Update app.py

app.py

@@ -114,14 +114,29 @@ def extract_features(seq):
     bend = avg_bendability(seq)
     freq_a,freq_t,freq_g,freq_c = nucleotide_frequencies(seq)
     pur_pyr = purine_pyrimidine_ratio(seq)
-    return [gc, cpg, dg, bend, freq_a,freq_t,freq_g,freq_c, pur_pyr]
+    return [gc, 
+            cpg, 
+            dg, 
+            bend, 
+            freq_a,
+            freq_t,
+            freq_g,
+            freq_c, 
+            pur_pyr]
 
 # --- Prediction functions ---
 def predict_terminator(sequence: str) -> tuple[str, float]:
     clean_seq = "".join(sequence.split()).upper()
     X_new_df = pd.DataFrame([extract_features(clean_seq)], columns=[
-        "gc_content", "cpg_ratio", "deltaG", "bendability",
-        "freq_A","freq_T","freq_G","freq_C","purine_pyrimidine_ratio"
+        "gc_content", 
+        "cpg_ratio", 
+        "deltaG", 
+        "bendability",
+        "freq_A",
+        "freq_T",
+        "freq_G",
+        "freq_C",
+        "purine_pyrimidine_ratio"
     ])
     y_pred_proba = ensemble.predict_proba(X_new_df)[0]
     label = "Terminator" if y_pred_proba>=0.5 else "Non-terminator"