refactor: user facing app code.

app.py

@@ -9,6 +9,8 @@ import gradio as gr
 from datasets import load_dataset
 from transformers import AutoModel
 
+# `LSH` and `Table` imports are necessary in order for the
+# `lsh.pickle` file to load successfully.
 from similarity_utils import LSH, BuildLSHTable, Table
 
 seed = 42
@@ -45,11 +47,8 @@ def query(image, top_k):
         candidates.append(candidate_dataset[int(image_id)]["image"])
         labels.append(f"Label: {label}")
 
-    candidates.insert(0, image)
-    labels.insert(0, "Query Image")
-
     for i, candidate in enumerate(candidates):
-        filename = f"{i}.png"
+        filename = f"similar_{i}.png"
         candidate.save(filename)
         images.append(filename)
 
@@ -58,12 +57,18 @@ def query(image, top_k):
     return list(zip(images, labels))
 
 
+title = "Fetch similar beans."
+description = "This Space demos an image similarity system. You can refer to [this notebook](TODO) to know the details of the system. You can pick any image from the available samples below. On the right hand side, you'll find the similar images returned"
+" by the system. The example images have been named with their corresponding integer class labels for easier identification. The fetched images will also have their integer labels tagged so that you can validate the correctness of the results."
+
 # You can set the type of gr.Image to be PIL, numpy or str (filepath)
 # Not sure what the best for this demo is.
 gr.Interface(
     query,
     inputs=[gr.Image(type="pil"), gr.Slider(value=5, minimum=1, maximum=10, step=1)],
-    outputs=gr.Gallery(),
+    outputs=gr.Gallery().style(grid=[3], height="auto"),
     # Filenames denote the integer labels. Know here: https://hf.co/datasets/beans
+    title=title,
+    description=description,
     examples=[["0.png", 5], ["1.png", 5], ["2.png", 5]],
 ).launch()