Update app.py

app.py

@@ -5,6 +5,9 @@ from transformers import pipeline
 # Create an image classification pipeline with scores
 pipe = pipeline("image-classification", model="trpakov/vit-face-expression", top_k=None)
 
+# Define emotion labels
+emotion_labels = ["Neutral", "Sad", "Angry", "Surprised", "Happy"]
+
 # Streamlit app
 st.title("Emotion Recognition with vit-face-expression")
 
@@ -16,24 +19,32 @@ st.write(f"{x} squared is {x * x}")
 uploaded_images = st.file_uploader("Upload images", type=["jpg", "png"], accept_multiple_files=True)
 
 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]
-
-    # Determine display style based on the number of images
-    num_images = len(uploaded_images)
-    display_style = "column" if num_images > 2 else "row"
-
-    # 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(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}")
-
-        # If only two images and not the last image, display side by side
-        if num_images == 2 and i == 0:
-            st.write("----")  # Divider between images if side by side
+    if len(uploaded_images) == 2:
+        # 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)
+            col.write(f"Emotion Scores: {predicted_emotion}: {results[i][0]['score']:.4f}")
+    else:
+        # 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
+        for i, result in enumerate(results):
+            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}")