Update app.py

app.py

@@ -27,33 +27,36 @@ from huggingface_hub import from_pretrained_keras
 
 # Define function to plot the uploaded image
 def plot_image(image, scale):
-    plt.figure(figsize=(4, 4))
-    x0 = image.shape[0] // 2 - scale * 128 / 2
-    plt.imshow(image, origin="lower")
-    plt.gca().add_patch(Rectangle((x0-0.5, x0-0.5), scale*128, scale*128, linewidth=1, edgecolor='w', facecolor='none'))
-    plt.axis('off')
-    plt.tight_layout()
-    with colA: st.pyplot()
+    # plt.figure(figsize=(4, 4))
+    # x0 = image.shape[0] // 2 - scale * 128 / 2
+    # plt.imshow(image, origin="lower")
+    # plt.gca().add_patch(Rectangle((x0-0.5, x0-0.5), scale*128, scale*128, linewidth=1, edgecolor='w', facecolor='none'))
+    # plt.axis('off')
+    # plt.tight_layout()
+    # with colA: st.pyplot()
+    with colA: st.image(image)
 
 # Define function to plot the prediction
 def plot_prediction(pred):
-    plt.figure(figsize=(4, 4))
-    plt.imshow(pred, origin="lower", norm=Normalize(vmin=0, vmax=1))
-    plt.axis('off')
-    with colB: st.pyplot()
+    # plt.figure(figsize=(4, 4))
+    # plt.imshow(pred, origin="lower", norm=Normalize(vmin=0, vmax=1))
+    # plt.axis('off')
+    # with colB: st.pyplot()
+    with colB: st.image(pred)
 
 # Define function to plot the decomposed prediction
 def plot_decomposed(decomposed):
-    plt.figure(figsize=(4, 4))
-    plt.imshow(decomposed, origin="lower")
-    N = int(np.max(decomposed))
-    for i in range(N):
-        new = np.where(decomposed == i+1, 1, 0)
-        x0, y0 = center_of_mass(new)
-        color = "white" if i < N//2 else "black"
-        plt.text(y0, x0, f"{i+1}", ha="center", va="center", fontsize=15, color=color)
-    plt.axis('off')
-    with colC: st.pyplot()
+    # plt.figure(figsize=(4, 4))
+    # plt.imshow(decomposed, origin="lower")
+    # N = int(np.max(decomposed))
+    # for i in range(N):
+    #     new = np.where(decomposed == i+1, 1, 0)
+    #     x0, y0 = center_of_mass(new)
+    #     color = "white" if i < N//2 else "black"
+    #     plt.text(y0, x0, f"{i+1}", ha="center", va="center", fontsize=15, color=color)
+    # plt.axis('off')
+    # with colC: st.pyplot()
+    with colC: st.image(decomposed)
 
 # Define function to cut input image and rebin it to 128x128 pixels
 def cut(data0, wcs0, scale=1):