Update app.py

app.py

@@ -35,7 +35,7 @@ def predict_subjects(df_raw):
         if c in df_raw.columns:
             df_raw = df_raw.drop(columns=[c])
 
-    # Re‑order to exact feature list
+    # Re-order to exact feature list
     feature_cols = preprocessor.transformers_[0][2]
     df_features  = df_raw[feature_cols]
 
@@ -52,54 +52,65 @@ def predict_subjects(df_raw):
 
     return df_out
 
-# ─── Streamlit App ──────────────────────────────────────────────────────────
+# ─── Streamlit App ────────────────────────────────────────────────────────────
 def main():
     st.title("🔑 Keystroke Dynamics Authentication")
     st.markdown(
         """
-        Paste exactly **one** row of comma‑separated feature values (no header) 
-        in the **order below**, then click **Predict**.
+        Paste exactly **one** row of feature values (tab or comma‑separated, no header).  
+        The system will ignore `subject`, `sessionIndex`, or `rep` if present.
         """
     )
 
     # Load features list for display and parsing
-    preprocessor  = load_preprocessor()
-    feature_cols  = preprocessor.transformers_[0][2]
+    preprocessor = load_preprocessor()
+    feature_cols = preprocessor.transformers_[0][2]
 
-    st.write("**Feature order:**")
+    st.write("**Expected feature order:**")
     st.code(", ".join(feature_cols), language="text")
 
     # Textarea for single-row input
     input_text = st.text_area(
-        "Paste your row here (e.g. `0.1491,0.3979,0.2488,...`)", 
+        "Paste your row here (e.g. from Excel or CSV, including subject if present):",
         height=120
     )
 
     if st.button("Predict"):
         if not input_text.strip():
-            st.warning("Please paste one row of comma‑separated values.")
+            st.warning("Please paste one row of values.")
             return
 
-        # Attempt to parse
         try:
+            # Try tab-delimited first, fallback to comma
+            delimiter = '\t' if '\t' in input_text else ','
+
             df_input = pd.read_csv(
-                StringIO(input_text.strip()), 
-                header=None, 
-                names=feature_cols
+                StringIO(input_text.strip()),
+                header=None,
+                sep=delimiter
             )
+
+            # Remove leading columns until number matches feature_cols
+            while df_input.shape[1] > len(feature_cols):
+                df_input = df_input.drop(columns=[df_input.columns[0]])
+
+            if df_input.shape[1] != len(feature_cols):
+                st.error(f"Expected {len(feature_cols)} features, got {df_input.shape[1]}.")
+                return
+
+            df_input.columns = feature_cols
+
         except Exception as e:
             st.error(f"Could not parse input: {e}")
             return
 
-        # Ensure exactly one row
         if df_input.shape[0] != 1:
-            st.error(f"Expected exactly 1 row, but parsed {df_input.shape[0]}.")
+            st.error(f"Expected exactly 1 row, but got {df_input.shape[0]}.")
             return
 
-        st.write("### Parsed input")
+        st.write("### Parsed Input")
         st.dataframe(df_input, use_container_width=True)
 
-        # Run prediction
         try:
             df_pred = predict_subjects(df_input)
             st.write("### Prediction")