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1 Parent(s): d7dd53f

Update app.py

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  1. app.py +9 -83
app.py CHANGED
@@ -17,93 +17,19 @@ uploaded_images = st.file_uploader("Upload images", type=["jpg", "png"], accept_
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  # Display thumbnail images alongside file names and sizes in the sidebar
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  if uploaded_images:
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- uploaded_images_dict = {}
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- for img in uploaded_images:
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  image = Image.open(img)
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- # Check if checkbox for the current image has already been created
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- if img.name not in uploaded_images_dict:
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- st.sidebar.image(image, caption=f"{img.name} {img.size / 1024.0:.1f} KB", width=40)
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- uploaded_images_dict[img.name] = True
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- st.sidebar.checkbox(f"Select {img.name}", key=img.name)
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  # Collect selected images based on checkbox input
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  selected_images = []
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- for img in uploaded_images:
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- selected = st.sidebar.checkbox(f"Select {img.name}", value=False, key=img.name)
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- if selected:
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- selected_images.append(Image.open(img))
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-
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- if st.button("Predict Emotions") and selected_images:
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- if len(selected_images) == 2:
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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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-
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- # Display images and predicted emotions side by side
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- col1, col2 = st.columns(2)
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- for i in range(2):
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- predicted_class = results[i][0]["label"]
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- predicted_emotion = predicted_class.split("_")[-1].capitalize()
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- col = col1 if i == 0 else col2
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- col.image(selected_images[i], caption=f"Predicted emotion: {predicted_emotion}", use_column_width=True)
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- col.write(f"Emotion Scores: {predicted_emotion}: {results[i][0]['score']:.4f}")
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- col.write(f"Original File Name: {uploaded_images[i].name}") # Display original file name
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-
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- # Display the keys and values of all results
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- st.write("Keys and Values of all results:")
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- col1, col2 = st.columns(2)
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- for i, result in enumerate(results):
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- col = col1 if i == 0 else col2
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- col.write(f"Keys and Values of results[{i}]:")
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- for res in result:
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- label = res["label"]
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- score = res["score"]
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- col.write(f"{label}: {score:.4f}")
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-
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- else:
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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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-
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- # Display images and predicted emotions
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- for i, result in enumerate(results):
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- predicted_class = result[0]["label"]
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- predicted_emotion = predicted_class.split("_")[-1].capitalize()
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- st.image(selected_images[i], caption=f"Predicted emotion: {predicted_emotion}", use_column_width=True)
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- st.write(f"Emotion Scores for #{i+1} Image")
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- st.write(f"{predicted_emotion}: {result[0]['score']:.4f}")
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- st.write(f"Original File Name: {uploaded_images[i].name}") # Display original file name
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-
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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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-
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- # Create an image classification pipeline with scores
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- pipe = pipeline("image-classification", model="trpakov/vit-face-expression", top_k=None)
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-
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- # Streamlit app
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- st.title("Emotion Recognition with vit-face-expression")
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-
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- # Slider example
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- x = st.slider('Select a value')
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- st.write(f"{x} squared is {x * x}")
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-
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- # Upload images
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- uploaded_images = st.file_uploader("Upload images", type=["jpg", "png"], accept_multiple_files=True)
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-
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- # Display thumbnail images alongside file names and sizes in the sidebar
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- if uploaded_images:
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- uploaded_images_dict = {}
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- for img in uploaded_images:
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- image = Image.open(img)
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- # Check if checkbox for the current image has already been created
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- if img.name not in uploaded_images_dict:
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- st.sidebar.image(image, caption=f"{img.name} {img.size / 1024.0:.1f} KB", width=40)
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- uploaded_images_dict[img.name] = True
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- st.sidebar.checkbox(f"Select {img.name}", key=img.name)
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-
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- # Collect selected images based on checkbox input
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- selected_images = []
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- for img in uploaded_images:
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- selected = st.sidebar.checkbox(f"Select {img.name}", value=False, key=img.name)
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  if selected:
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  selected_images.append(Image.open(img))
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  # Display thumbnail images alongside file names and sizes in the sidebar
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  if uploaded_images:
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+ for idx, img in enumerate(uploaded_images):
 
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  image = Image.open(img)
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+ # Generate a unique key for each checkbox
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+ checkbox_key = f"{img.name}_{idx}"
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+ st.sidebar.image(image, caption=f"{img.name} {img.size / 1024.0:.1f} KB", width=40)
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+ st.sidebar.checkbox(f"Select {img.name}", value=False, key=checkbox_key)
 
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  # Collect selected images based on checkbox input
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  selected_images = []
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+ for idx, img in enumerate(uploaded_images):
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+ # Generate a unique key for each checkbox
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+ checkbox_key = f"{img.name}_{idx}"
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+ selected = st.sidebar.checkbox(f"Select {img.name}", value=False, key=checkbox_key)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  if selected:
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  selected_images.append(Image.open(img))
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