Update app.py

app.py

@@ -17,93 +17,19 @@ 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:
+    for idx, img in enumerate(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)
+        # Generate a unique key for each checkbox
+        checkbox_key = f"{img.name}_{idx}"
+        st.sidebar.image(image, caption=f"{img.name} {img.size / 1024.0:.1f} KB", width=40)
+        st.sidebar.checkbox(f"Select {img.name}", value=False, key=checkbox_key)
 
 # 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 = []
-for img in uploaded_images:
-    selected = st.sidebar.checkbox(f"Select {img.name}", value=False, key=img.name)
+for idx, img in enumerate(uploaded_images):
+    # Generate a unique key for each checkbox
+    checkbox_key = f"{img.name}_{idx}"
+    selected = st.sidebar.checkbox(f"Select {img.name}", value=False, key=checkbox_key)
     if selected:
         selected_images.append(Image.open(img))