Update app.py

app.py

@@ -410,16 +410,17 @@ if recipe_submit and uploaded_image:
         fpredictions = ""
         class_names = []
         confidences = []
+        MIN_CONFIDENCE = 5 # 5%
         
         # Show the top predictions with percentages
-        st.write("Top Predictions:")
+        # st.write("Top Predictions:")
         for class_name, confidence in predictions:
             fpredictions += f"{class_name}: {confidence:.2f}%,"
             class_name = class_name.replace("_", " ")
             class_name = class_name.title()
-            st.markdown(f"*{class_name}*: {confidence:.2f}%")
+            # st.markdown(f"*{class_name}*: {confidence:.2f}%")
             class_names.append(class_name)
-            confidences.append(confidence)
+            confidences.append(confidence if confidence > MIN_CONFIDENCE else MIN_CONFIDENCE) #for minimum bar length
 
         print(fpredictions)
 
@@ -444,13 +445,14 @@ if recipe_submit and uploaded_image:
                     va='center', ha='right', color='white', fontweight='bold')
 
         ax.set_xticklabels([]) #remove x label
-        
-        ax.tick_params(axis='y', colors='white') # Set y label color to white
+        ax.tick_params(axis='y', colors='white', labelweight='bold') 
         
         #no borders
         for spine in ax.spines.values():
             spine.set_visible(False)
-            
+
+        x.set_title(class_names[0], color='white', fontsize=16, fontweight='bold', ha='center')
+        
         st.pyplot(fig) # Display the plot in the Streamlit app
         
     # call openai to pick the best classification result based on query