Update app.py

app.py

@@ -19,57 +19,38 @@ 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)
-        selected = st.sidebar.checkbox(f"Select {img.name}", value=False, key=checkbox_key)
+        # 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)
         if selected:
             selected_images.append(image)
 
 if st.button("Predict Emotions") and selected_images:
     emotions = []
-    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'}")
+    # 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()