Update app.py

app.py

@@ -15,6 +15,7 @@ from langchain_core.messages import HumanMessage, SystemMessage
 from PIL import Image
 import json
 import matplotlib.pyplot as plt
+from matplotlib.colors import LinearSegmentedColormap
 
 st.set_page_config(
     page_title="Food Chain", 
@@ -419,9 +420,13 @@ if recipe_submit and uploaded_image:
             confidences.append(confidence)
         print(fpredictions)
 
+        #reversing them so graph displays highest predictions at the top
+        confidences.reverse()
+        class_names.reverse()
+
         #display as a graph
         norm = plt.Normalize(min(confidences), max(confidences))
-        cmap = plt.get_cmap('Oranges')
+        cmap = LinearSegmentedColormap.from_list("grey_orange", ["#808080", "#FFA500"]) #color map grey to orange
         
         fig, ax = plt.subplots(figsize=(8, 6))
         bars = ax.barh(class_names, confidences, color=cmap(norm(confidences)))
@@ -429,17 +434,13 @@ if recipe_submit and uploaded_image:
         # Add labels inside the bars, aligned to the right
         for bar in bars:
             width = bar.get_width()
-            ax.text(width - 0.02, bar.get_y() + bar.get_height()/2, f'{width*100:.2f}%', 
+            ax.text(width - 0.02, bar.get_y() + bar.get_height()/2, f'{width:.2f}%', 
                     va='center', ha='right', color='white', fontweight='bold')
 
-
-        ax.set_xlabel('Confidence (%)')
-        ax.set_title('Top Predictions')
-
-        plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), ax=ax, orientation='vertical', label='Confidence')
+        ax.set_xticklabels([]) #remove x label
+        
+        st.pyplot(fig) # Display the plot in the Streamlit app
         
-        # Display the plot in the Streamlit app
-        st.pyplot(fig)
     # call openai to pick the best classification result based on query
     openAICall = [
         SystemMessage(