xtlyxt commited on
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546add8
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1 Parent(s): 9d29736

Update app.py

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Files changed (1) hide show
  1. app.py +29 -13
app.py CHANGED
@@ -19,16 +19,32 @@ st.write(f"{x} squared is {x * x}")
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  uploaded_images = st.file_uploader("Upload images", type=["jpg", "png"], accept_multiple_files=True)
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  if st.button("Predict Emotions") and uploaded_images:
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- # Open the uploaded images
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- images = [Image.open(img) for img in uploaded_images]
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-
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- # Predict emotion for each image using the pipeline
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- results = [pipe(image) for image in 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(images[i], caption=f"Predicted emotion: {predicted_emotion}", use_column_width=True)
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- st.write(f"Emotion Scores for Image {i+1}:")
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- st.write(f"{predicted_emotion}: {result[0]['score']:.4f}")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  uploaded_images = st.file_uploader("Upload images", type=["jpg", "png"], accept_multiple_files=True)
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  if st.button("Predict Emotions") and uploaded_images:
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+ if len(uploaded_images) == 2:
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+ # Open the uploaded images
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+ images = [Image.open(img) for img in uploaded_images]
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+
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+ # Predict emotion for each image using the pipeline
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+ results = [pipe(image) for image in 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(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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+ else:
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+ # Open the uploaded images
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+ images = [Image.open(img) for img in uploaded_images]
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+
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+ # Predict emotion for each image using the pipeline
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+ results = [pipe(image) for image in 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(images[i], caption=f"Predicted emotion: {predicted_emotion}", use_column_width=True)
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+ st.write(f"Emotion Scores for Image {i+1}:")
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+ st.write(f"{predicted_emotion}: {result[0]['score']:.4f}")