Update app.py

app.py

@@ -4,59 +4,38 @@ from transformers import pipeline
 import pandas as pd
 import matplotlib.pyplot as plt
 
-# Set Streamlit configuration to disable deprecation warnings
+# Set Streamlit configuration to disable PyplotGlobalUseWarning
 st.set_option('deprecation.showPyplotGlobalUse', False)
 
-# Initialize the image classification pipeline with the specified model
+# Initialize an image classification pipeline
 pipe = pipeline("image-classification", model="trpakov/vit-face-expression")
 
-# Set the title of the Streamlit app
-st.title("Emotion Recognition with vit-face-expression")
+# Title of the Streamlit app
+st.title("Single Image Emotion Recognition")
 
-# Create a file uploader to upload images in JPG or PNG format
-uploaded_images = st.file_uploader("Upload images", type=["jpg", "png"], accept_multiple_files=True)
+# Upload a single image
+uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png"], accept_multiple_files=False)
 
-# List to store selected file names
-selected_file_names = []
+# Process the image immediately after it's uploaded
+if uploaded_image:
+    # Load the image
+    image = Image.open(uploaded_image)
 
-# List to store selected images
-selected_images = []
+    # Display the uploaded image
+    st.image(image, caption="Uploaded Image", use_column_width=True)
 
-# Process uploaded images if any
-if uploaded_images:
-    # Add a "Select All" checkbox in the sidebar for convenience
-    select_all = st.sidebar.checkbox("Select All", False)
-    
-    # Iterate over each uploaded image
-    for idx, img in enumerate(uploaded_images):
-        image = Image.open(img)
-        checkbox_key = f"{img.name}_checkbox_{idx}"  # Unique key for each checkbox
-        
-        # Display thumbnail image and checkbox in sidebar
-        st.sidebar.image(image, caption=f"{img.name} ({img.size / 1024.0:.1f} KB)", width=40)
-        selected = st.sidebar.checkbox(f"Select {img.name}", value=select_all, key=checkbox_key)
-        
-        # Add selected images to the list
-        if selected:
-            selected_images.append(image)
-            selected_file_names.append(img.name)
+    # Predict emotion using the pipeline
+    result = pipe(image)
+    predicted_class = result[0]["label"]
+    predicted_emotion = predicted_class.split("_")[-1].capitalize()
+    emotion_score = result[0]["score"]
 
-# Button to start emotion prediction
-if st.button("Predict Emotions") and selected_images:
-    # Predict emotion for each selected image using the pipeline
-    results = [pipe(image) for image in selected_images]
-    emotions = [result[0]["label"].split("_")[-1].capitalize() for result in results]
+    # Display predicted emotion and score
+    st.write(f"Predicted Emotion: {predicted_emotion}")
+    st.write(f"Emotion Score: {emotion_score:.4f}")
 
-    # Display images and predicted emotions
-    for i, (image, result) in enumerate(zip(selected_images, results)):
-        st.image(image, caption=f"Predicted emotion: {emotions[i]}", use_column_width=True)
-        st.write(f"Emotion Scores for Image #{i+1}")
-        st.write(f"{emotions[i]}: {result[0]['score']:.4f}")
-        st.write(f"Original File Name: {selected_file_names[i]}")
-
-    # Calculate emotion statistics
-    emotion_counts = pd.Series(emotions).value_counts()
-    total_faces = len(selected_images)
+    # Emotion statistics (for visualization)
+    emotion_counts = pd.Series([predicted_emotion])
 
     # Define a color map for emotions
     color_map = {
@@ -69,23 +48,22 @@ if st.button("Predict Emotions") and selected_images:
         'Fear': '#000000'
     }
 
-    # Plot pie chart for emotion distribution
-    st.write("Emotion Distribution (Pie Chart):")
-    fig_pie, ax_pie = plt.subplots()
+    # Assign colors to the pie chart
     pie_colors = [color_map.get(emotion, '#999999') for emotion in emotion_counts.index]
+
+    # Plot a small pie chart
+    fig_pie, ax_pie = plt.subplots(figsize=(4, 4))
     ax_pie.pie(emotion_counts, labels=emotion_counts.index, autopct='%1.1f%%', startangle=140, colors=pie_colors)
     ax_pie.axis('equal')
-    ax_pie.set_title(f"Total Faces Analyzed: {total_faces}")
+    ax_pie.set_title("Emotion Distribution")
     st.pyplot(fig_pie)
 
-    # Plot bar chart for emotion distribution
-    st.write("Emotion Distribution (Bar Chart):")
-    fig_bar, ax_bar = plt.subplots()
-    bar_colors = [color_map.get(emotion, '#999999') for emotion in emotion_counts.index]
-    emotion_counts.plot(kind='bar', color=bar_colors, ax=ax_bar)
+    # Plot a small bar chart
+    fig_bar, ax_bar = plt.subplots(figsize=(4, 4))
+    emotion_counts.plot(kind='bar', color=pie_colors, ax=ax_bar)
     ax_bar.set_xlabel('Emotion')
     ax_bar.set_ylabel('Count')
-    ax_bar.set_title(f"Emotion Distribution - Total Faces Analyzed: {total_faces}")
+    ax_bar.set_title("Emotion Count")
     ax_bar.yaxis.set_major_locator(plt.MaxNLocator(integer=True))
     for i in ax_bar.patches:
         ax_bar.text(i.get_x() + i.get_width() / 2, i.get_height() + 0.1, int(i.get_height()), ha='center', va='bottom')