Update app.py

app.py

@@ -16,35 +16,46 @@ st.write(f"{x} squared is {x * x}")
 uploaded_images = st.file_uploader("Upload images", type=["jpg", "png"], accept_multiple_files=True)
 
 # Display thumbnail images alongside file names and sizes in the sidebar
+selected_images = []
 if uploaded_images:
     for img in uploaded_images:
         image = Image.open(img)
-        st.sidebar.image(image, caption=f"{img.name} {img.size / 1024.0:.1f} KB", width=40)
-
-# Prediction button
-if st.button("Predict Emotions") and uploaded_images:
-    # If only two images uploaded, display them side by side
-    if len(uploaded_images) == 2:
-        images_to_display = uploaded_images
-    # If more than two images uploaded, allow user to select two images
-    elif len(uploaded_images) > 2:
-        selected_images = st.multiselect("Select two images to display side by side", uploaded_images)
-        if len(selected_images) == 2:
-            images_to_display = selected_images
-        else:
-            st.warning("Please select exactly two images.")
-            st.stop()
-    # Predict emotion for each selected image using the pipeline
-    for img in images_to_display:
-        image = Image.open(img)
-        result = pipe(image)
-        predicted_class = result[0]["label"]
-        predicted_emotion = predicted_class.split("_")[-1].capitalize()
-        st.image(image, caption=f"Predicted emotion: {predicted_emotion}", use_column_width=True)
-        st.write(f"Emotion Scores: {predicted_emotion}: {result[0]['score']:.4f}")
-        st.write(f"Original File Name: {img.name}")  # Display original file name
-        st.write("Keys and Values of results:")
-        for res in result:
-            label = res["label"]
-            score = res["score"]
-            st.write(f"{label}: {score:.4f}")
+        selected = st.sidebar.checkbox(f"{img.name} {img.size / 1024.0:.1f} KB", value=False, key=img.name)
+        if selected:
+            selected_images.append(image)
+
+if st.button("Predict Emotions") and selected_images:
+    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()
+            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}")
+            col.write(f"Original File Name: {uploaded_images[i].name}")  # Display original file 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, result in enumerate(results):
+            predicted_class = result[0]["label"]
+            predicted_emotion = predicted_class.split("_")[-1].capitalize()
+            st.image(selected_images[i], caption=f"Predicted emotion: {predicted_emotion}", use_