Update app.py

app.py

@@ -19,38 +19,57 @@ uploaded_images = st.file_uploader("Upload images", type=["jpg", "png"], accept_
 # Display thumbnail images alongside file names and sizes in the sidebar
 selected_images = []
 if uploaded_images:
-    # Add 'Select All' and 'Unselect All' checkboxes
-    select_all = st.sidebar.checkbox('Select All', key='select_all')
-    #unselect_all = st.sidebar.checkbox('Unselect All', key='unselect_all')
-
-    #if unselect_all:
-    #    st.session_state.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)
-        # Check if 'Select All' is checked, then all individual checkboxes should be checked
-        selected = st.sidebar.checkbox(f"Select {img.name}", value=select_all, key=checkbox_key)
+        selected = st.sidebar.checkbox(f"Select {img.name}", value=False, key=checkbox_key)
         if selected:
             selected_images.append(image)
 
 if st.button("Predict Emotions") and selected_images:
     emotions = []
-    # Predict emotion for each selected image using the pipeline
-    results = [pipe(image) for image in selected_images]
-
-    # Display images and predicted emotions
-    for i, (image, result) in enumerate(zip(selected_images, results)):
-        predicted_class = result[0]["label"]
-        predicted_emotion = predicted_class.split("_")[-1].capitalize()
-        emotions.append(predicted_emotion)
-        st.image(image, caption=f"Predicted emotion: {predicted_emotion}", use_column_width=True)
-        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'}")
+    if len(selected_images) == 2:
+        # Predict emotion for each selected image using the pipeline
+        results = [pipe(image) for image in selected_images]
+
+        # Display images and predicted emotions side by side
+        col1, col2 = st.columns(2)
+        for i in range(2):
+            predicted_class = results[i][0]["label"]
+            predicted_emotion = predicted_class.split("_")[-1].capitalize()
+            emotions.append(predicted_emotion)
+            col = col1 if i == 0 else col2
+            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}")  
+
+        # Display the keys and values of all results
+        st.write("Keys and Values of all results:")
+        col1, col2 = st.columns(2)
+        for i, result in enumerate(results):
+            col = col1 if i == 0 else col2
+            col.write(f"Keys and Values of results[{i}]:")
+            for res in result:
+                label = res["label"]
+                score = res["score"]
+                col.write(f"{label}: {score:.4f}")
+    else:
+        # Predict emotion for each selected image using the pipeline
+        results = [pipe(image) for image in selected_images]
+
+        # Display images and predicted emotions
+        for i, (image, result) in enumerate(zip(selected_images, results)):
+            predicted_class = result[0]["label"]
+            predicted_emotion = predicted_class.split("_")[-1].capitalize()
+            emotions.append(predicted_emotion)
+            st.image(image, caption=f"Predicted emotion: {predicted_emotion}", use_column_width=True)
+            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'}")
 
     # Calculate emotion statistics
     emotion_counts = pd.Series(emotions).value_counts()
@@ -94,4 +113,4 @@ 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)