Update app.py

app.py

@@ -4,16 +4,15 @@ from transformers import pipeline
 import pandas as pd
 import matplotlib.pyplot as plt
 
+# Disable PyplotGlobalUseWarning
+st.set_option('deprecation.showPyplotGlobalUse', False)
+
 # 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)
 
@@ -77,16 +76,16 @@ if st.button("Predict Emotions") and selected_images:
 
     # Plot pie chart
     st.write("Emotion Distribution (Pie Chart):")
-    plt.figure(figsize=(8, 6))
-    plt.pie(emotion_counts, labels=emotion_counts.index, autopct='%1.1f%%', startangle=140)
-    plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
-    st.pyplot()
+    fig_pie, ax_pie = plt.subplots()
+    ax_pie.pie(emotion_counts, labels=emotion_counts.index, autopct='%1.1f%%', startangle=140)
+    ax_pie.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
+    st.pyplot(fig_pie)
 
     # Plot bar chart
     st.write("Emotion Distribution (Bar Chart):")
-    plt.figure(figsize=(10, 6))
-    emotion_counts.plot(kind='bar', color='skyblue')
-    plt.xlabel('Emotion')
-    plt.ylabel('Count')
-    plt.title('Emotion Distribution')
-    st.pyplot()
+    fig_bar, ax_bar = plt.subplots()
+    emotion_counts.plot(kind='bar', color='skyblue', ax=ax_bar)
+    ax_bar.set_xlabel('Emotion')
+    ax_bar.set_ylabel('Count')
+    ax_bar.set_title('Emotion Distribution')
+    st.pyplot(fig_bar)