app.py

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)


# Generate DataFrame button
if st.button("Generate DataFrame") and selected_images:
    # Create a list to store data for DataFrame
    df_data = []

    # Iterate through selected images to gather data
    for image, file_name, result in zip(selected_images, selected_file_names, results):
        # Extract image metadata
        size_kb = image.size[0] * image.size[1] / 1024.0  # Calculating size in KB
        timestamp = datetime.datetime.now()  # Current timestamp
        color_type = "Color" if len(image.getbands()) > 1 else "Grayscale"
        
        # Extract predicted emotions and scores
        emotion_scores = {res["label"].split("_")[-1].capitalize(): res["score"] for res in result}

        # Append image metadata and emotion scores to the list
        df_data.append({
            "Neutral": f"{emotion_scores.get('Neutral', 0.0):.4f}",
            "Happy": f"{emotion_scores.get('Happy', 0.0):.4f}",
            "Sad": f"{emotion_scores.get('Sad', 0.0):.4f}",
            "Angry": f"{emotion_scores.get('Angry', 0.0):.4f}",
            "Disgust": f"{emotion_scores.get('Disgust', 0.0):.4f}",
            "Surprise": f"{emotion_scores.get('Surprise', 0.0):.4f}",
            "Fear": f"{emotion_scores.get('Fear', 0.0):.4f}",
            "File Name": file_name,
            "Size (KB)": size_kb,
            "Timestamp": timestamp,
            "Color Type": color_type
        })

    # Create DataFrame
    df = pd.DataFrame(df_data)

    # Display DataFrame
    st.write(df)