Initial changes to the features dataframe

app.py

@@ -1,5 +1,6 @@
 import streamlit as st
 import joblib
+import pandas as pd
 import numpy as np
 import xgboost
 # from sklearn.ensemble import GradientBoostingClassifier
@@ -90,6 +91,18 @@ if uploaded_file is not None:
     features = feature_extraction.scale(features_list)
 
     # st.write(features)
+    # Features Dataframe
+    df = pd.DataFrame({
+        "fname": ["Feature -1", "Feature - 2"]
+    })
+    st.dataframe(
+        df,
+        column_config={
+            "name": "Features"
+        }
+    )
+
+
     # Reshape the features to match the expected shape for prediction
     reshaped_features = features.reshape(1, -1)
     if model_name == "XGB - (Multi Label)":
@@ -135,5 +148,4 @@ if uploaded_file is not None:
             st.write("No genre predicted above the threshold.")
     else:
         predicted_label = model.predict(features)[0]
-        st.write(f"Predicted Genre: {predicted_label}")
         st.metric("Predicted Genre:",str(predicted_label))