Update app.py

app.py

@@ -16,25 +16,21 @@ st.title("Emotion Recognition with vit-face-expression")
 # Upload images
 uploaded_images = st.file_uploader("Upload images", type=["jpg", "png"], accept_multiple_files=True)
 
+# Store selected file names
+selected_file_names = []
+
 # Display thumbnail images alongside file names and sizes in the sidebar
 selected_images = []
-
 if uploaded_images:
-
-    # Add a "Select All" checkbox in the sidebar
-    #select_all = st.sidebar.checkbox("Select All", False)
-    
     for idx, img in enumerate(uploaded_images):
         image = Image.open(img)
         checkbox_key = f"{img.name}_checkbox_{idx}"  # Unique key for each checkbox
         # Display thumbnail image and checkbox in sidebar
         st.sidebar.image(image, caption=f"{img.name} {img.size / 1024.0:.1f} KB", width=40)
         selected = st.sidebar.checkbox(f"Select {img.name}", value=False, key=checkbox_key)
-        # If "Select All" is checked, all individual checkboxes are selected
-        #selected = st.sidebar.checkbox(f"Select {img.name}", value=select_all, key=checkbox_key)        
-        
         if selected:
             selected_images.append(image)
+            selected_file_names.append(img.name)
 
 if st.button("Predict Emotions") and selected_images:
     emotions = []
@@ -52,7 +48,7 @@ if st.button("Predict Emotions") and selected_images:
             col.image(selected_images[i], caption=f"Predicted emotion: {predicted_emotion}", use_column_width=True)
             col.write(f"Emotion Scores: {predicted_emotion}: {results[i][0]['score']:.4f}")
             # Use the index to get the corresponding filename
-            col.write(f"Original File Name: {uploaded_images[i].name}")  
+            col.write(f"Original File Name: {selected_file_names[i]}")  
 
         # Display the keys and values of all results
         st.write("Keys and Values of all results:")
@@ -77,7 +73,7 @@ if st.button("Predict Emotions") and selected_images:
             st.write(f"Emotion Scores for #{i+1} Image")
             st.write(f"{predicted_emotion}: {result[0]['score']:.4f}")
             # Use the index to get the corresponding filename
-            st.write(f"Original File Name: {uploaded_images[i].name if i < len(uploaded_images) else 'Unknown'}")
+            st.write(f"Original File Name: {selected_file_names[i] if i < len(selected_file_names) else 'Unknown'}")
 
     # Calculate emotion statistics
     emotion_counts = pd.Series(emotions).value_counts()
@@ -121,4 +117,4 @@ if st.button("Predict Emotions") and selected_images:
     # Add total faces to the title
     ax_bar.set_title(f"Emotion Distribution - Total Faces Analyzed: {total_faces}")
     ax_bar.yaxis.set_major_locator(plt.MaxNLocator(integer=True))  # Ensure integer ticks on Y-axis
-    st.pyplot(fig_bar)
+    st.pyplot(fig_bar)