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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 | |
| # Configuration to suppress warnings in Streamlit | |
| st.set_option('deprecation.showPyplotGlobalUse', False) | |
| # Initialize the image classification pipeline with a specific model | |
| pipe = pipeline("image-classification", model="trpakov/vit-face-expression") | |
| # Setting the title of the Streamlit application | |
| st.title("Emotion Recognition App by Prateek Mohan") | |
| # Interface for uploading an image | |
| uploaded_image = st.file_uploader("Please upload an image", type=["jpg", "png"], accept_multiple_files=False) | |
| color_map = { | |
| 'Neutral': '#B38B6D', | |
| 'Happy': '#FFFF00', | |
| 'Sad': '#0000FF', | |
| 'Angry': '#FF0000', | |
| 'Disgust': '#008000', | |
| 'Surprise': '#FFA500', | |
| 'Fear': '#000000' | |
| } | |
| # Process the image immediately after upload | |
| if uploaded_image: | |
| # Load and display the image | |
| image = Image.open(uploaded_image) | |
| st.image(image, caption="Uploaded Image", use_column_width=True) | |
| # Emotion prediction using the pre-trained model | |
| result = pipe(image) | |
| predicted_class = result[0]["label"] | |
| predicted_emotion = predicted_class.split("_")[-1].capitalize() | |
| emotion_score = result[0]["score"] | |
| # Displaying the predicted emotion and its confidence score | |
| st.write(f"Predicted Emotion: {predicted_emotion}") | |
| st.write(f"Confidence Score: {emotion_score:.4f}") | |
| # Data preparation for visualization | |
| emotion_counts = pd.Series([predicted_emotion]) | |
| # Color mapping for different emotions | |
| color_map = { | |
| 'Neutral': '#B38B6D', | |
| 'Happy': '#FFFF00', | |
| 'Sad': '#0000FF', | |
| 'Angry': '#FF0000', | |
| 'Disgust': '#008000', | |
| 'Surprise': '#FFA500', | |
| 'Fear': '#000000' | |
| } | |
| pie_colors = [color_map.get(emotion, '#999999') for emotion in emotion_counts.index] | |
| # Pie chart visualization | |
| 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("Emotion Distribution") | |
| st.pyplot(fig_pie) | |
| # Bar chart visualization | |
| 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("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, str(int(i.get_height())), ha='center', va='bottom') | |
| st.pyplot(fig_bar) |