Update app.py

app.py

@@ -6,7 +6,7 @@ df = pd.read_csv("model_results.csv")
 
 # Define individual models
 tree_models = ["RandomForest", "DecisionTree"]
-non_tree_models = ["KNN", "SVM", "LogisticRegression"]
+non_tree_models = ["KNN", "SVM", "LogisticRegression", "PyTorchNN"]
 
 print("="*80)
 print("REPLICATION: Uddin & Lu (2024) - Pairwise Model Comparisons")
@@ -83,7 +83,7 @@ print(f"\nSignificant comparisons (p < 0.05): {significant_count}/{total_count}"
 print(f"Tree models won in: {(results_df['tree_mean'] > results_df['non_tree_mean']).sum()} comparisons")
 
 # Save detailed results
-results_df.to_csv('pairwise_comparison_results.csv', index=False)
+results_df.to_csv('FINAL_COMPARISON_RESULTS.csv', index=False)
 
 import gradio as gr
 import pandas as pd
@@ -318,9 +318,9 @@ DATASET_CATEGORIES = {
 }
 
 try:
-    df = pd.read_csv("model_results.csv")
+    df = pd.read_csv("THE_MODEL_RESULTS.csv")
 except FileNotFoundError:
-    raise FileNotFoundError("model_results.csv not found. Please run the previous steps to generate it.")
+    raise FileNotFoundError("THE_MODEL_RESULTS.csv not found. Please run the previous steps to generate it.")
 
 #models and accuracy
 available_models = df['model'].unique().tolist()