cleaning up hardcoded aspects of code

app.py

@@ -16,43 +16,25 @@ from bayes.data_routines import get_dataset_by_name
 from bayes.models import *
 from image_posterior import create_gif	
 
-parser = argparse.ArgumentParser()
-parser.add_argument("--cred_width", type=float, default=0.1)
-parser.add_argument("--save_loc", type=str, required=True)
-parser.add_argument("--n_top_segs", type=int, default=5)
-parser.add_argument("--n_gif_images", type=int, default=20)
-
-# app = flask.Flask(__name__, template_folder="./")
-
-IMAGE_NAME = "imagenet_diego"
 BLENHEIM_SPANIEL_CLASS = 156
 
 
-def get_image_data():
+def get_image_data(image_name):
     """Gets the image data and model."""
-    puppy_image = get_dataset_by_name(IMAGE_NAME, get_label=False)
-    print("IMAGE RETURNED FROM GETTING DATASET:\n", puppy_image)
-    model_and_data = process_imagenet_get_model(puppy_image)
-    print("MODEL RETURNED FROM PROCESSING IMAGE:\n", model_and_data)
-    return puppy_image, model_and_data
+    if (image_name == "imagenet_diego.png"): 
+        image = get_dataset_by_name("imagenet_diego", get_label=False)
+        model_and_data = process_imagenet_get_model(image)
+
+    return image, model_and_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=\"background-image: url(./imagenet_diego.png); height: 400px; width: 400px;\"></div>"
-
-    html = (
-        "<div >"
-        "<img  src='file/diego.gif' alt='picture of dog'/>"
-        + "</div>"
-    )
-    return html
-
     cred_width = c_width
     n_top_segs = n_top
     n_gif_images = n_gif_imgs
-    puppy_image, model_and_data = get_image_data()
+    image, model_and_data = get_image_data(image_name)
 
     # Unpack datax
     xtest = model_and_data["xtest"]
@@ -60,6 +42,7 @@ def segmentation_generation(image_name, c_width, n_top, n_gif_imgs):
     segs = model_and_data["xtest_segs"]
     get_model = model_and_data["model"]
     label = model_and_data["label"]
+    print("LABEL:", label)
 
     # Unpack instance and segments
     instance = xtest[0]
@@ -88,7 +71,7 @@ def segmentation_generation(image_name, c_width, n_top, n_gif_imgs):
                             l2=False)
 
     # Create the gif of the explanation
-    return create_gif(rout['blr'], segments, instance, n_gif_images, n_top_segs)
+    return create_gif(rout['blr'], image_name, segments, instance, n_gif_images, n_top_segs)
 
 def image_mod(image):
     return image.rotate(45)
@@ -96,8 +79,8 @@ def image_mod(image):
 if __name__ == "__main__":
     # gradio's image inputs look like this: <PIL.Image.Image image mode=RGB size=305x266 at 0x7F3D01C91FA0>
     # need to learn how to handle image inputs, or deal with file inputs or just file path strings
-    inp = gr.inputs.Textbox(lines=1, placeholder="Insert file path here", default="", label="Input Image Path", optional=False)
-    out = gr.outputs.HTML(label="Output Video")
+    inp = gr.inputs.Textbox(lines=1, placeholder="Select an example from below", default="", label="Input Image Path", optional=False)
+    out = gr.outputs.HTML(label="Output GIF")
 
     iface = gr.Interface(
         segmentation_generation, 
@@ -108,8 +91,6 @@ if __name__ == "__main__":
             gr.inputs.Slider(minimum=10, maximum=50, step=1, default=20, label="n_gif_images", optional=False), 
         ], 
         outputs=out, 
-        examples=[["./imagenet_diego.png", 0.05, 7, 50]]
+        examples=[["imagenet_diego.png", 0.01, 7, 50]]
     )
-    iface.launch(enable_queue = True)
-
-    # app.run(host='0.0.0.0',  port=int(os.environ.get('PORT', 7860)))
+    iface.launch(show_error=True, enable_queue=True)

image_posterior.py

@@ -41,7 +41,7 @@ def fill_segmentation(values, segmentation, image, n_max=5):
             c_image[segmentation == i, c] = np.max(image)
     return c_image.astype(int), out.astype(int)
 
-def create_gif(explanation_blr, segments, image, n_images=20, n_max=5):
+def create_gif(explanation_blr, img_name, segments, image, n_images=20, n_max=5):
     """Create the gif corresponding to the image explanation.
 
     Arguments:
@@ -64,25 +64,20 @@ 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")
+        print("CREATING GIF NOW")
         # Save to gif
         # https://stackoverflow.com/questions/61716066/creating-an-animation-out-of-matplotlib-pngs
         # print(f"Saving gif to {save_loc}")
 
         ims = [imageio.imread(f) for f in paths]
-        writer = imageio.get_writer('./test.mp4', fps = 10)
-        for im in ims: 
-            writer.append_data(im)
-        writer.close()
-        
+        imageio.mimwrite(f'{img_name}_explanation.gif', ims)
+
+    html = (
+        "<div >"
+        f"<img  src='file/{img_name}_explanation.gif' alt='explanation gif'/>"
+        + "</div>"
+    )
+    return html
+
         
-        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
-        # return imageio.mimwrite(imageio.RETURN_BYTES, ims)