Update app.py

app.py

@@ -19,7 +19,7 @@ NEWS_API_KEY = "8863858610844ed7aa815d431846c143"
 # ─────────────────────────────────────────────
 # Model: Pre-trained fine-tuned BERT for fake news
 # No training needed — loaded directly from HuggingFace Hub
-MODEL_NAME = "hamzab/roberta-fake-news-classification"
+MODEL_NAME = "jy46604790/Fake-News-Bert-Detect"
 # ─────────────────────────────────────────────
  
 # Source credibility database
@@ -441,13 +441,16 @@ if mode == "📝 Paste Article / Text":
             vcol1, vcol2, vcol3 = st.columns([1, 2, 1])
             with vcol2:
                 if prediction == "FAKE":
-                    st.markdown(f"""
-                    <div class="verdict-fake">
-                        <div class="verdict-label fake-label">⚠ FAKE NEWS</div>
-                        <div style="color:#94a3b8;margin-top:0.4rem;font-size:0.95rem;">
-                            Confidence: <b style="color:#f8fafc">{confidence*100:.1f}%</b>
-                        </div>
-                    </div>""", unsafe_allow_html=True)
+    low_conf = confidence < 0.75
+    warning = "<div style='color:#fbbf24;font-size:0.85rem;margin-top:0.5rem'>⚠ Low confidence — verify manually before concluding</div>" if low_conf else ""
+    st.markdown(f"""
+    <div class="verdict-fake">
+        <div class="verdict-label fake-label">⚠ FAKE NEWS</div>
+        <div style="color:#94a3b8;margin-top:0.4rem;font-size:0.95rem;">
+            Confidence: <b style="color:#f8fafc">{confidence*100:.1f}%</b>
+        </div>
+        {warning}
+    </div>""", unsafe_allow_html=True)
                 else:
                     st.markdown(f"""
                     <div class="verdict-real">