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facebasic / app (13)-w1-full working.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)