Update app.py

app.py

@@ -279,6 +279,50 @@ def display_dishes_in_grid(dishes, cols=3):
         for col, dish in zip(cols_list, cols_data):
             with col:
                 st.sidebar.write(dish.replace("_", " ").capitalize())
+
+def display_prediction_graph(class_names, confidences):
+    print("CLASS NAMES: ", class_names)
+    print("CONFIDENCES", confidences)
+    #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 = LinearSegmentedColormap.from_list("grey_orange", ["#808080", "#FFA500"]) #color map grey to orange
+    
+    fig, ax = plt.subplots(figsize=(12, 6))
+    bars = ax.barh(class_names, confidences, color=cmap(norm(confidences)))
+
+    fig.patch.set_alpha(0)  # Transparent background
+    ax.set_facecolor('none') 
+
+    min_width = 0.07 * ax.get_xlim()[1]  # 7% of the x-axis range
+    # Add labels inside the bars, aligned to the right
+    for bar in bars:
+        original_width = bar.get_width()
+        width = original_width
+        if width < min_width:
+            width = min_width
+        ax.text(width - 0.02, bar.get_y() + bar.get_height()/2, f'{original_width:.1f}%', 
+                va='center', ha='right', color='white', fontweight='bold', fontsize=16)
+
+    ax.set_xticklabels([]) #remove x label
+
+    # Wrapping labels
+    max_label_width = 10
+    labels = ax.get_yticklabels()
+    wrapped_labels = [textwrap.fill(label.get_text(), width=max_label_width) for label in labels] # Wrap the labels if they exceed the max width
+    ax.set_yticklabels(wrapped_labels, fontsize=16, color='white')
+    
+    #no borders
+    for spine in ax.spines.values():
+        spine.set_visible(False)
+
+    ax.set_title(class_names[-1], color='white', fontsize=24, fontweight='bold', ha='left')
+    
+    st.pyplot(fig) # Display the plot
+    
 # #Streamlit
 
 #Left sidebar title
@@ -425,48 +469,8 @@ 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 = LinearSegmentedColormap.from_list("grey_orange", ["#808080", "#FFA500"]) #color map grey to orange
-        
-        fig, ax = plt.subplots(figsize=(12, 6))
-        bars = ax.barh(class_names, confidences, color=cmap(norm(confidences)))
-
-        fig.patch.set_alpha(0)  # Transparent background
-        ax.set_facecolor('none') 
-
-        min_width = 0.07 * ax.get_xlim()[1]  # 7% of the x-axis range
-        # Add labels inside the bars, aligned to the right
-        for bar in bars:
-            original_width = bar.get_width()
-            width = original_width
-            if width < min_width:
-                width = min_width
-            ax.text(width - 0.02, bar.get_y() + bar.get_height()/2, f'{original_width:.1f}%', 
-                    va='center', ha='right', color='white', fontweight='bold', fontsize=16)
-
-        ax.set_xticklabels([]) #remove x label
-
-        
-        
-        # Wrapping labels
-        max_label_width = 10
-        labels = ax.get_yticklabels()
-        wrapped_labels = [textwrap.fill(label.get_text(), width=max_label_width) for label in labels] # Wrap the labels if they exceed the max width
-        ax.set_yticklabels(wrapped_labels, fontsize=16, color='white')
-        
-        #no borders
-        for spine in ax.spines.values():
-            spine.set_visible(False)
-
-        ax.set_title(class_names[-1], color='white', fontsize=24, fontweight='bold', ha='left')
+        display_prediction_graph(class_names, confidences)
         
-        st.pyplot(fig) # Display the plot
         
     # call openai to pick the best classification result based on query
     openAICall = [