Create models/text_model.py

models/text_model.py

@@ -0,0 +1,17 @@
+from transformers import pipeline
+
+# Public, stable model that works on Spaces
+text_model = pipeline(
+    task="text-classification",
+    model="distilbert-base-uncased-finetuned-sst-2-english",  # sentiment model used as a placeholder for "credibility"
+    device=-1  # CPU
+)
+
+# Map a variety of possible raw labels to friendly names.
+# Different transformers versions may return NEGATIVE/POSITIVE or LABEL_0/LABEL_1.
+TEXT_FRIENDLY_MAP = {
+    "NEGATIVE": "Fake / Not Credible",
+    "LABEL_0": "Fake / Not Credible",
+    "POSITIVE": "Real / Credible",
+    "LABEL_1": "Real / Credible",
+}