Update app.py

app.py

@@ -20,15 +20,17 @@ st.set_option('deprecation.showPyplotGlobalUse', False)
 
 st.set_page_config(page_title="Cavity Detection Tool", layout="wide")
 
-# st.title("Cavity Detection Tool")
+st.title("Cavity Detection Tool")
 
-st.markdown("Cavity Detection Tool (CADET) is a machine learning pipeline trained to detect X-ray cavities from noisy Chandra images of early-type galaxies. To use this tool: upload your image, select the scale of interest, and make a prediction! If you use this tool for your research, please cite [Plšek et al. 2023](https://arxiv.org/abs/2304.05457)")
+_, col, _ = st.columns([1, 3, 1])
 
-st.markdown("Input images should be centered at the centre of the galaxy and point sources should be filled with surrounding background ([dmfilth](https://cxc.cfa.harvard.edu/ciao/ahelp/dmfilth.html)).")
-
-
-# Create file uploader widget
-uploaded_file = st.file_uploader("Choose a FITS file", type=['fits'])
+with col:
+    st.markdown("Cavity Detection Tool (CADET) is a machine learning pipeline trained to detect X-ray cavities from noisy Chandra images of early-type galaxies. To use this tool: upload your image, select the scale of interest, and make a prediction! If you use this tool for your research, please cite [Plšek et al. 2023](https://arxiv.org/abs/2304.05457)")
+    
+    st.markdown("Input images should be centered at the centre of the galaxy and point sources should be filled with surrounding background ([dmfilth](https://cxc.cfa.harvard.edu/ciao/ahelp/dmfilth.html)).")
+    
+    # Create file uploader widget
+    uploaded_file = st.file_uploader("Choose a FITS file", type=['fits'])
 
 # Define function to plot the uploaded image
 def plot_image(image, scale):
@@ -104,56 +106,56 @@ if uploaded_file is not None:
         data = hdul[0].data
         wcs = WCS(hdul[0].header)
     
-    # Make four columns for buttons
-    col1, col2, col3, col4 = st.columns(4)
-    col1.subheader("Input image")
-    col3.subheader("Prediction")
+    # # Make four columns for buttons
+    # col1, col2, col3, col4 = st.columns(4)
+    # col1.subheader("Input image")
+    # col3.subheader("Prediction")
     
-    with col1:
-        st.markdown("""<style>[data-baseweb="select"] {margin-top: 16px;}</style>""", unsafe_allow_html=True)
-        max_scale = int(data.shape[0] // 128)
-        # scale = int(st.selectbox('Scale:',[i+1 for i in range(max_scale)], label_visibility="hidden"))
-        scale = int(st.selectbox('Scale:',[f"{(i+1)*128}x{(i+1)*128}" for i in range(max_scale)], label_visibility="hidden"))
-        scale = scale.split("x")[0] // 128
+    # with col1:
+    #     st.markdown("""<style>[data-baseweb="select"] {margin-top: 16px;}</style>""", unsafe_allow_html=True)
+    #     max_scale = int(data.shape[0] // 128)
+    #     # scale = int(st.selectbox('Scale:',[i+1 for i in range(max_scale)], label_visibility="hidden"))
+    #     scale = int(st.selectbox('Scale:',[f"{(i+1)*128}x{(i+1)*128}" for i in range(max_scale)], label_visibility="hidden"))
+    #     scale = scale.split("x")[0] // 128
     
-    with col2:
-        detect = st.button('Detect cavities')
+    # with col2:
+    #     detect = st.button('Detect cavities')
 
-    with col3:
-        decompose = st.button('Docompose cavities')
+    # with col3:
+    #     decompose = st.button('Docompose cavities')
         
-    # Make two columns for plots
-    colA, colB = st.columns(2)
+    # # Make two columns for plots
+    # colA, colB = st.columns(2)
 
-    image = np.log10(data+1)
-    plot_image(image, scale)
+    # image = np.log10(data+1)
+    # plot_image(image, scale)
     
-    if detect:
-        data, wcs = cut(data, wcs, scale=scale)
-        image = np.log10(data+1)
+    # if detect:
+    #     data, wcs = cut(data, wcs, scale=scale)
+    #     image = np.log10(data+1)
         
-        y_pred = 0
-        for j in [0,1,2,3]:
-            rotated = np.rot90(image, j)
-            pred = model.predict(rotated.reshape(1, 128, 128, 1)).reshape(128 ,128)
-            pred = np.rot90(pred, -j)
-            y_pred += pred / 4
-
-        # Thresholding
-        y_pred = np.where(y_pred > 0.4, y_pred, 0)
-
-        # if decompose:
-        #     cavs = decompose_cavity(y_pred, )
+    #     y_pred = 0
+    #     for j in [0,1,2,3]:
+    #         rotated = np.rot90(image, j)
+    #         pred = model.predict(rotated.reshape(1, 128, 128, 1)).reshape(128 ,128)
+    #         pred = np.rot90(pred, -j)
+    #         y_pred += pred / 4
+
+    #     # Thresholding
+    #     y_pred = np.where(y_pred > 0.4, y_pred, 0)
+
+    #     # if decompose:
+    #     #     cavs = decompose_cavity(y_pred, )
         
-        plot_prediction(y_pred, decompose)
+    #     plot_prediction(y_pred, decompose)
 
-        ccd = CCDData(y_pred, unit="adu", wcs=wcs)
-        ccd.write("predicted.fits", overwrite=True)
-        with open('predicted.fits', 'rb') as f:
-            res = f.read()
+    #     ccd = CCDData(y_pred, unit="adu", wcs=wcs)
+    #     ccd.write("predicted.fits", overwrite=True)
+    #     with open('predicted.fits', 'rb') as f:
+    #         res = f.read()
         
-        with col4:
-            pass
-            st.markdown("""<style>[data-baseweb="select"] {margin-top: 16px;}</style>""", unsafe_allow_html=True)
-        #     # download = st.button('Download')
-            download = st.download_button(label="Download", data=res, file_name="predicted.fits", mime="application/octet-stream")
+    #     with col4:
+    #         pass
+    #         st.markdown("""<style>[data-baseweb="select"] {margin-top: 16px;}</style>""", unsafe_allow_html=True)
+    #     #     # download = st.button('Download')
+    #         download = st.download_button(label="Download", data=res, file_name="predicted.fits", mime="application/octet-stream")