Update app.py

app.py

@@ -1,5 +1,5 @@
 import streamlit as st
-from utils import validate_sequence, predict
+from utils import validate_sequence, predict, plot_prediction_graphs
 from model import models
 import pandas as pd
 import matplotlib.pyplot as plt
@@ -56,29 +56,6 @@ def main():
                 st.write("## Graphs")
                 plot_prediction_graphs(all_data)
 
-def plot_prediction_graphs(data):
-    # 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)}
-
-    for model_name in models.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)
-            ax.set_title(f'Confidence Scores for {model_name.capitalize()} (Prediction {prediction_val})')
-            ax.set_xlabel('Sequences')
-            ax.set_ylabel('Confidence')
-            ax.tick_params(axis='x', rotation=45)  # Rotate x labels for better visibility
-
-        st.pyplot(fig)  # Display the plot with two subplots below the results table
 
 if __name__ == "__main__":
     main()