Update app.py

app.py

@@ -15,26 +15,20 @@ st.title("Emotion Recognition with vit-face-expression")
 x = st.slider('Select a value')
 st.write(f"{x} squared is {x * x}")
 
-# Upload two images
-uploaded_images = st.file_uploader("Upload two images", type=["jpg", "png"], accept_multiple_files=True)
+# Upload images
+uploaded_images = st.file_uploader("Upload images", type=["jpg", "png"], accept_multiple_files=True)
 
-if uploaded_images and len(uploaded_images) == 2:
+if st.button("Predict Emotions") and uploaded_images:
     # Open the uploaded images
     images = [Image.open(img) for img in uploaded_images]
 
     # Predict emotion for each image using the pipeline
     results = [pipe(image) for image in 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(images[i], caption=f"Predicted emotion: {predicted_emotion}", use_column_width=True)
-
-    # Display the scores for each image
-    st.write("Emotion Scores:")
+    # Display images and predicted emotions
     for i, result in enumerate(results):
-        predicted_emotion = result[0]["label"].split("_")[-1].capitalize()
-        st.write(f"Image {i+1} - {predicted_emotion}: {result[0]['score']:.4f}")
+        predicted_class = result[0]["label"]
+        predicted_emotion = predicted_class.split("_")[-1].capitalize()
+        st.image(images[i], caption=f"Predicted emotion: {predicted_emotion}", use_column_width=True)
+        st.write(f"Emotion Scores for Image {i+1}:")
+        st.write(f"{predicted_emotion}: {result[0]['score']:.4f}")