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Create app.py
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app.py
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import streamlit as st
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from PIL import Image
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from transformers import pipeline
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import pandas as pd
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import matplotlib.pyplot as plt
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# Set Streamlit configuration to disable deprecation warnings
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st.set_option('deprecation.showPyplotGlobalUse', False)
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# Initialize the image classification pipeline with the specified model
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pipe = pipeline("image-classification", model="trpakov/vit-face-expression")
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# Set the title of the Streamlit app
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st.title("Emotion Recognition with vit-face-expression")
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# Create a file uploader to upload images in JPG or PNG format
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uploaded_images = st.file_uploader("Upload images", type=["jpg", "png"], accept_multiple_files=True)
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# List to store selected file names
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selected_file_names = []
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# List to store selected images
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selected_images = []
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# Process uploaded images if any
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if uploaded_images:
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# Add a "Select All" checkbox in the sidebar for convenience
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select_all = st.sidebar.checkbox("Select All", False)
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# Iterate over each uploaded image
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for idx, img in enumerate(uploaded_images):
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image = Image.open(img)
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checkbox_key = f"{img.name}_checkbox_{idx}" # Unique key for each checkbox
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# Display thumbnail image and checkbox in sidebar
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st.sidebar.image(image, caption=f"{img.name} ({img.size / 1024.0:.1f} KB)", width=40)
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selected = st.sidebar.checkbox(f"Select {img.name}", value=select_all, key=checkbox_key)
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# Add selected images to the list
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if selected:
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selected_images.append(image)
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selected_file_names.append(img.name)
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# Button to start emotion prediction
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if st.button("Predict Emotions") and selected_images:
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# Predict emotion for each selected image using the pipeline
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results = [pipe(image) for image in selected_images]
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emotions = [result[0]["label"].split("_")[-1].capitalize() for result in results]
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# Display images and predicted emotions
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for i, (image, result) in enumerate(zip(selected_images, results)):
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st.image(image, caption=f"Predicted emotion: {emotions[i]}", use_column_width=True)
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st.write(f"Emotion Scores for Image #{i+1}")
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st.write(f"{emotions[i]}: {result[0]['score']:.4f}")
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st.write(f"Original File Name: {selected_file_names[i]}")
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# Calculate emotion statistics
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emotion_counts = pd.Series(emotions).value_counts()
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total_faces = len(selected_images)
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# Define a color map for emotions
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color_map = {
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'Neutral': '#B38B6D',
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'Happy': '#FFFF00',
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'Sad': '#0000FF',
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'Angry': '#FF0000',
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'Disgust': '#008000',
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'Surprise': '#FFA500',
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'Fear': '#000000'
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}
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# Plot pie chart for emotion distribution
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st.write("Emotion Distribution (Pie Chart):")
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fig_pie, ax_pie = plt.subplots()
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pie_colors = [color_map.get(emotion, '#999999') for emotion in emotion_counts.index]
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ax_pie.pie(emotion_counts, labels=emotion_counts.index, autopct='%1.1f%%', startangle=140, colors=pie_colors)
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ax_pie.axis('equal')
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ax_pie.set_title(f"Total Faces Analyzed: {total_faces}")
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st.pyplot(fig_pie)
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# Plot bar chart for emotion distribution
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st.write("Emotion Distribution (Bar Chart):")
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fig_bar, ax_bar = plt.subplots()
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bar_colors = [color_map.get(emotion, '#999999') for emotion in emotion_counts.index]
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emotion_counts.plot(kind='bar', color=bar_colors, ax=ax_bar)
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ax_bar.set_xlabel('Emotion')
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ax_bar.set_ylabel('Count')
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ax_bar.set_title(f"Emotion Distribution - Total Faces Analyzed: {total_faces}")
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ax_bar.yaxis.set_major_locator(plt.MaxNLocator(integer=True))
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for i in ax_bar.patches:
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ax_bar.text(i.get_x() + i.get_width() / 2, i.get_height() + 0.1, int(i.get_height()), ha='center', va='bottom')
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st.pyplot(fig_bar)
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