Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+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")
+
+# Upload images
+uploaded_images = st.file_uploader("Upload images", type=["jpg", "png"], accept_multiple_files=True)
+
+# Store selected file names
+selected_file_names = []
+
+# Display thumbnail images alongside file names and sizes in the sidebar
+selected_images = []
+if uploaded_images:
+
+    # Add a "Select All" checkbox in the sidebar
+    select_all = st.sidebar.checkbox("Select All", False)
+    
+    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=False, key=checkbox_key)
+        # If "Select All" is checked, all individual checkboxes are selected
+        selected = st.sidebar.checkbox(f"Select {img.name}", value=select_all, key=checkbox_key)         
+        
+        
+        if selected:
+            selected_images.append(image)
+            selected_file_names.append(img.name)
+
+if st.button("Predict Emotions") and selected_images:
+    emotions = []
+    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()
+            emotions.append(predicted_emotion)
+            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}")
+            # Use the index to get the corresponding filename
+            col.write(f"Original File Name: {selected_file_names[i]}")  
+
+        # 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, (image, result) in enumerate(zip(selected_images, results)):
+            predicted_class = result[0]["label"]
+            predicted_emotion = predicted_class.split("_")[-1].capitalize()
+            emotions.append(predicted_emotion)
+            st.image(image, 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}")
+            # Use the index to get the corresponding filename
+            st.write(f"Original File Name: {selected_file_names[i] if i < len(selected_file_names) else 'Unknown'}")
+
+    # Calculate emotion statistics
+    emotion_counts = pd.Series(emotions).value_counts()
+
+    # Define a color map that matches the emotions to specific colors
+    color_map = {
+        'Neutral': '#B38B6D',  # Taupe
+        'Happy': '#FFFF00',    # Yellow
+        'Sad': '#0000FF',      # Blue
+        'Angry': '#FF0000',    # Red
+        'Disgust': '#008000',  # Green
+        'Surprise': '#FFA500', # Orange (Bright)
+        'Fear': '#000000'      # Black
+        # Add more emotions and their corresponding colors here
+    }
+
+    # Calculate the total number of faces analyzed
+    total_faces = len(selected_images)
+
+    # Use the color map to assign colors to the pie chart
+    pie_colors = [color_map.get(emotion, '#999999') for emotion in emotion_counts.index]  # Default to grey if not found
+
+    # Plot pie chart with total faces in the title
+    st.write("Emotion Distribution (Pie Chart):")
+    fig_pie, ax_pie = plt.subplots()
+    #font color
+    ax_pie.pie(emotion_counts, labels=emotion_counts.index, autopct='%1.1f%%', startangle=140, colors=pie_colors, textprops={'color': 'white', 'weight': 'bold'})
+
+    ax_pie.pie(emotion_counts, labels=emotion_counts.index, autopct='%1.1f%%', startangle=140, colors=pie_colors)
+    ax_pie.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
+    # Add total faces to the title
+    ax_pie.set_title(f"Total Faces Analyzed: {total_faces}")
+    st.pyplot(fig_pie)
+
+    # Use the same color map for the bar chart
+    bar_colors = [color_map.get(emotion, '#999999') for emotion in emotion_counts.index]  # Default to grey if not found
+
+    # Plot bar chart with total faces in the title
+    st.write("Emotion Distribution (Bar Chart):")
+    fig_bar, ax_bar = plt.subplots()
+    emotion_counts.plot(kind='bar', color=bar_colors, ax=ax_bar)
+    ax_bar.set_xlabel('Emotion')
+    ax_bar.set_ylabel('Count')
+    # Add total faces to the title
+    ax_bar.set_title(f"Emotion Distribution - Total Faces Analyzed: {total_faces}")
+    ax_bar.yaxis.set_major_locator(plt.MaxNLocator(integer=True))  # Ensure integer ticks on Y-axis
+    # Display bar values as integers
+    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')
+
+    st.pyplot(fig_bar)