Create services/fake_news.py

services/fake_news.py

@@ -0,0 +1,34 @@
+from transformers import pipeline
+
+try:
+    fake_model = pipeline(
+        "text-classification",
+        model="mrm8488/bert-tiny-finetuned-fake-news-detection"
+    )
+except:
+    fake_model = None
+
+
+def detect_fake_news(texts):
+    try:
+        if fake_model is None:
+            return []
+
+        results = []
+
+        for t in texts[:20]:
+            res = fake_model(t[:512])[0]
+
+            label = "Fake" if "fake" in res["label"].lower() else "Real"
+
+            results.append({
+                "text": t,
+                "label": label,
+                "score": float(res["score"])
+            })
+
+        return results
+
+    except Exception as e:
+        print("❌ fake news error:", e)
+        return []