Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| from PIL import Image | |
| from transformers import pipeline | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| from pandas.plotting import parallel_coordinates | |
| # Initialize session state for results, image names, and image sizes if not already present | |
| if 'results' not in st.session_state: | |
| st.session_state['results'] = [] | |
| if 'image_names' not in st.session_state: | |
| st.session_state['image_names'] = [] | |
| if 'image_sizes' not in st.session_state: | |
| st.session_state['image_sizes'] = [] | |
| # 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) | |
| # Display thumbnail images alongside file names and sizes in the sidebar | |
| selected_images = [] | |
| if uploaded_images: | |
| # Reset the image names and sizes lists each time new images are uploaded | |
| st.session_state['image_names'] = [img.name for img in uploaded_images] | |
| st.session_state['image_sizes'] = [round(img.size / 1024.0, 1) for img in 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=select_all, key=checkbox_key) | |
| if selected: | |
| selected_images.append(image) | |
| if st.button("Predict Emotions") and selected_images: | |
| # Predict emotion for each selected image using the pipeline | |
| st.session_state['results'] = [pipe(image) for image in selected_images] | |
| # Generate DataFrame from results | |
| if st.button("Generate HeatMap & DataFrame"): | |
| # Access the results, image names, and sizes from the session state | |
| results = st.session_state['results'] | |
| image_names = st.session_state['image_names'] | |
| image_sizes = st.session_state['image_sizes'] | |
| if results: | |
| # Initialize an empty list to store all the data | |
| data = [] | |
| # Iterate over the results and populate the list with dictionaries | |
| for i, result_set in enumerate(results): | |
| # Initialize a dictionary for the current set with zeros | |
| current_data = { | |
| 'Happy': 0, | |
| 'Surprise': 0, | |
| 'Neutral': 0, | |
| 'Sad': 0, | |
| 'Disgust': 0, | |
| 'Angry': 0, | |
| 'Fear': 0, | |
| # Add other emotions if necessary | |
| 'Image Name': image_names[i], | |
| #'Image Size (KB)': image_sizes[i] | |
| 'Image Size (KB)': f"{image_sizes[i]:.1f}" # Format the size to one decimal place | |
| } | |
| for result in result_set: | |
| # Capitalize the label and update the score in the current set | |
| emotion = result['label'].capitalize() | |
| score = round(result['score'], 4) # Round the score to 4 decimal places | |
| current_data[emotion] = score | |
| # Append the current data to the data list | |
| data.append(current_data) | |
| # Convert the list of dictionaries into a pandas DataFrame | |
| df_emotions = pd.DataFrame(data) | |
| # Display the DataFrame | |
| st.write(df_emotions) | |
| # Plotting the heatmap for the first seven columns | |
| plt.figure(figsize=(10, 10)) | |
| sns.heatmap(df_emotions.iloc[:, :7], annot=True, fmt=".1f", cmap='viridis') | |
| plt.title('Heatmap of Emotion Scores') | |
| plt.xlabel('Emotion Categories') | |
| plt.ylabel('Data Points') | |
| st.pyplot(plt) | |
| # Optional: Save the DataFrame to a CSV file | |
| df_emotions.to_csv('emotion_scores.csv', index=False) | |
| st.success('DataFrame generated and saved as emotion_scores.csv') | |
| with open('emotion_scores.csv', 'r') as f: | |
| csv_file = f.read() | |
| st.download_button( | |
| label='Download Emotion Scores as CSV', | |
| data=csv_file, | |
| file_name='emotion_scores.csv', | |
| mime='text/csv', | |
| ) | |
| st.success('DataFrame generated and available for download.') | |
| else: | |
| st.error("No results to generate DataFrame. Please predict emotions first.") | |