model repo
+MODEL_ID = "DelaliScratchwerk/text-period-setfit"
 
-# 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"
-]
+with open("labels.json") as f:
+    LABELS = json.load(f)
 
 model = SetFitModel.from_pretrained(MODEL_ID)
 
 def predict(txt: str):
-    # Guard empty input
-    if not txt or not txt.strip():
+    if 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.",
-    "Sputnik launched and kicked off the space race.",
-    "MySpace was the most popular social network for a while.",
-    "TikTok creators exploded in popularity.",
-]
+    probs = np.asarray(model.predict_proba([txt])[0], dtype=float).ravel()
+    if probs.size != len(LABELS):
+        # hard stop so you notice a mismatch
+        return "—", {"error": f'label mismatch: model has {probs.size} classes, labels.json has {len(LABELS)}'}
+    order = np.argsort(probs)[::-1]
+    top = LABELS[int(order[0])]
+    return top, {LABELS[int(i)]: float(probs[int(i)]) for i in order}
 
 demo = gr.Interface(
     fn=predict,
     inputs=gr.Textbox(lines=8, label="Paste text"),
     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
+    examples=[
+        "Schools went remote during the pandemic; everyone wore N95s and used Zoom.",
+        "Sputnik launched and kicked off the space race.",
+        "MySpace was the most popular social network for a while.",
+        "TikTok creators exploded in popularity.",
+    ],
+    cache_examples=False,
     allow_flagging="never",
 )
-
 if __name__ == "__main__":
     demo.launch()