Update app.py
Browse files
app.py
CHANGED
@@ -60,9 +60,9 @@ def plot_prediction(pred):
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with colB: st.pyplot()
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# Define function to plot the decomposed prediction
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def plot_decomposed(
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plt.figure(figsize=(4, 4))
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plt.imshow(
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plt.axis('off')
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with colC: st.pyplot()
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@@ -172,9 +172,6 @@ if uploaded_file is not None:
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plot_prediction(y_pred)
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# with colC:
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# st.markdown("""<style>[data-baseweb="select"] {margin-top: -36px;}</style>""", unsafe_allow_html=True)
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if decompose:
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y_pred = np.load("thresh.npy")
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@@ -188,7 +185,7 @@ if uploaded_file is not None:
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ccd.write(f"predicted_{i+1}.fits", overwrite=True)
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image_decomposed += (i+1) * np.where(cav > 0, 1, 0)
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shutil.make_archive("predictions.zip", 'zip', "predictions")
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np.save("decomposed.npy", image_decomposed)
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try: image_decomposed = np.load("decomposed.npy")
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with colB: st.pyplot()
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# Define function to plot the decomposed prediction
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def plot_decomposed(decomposed):
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plt.figure(figsize=(4, 4))
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plt.imshow(decomposed, origin="lower")
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plt.axis('off')
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with colC: st.pyplot()
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plot_prediction(y_pred)
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if decompose:
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y_pred = np.load("thresh.npy")
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ccd.write(f"predicted_{i+1}.fits", overwrite=True)
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image_decomposed += (i+1) * np.where(cav > 0, 1, 0)
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# shutil.make_archive("predictions.zip", 'zip', "predictions")
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np.save("decomposed.npy", image_decomposed)
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try: image_decomposed = np.load("decomposed.npy")
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