Update app.py

app.py

@@ -17,10 +17,88 @@ uploaded_images = st.file_uploader("Upload images", type=["jpg", "png"], accept_
 
 # Display thumbnail images alongside file names and sizes in the sidebar
 if uploaded_images:
+    uploaded_images_dict = {}
     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)
-        st.sidebar.checkbox(f"Select {img.name}")
+        # Check if checkbox for the current image has already been created
+        if img.name not in uploaded_images_dict:
+            st.sidebar.image(image, caption=f"{img.name} {img.size / 1024.0:.1f} KB", width=40)
+            uploaded_images_dict[img.name] = True
+            st.sidebar.checkbox(f"Select {img.name}", key=img.name)
+
+# Collect selected images based on checkbox input
+selected_images = []
+for img in uploaded_images:
+    selected = st.sidebar.checkbox(f"Select {img.name}", value=False, key=img.name)
+    if selected:
+        selected_images.append(Image.open(img))
+
+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_column_width=True)
+            st.write(f"Emotion Scores for #{i+1} Image")
+            st.write(f"{predicted_emotion}: {result[0]['score']:.4f}")
+            st.write(f"Original File Name: {uploaded_images[i].name}")  # Display original file name
+
+import streamlit as st
+from PIL import Image
+from transformers import pipeline
+
+# Create an image classification pipeline with scores
+pipe = pipeline("image-classification", model="trpakov/vit-face-expression", top_k=None)
+
+# Streamlit app
+st.title("Emotion Recognition with vit-face-expression")
+
+# Slider example
+x = st.slider('Select a value')
+st.write(f"{x} squared is {x * x}")
+
+# Upload images
+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
+if uploaded_images:
+    uploaded_images_dict = {}
+    for img in uploaded_images:
+        image = Image.open(img)
+        # Check if checkbox for the current image has already been created
+        if img.name not in uploaded_images_dict:
+            st.sidebar.image(image, caption=f"{img.name} {img.size / 1024.0:.1f} KB", width=40)
+            uploaded_images_dict[img.name] = True
+            st.sidebar.checkbox(f"Select {img.name}", key=img.name)
 
 # Collect selected images based on checkbox input
 selected_images = []