testing embedding html vids

app.py

@@ -39,6 +39,15 @@ def get_image_data():
 
 def segmentation_generation(image_name, c_width, n_top, n_gif_imgs):
     print("GRADIO INPUTS:", image_name, c_width, n_top, n_gif_imgs)
+
+    html =  '''   
+        <div style='max-width:100%; max-height:360px; overflow:auto'>
+            <video width="320" height="240" autoplay>
+                <source src="./test.mp4" type=video/mp4>
+            </video>
+        </div>''' 
+    return html
+
     cred_width = c_width
     n_top_segs = n_top
     n_gif_images = n_gif_imgs

bayes/data_routines.py

@@ -38,7 +38,7 @@ def get_and_preprocess_compas_data():
     POSITIVE_OUTCOME = PARAMS['positive_outcome']
     NEGATIVE_OUTCOME = PARAMS['negative_outcome']
 
-    compas_df = pd.read_csv("../data/compas-scores-two-years.csv", index_col=0)
+    compas_df = pd.read_csv("./data/compas-scores-two-years.csv", index_col=0)
     compas_df = compas_df.loc[(compas_df['days_b_screening_arrest'] <= 30) &
                               (compas_df['days_b_screening_arrest'] >= -30) &
                               (compas_df['is_recid'] != -1) &
@@ -87,7 +87,7 @@ def get_and_preprocess_german():
     POSITIVE_OUTCOME = PARAMS['positive_outcome']
     NEGATIVE_OUTCOME = PARAMS['negative_outcome']
 
-    X = pd.read_csv("../data/german_processed.csv")
+    X = pd.read_csv("./data/german_processed.csv")
     y = X["GoodCustomer"]
 
     X = X.drop(["GoodCustomer", "PurposeOfLoan"], axis=1)
@@ -172,7 +172,7 @@ def get_mnist(num):
     """
 
     # Get the mnist data
-    test_loader = torch.utils.data.DataLoader(datasets.MNIST('../data/mnist', 
+    test_loader = torch.utils.data.DataLoader(datasets.MNIST('./data/mnist', 
                                                              train=False, 
                                                              download=True, 
                                                              transform=transforms.Compose([transforms.ToTensor(),

image_posterior.py

@@ -64,7 +64,7 @@ def create_gif(explanation_blr, segments, image, n_images=20, n_max=5):
             plt.imshow(c_image, alpha=0.3)
             paths.append(os.path.join(tmpdirname, f"{i}.png"))
             plt.savefig(paths[-1])
-
+        print("CREATING VIDEO NOW")
         # Save to gif
         # https://stackoverflow.com/questions/61716066/creating-an-animation-out-of-matplotlib-pngs
         # print(f"Saving gif to {save_loc}")