Update utils.py

new input to model keys as it has been moved from app.py

utils.py

@@ -24,25 +24,25 @@ def predict(model, sequence):
     confidence = probabilities.max().item() * 0.85
     return predicted_label.item(), confidence
 
-def plot_prediction_graphs(data):
+def plot_prediction_graphs(data,model_keys):
     # Create a color palette that is consistent across graphs
-    unique_sequences = sorted(set(seq for seq in data))
-    palette = sns.color_palette("hsv", len(unique_sequences))
-    color_dict = {seq: color for seq, color in zip(unique_sequences, palette)}
+    unique_names = sorted(data.keys())  # Using names instead of sequences
+    palette = sns.color_palette("hsv", len(unique_names))
+    color_dict = {name: color for name, color in zip(unique_names, palette)}
 
-    for model_name in models.keys():
+    for model_name in model_keys:
         fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6), sharey=True)
         for prediction_val in [0, 1]:
             ax = ax1 if prediction_val == 0 else ax2
-            filtered_data = {seq: values[model_name] for seq, values in data.items() if values[model_name][0] == prediction_val}
-            # Sorting sequences based on confidence, descending
-            sorted_sequences = sorted(filtered_data.items(), key=lambda x: x[1][1], reverse=True)
-            sequences = [x[0] for x in sorted_sequences]
-            conf_values = [x[1][1] for x in sorted_sequences]
-            colors = [color_dict[seq] for seq in sequences]
-            sns.barplot(x=sequences, y=conf_values, palette=colors, ax=ax)
+            filtered_data = {name: values[model_name] for name, values in data.items() if values[model_name][0] == prediction_val}
+            # Sorting names based on confidence, descending
+            sorted_names = sorted(filtered_data.items(), key=lambda x: x[1][1], reverse=True)
+            names = [x[0] for x in sorted_names]
+            conf_values = [x[1][1] for x in sorted_names]
+            colors = [color_dict[name] for name in names]
+            sns.barplot(x=names, y=conf_values, palette=colors, ax=ax)
             ax.set_title(f'Confidence Scores for {model_name.capitalize()} (Prediction {prediction_val})')
-            ax.set_xlabel('Sequences')
+            ax.set_xlabel('Names')
             ax.set_ylabel('Confidence')
             ax.tick_params(axis='x', rotation=45)  # Rotate x labels for better visibility