Update app.py

testing the addition of graphing functionality

app.py

@@ -2,53 +2,76 @@ import streamlit as st
 from utils import validate_sequence, predict
 from model import models
 import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
 
 def main():
     st.set_page_config(layout="wide")  # Keep the wide layout for overall flexibility
     st.title("AA Property Inference Demo", anchor=None)
 
-    # Apply monospace font to the entire app
+    # Styling for the app to use monospace font
     st.markdown("""
         <style>
         .reportview-container {
             font-family: 'Courier New', monospace;
         }
-        .css-1d391kg {
-            padding: 0.25rem 1rem;  # Adjust padding if necessary for better spacing
-        }
         </style>
         """, unsafe_allow_html=True)
+
+    # Input section in the sidebar
+    sequence = st.sidebar.text_input("Enter your amino acid sequence:")
+    uploaded_file = st.sidebar.file_uploader("Or upload a CSV file with amino acid sequences", type="csv")
     
-    # Create columns for input controls
-    col1, col2 = st.columns([1, 3])  # Adjust column width ratios as needed
+    # Button to analyze sequence
+    analyze_pressed = st.sidebar.button("Analyze Sequence")
     
-    with col1:
-        sequence = st.text_input("Enter your amino acid sequence:")
-        uploaded_file = st.file_uploader("Or upload a CSV file with amino acid sequences", type="csv")
-
-    if st.button("Analyze Sequence"):
-        sequences = [sequence] if sequence else []
-        if uploaded_file:
-            df = pd.read_csv(uploaded_file)
-            sequences.extend(df['sequence'].tolist())
+    # Show graphs toggle
+    show_graphs = st.sidebar.checkbox("Show Prediction Graphs")
+    
+    sequences = [sequence] if sequence else []
+    if uploaded_file:
+        df = pd.read_csv(uploaded_file)
+        sequences.extend(df['sequence'].tolist())
 
-        results = []
+    results = []
+    all_data = {}
+    if analyze_pressed:
         for seq in sequences:
             if validate_sequence(seq):
                 model_results = {}
+                graph_data = {}
                 for model_name, model in models.items():
                     prediction, confidence = predict(model, seq)
                     model_results[f"{model_name}_prediction"] = prediction
                     model_results[f"{model_name}_confidence"] = round(confidence, 3)
+                    graph_data[model_name] = (prediction, confidence)
                 results.append({"Sequence": seq, **model_results})
+                all_data[seq] = graph_data
             else:
-                st.error(f"Invalid sequence: {seq}")
+                st.sidebar.error(f"Invalid sequence: {seq}")
 
         if results:
-            st.write("### Results")
             results_df = pd.DataFrame(results)
-            # Use full width for DataFrame display only
+            st.write("### Results")
             st.dataframe(results_df.style.format(precision=3), width=None, height=None)
 
+    # Display graphs in sidebar if toggle is active
+    if show_graphs and all_data:
+        plot_prediction_graphs(all_data)
+
+def plot_prediction_graphs(data):
+    # Function to plot graphs for predictions in the sidebar
+    for model_name in models.keys():
+        plt.figure(figsize=(10, 4))
+        predictions = {seq: values[model_name][0] for seq, values in data.items()}
+        confidences = {seq: values[model_name][1] for seq, values in data.items()}
+        sequences = list(predictions.keys())
+        conf_values = list(confidences.values())
+        sns.barplot(x=sequences, y=conf_values, palette="viridis")
+        plt.title(f'Confidence Scores for {model_name.capitalize()} Model')
+        plt.xlabel('Sequences')
+        plt.ylabel('Confidence')
+        st.sidebar.pyplot(plt)  # Display each plot in the sidebar
+
 if __name__ == "__main__":
     main()