Upload app.py

app.py

@@ -75,15 +75,6 @@ than the query "red fox, score:7".
 The "not-for-all-audience" tag informs users that this tool's text output is derived from e621.net data for tag prediction and completion.
 The app will try not to display nsfw tags unless the "Allow NSFW Tags" is checked, but the filter is not perfect.
 
-## How is the artist list calculated?
-
-Each artist is represented by a "pseudo-document" composed of all the tags from their uploaded images, treating these tags similarly to words in a text document. 
-Similarly, when you input a set of tags, the system creates a pseudo-document for your query out of all the tags. 
-It then uses a technique called cosine similarity to compare your tags against each artist's collection, essentially finding which artist's tags are most "similar" to yours.
-This method helps identify artists whose work is closely aligned with the themes or elements you're interested in.
-For those curious about the underlying mechanics of comparing text-like data, we employ the TF-IDF (Term Frequency-Inverse Document Frequency) method, a standard approach in information retrieval. 
-You can read more about TF-IDF on its [Wikipedia page](https://en.wikipedia.org/wiki/Tf%E2%80%93idf).
-
 ## How does the tag corrector work?
 
 We collect the tag sets from over 4 million e621 posts, treating the tag set from each image as an individual document.
@@ -98,7 +89,23 @@ Using the same 4 million post dataset, we calculate the conditional probability
 This is done by creating a co-occurrence matrix from our dataset, which records how frequently each pair of tags appears together across all documents.
 By considering the context in which tags are used, we can now not only correct misspellings and rephrasings but also make more contextually relevant suggestions.
 The "similarity weight" slider controls how much weight these conditional probabilities are given vs how much weight the FastText similarity model is given when suggesting replacements for invalid tags.
-A similarity weight slider value of 0 means that only the FastText model's predictions will be used to calculate similarity scores, and a value of 1 means only the conditional probabilities are used (although the FastText model is still used to trim the list of candidates).  
+A similarity weight slider value of 0 means that only the FastText model's predictions will be used to calculate similarity scores, and a value of 1 means only the conditional probabilities are used (although the FastText model is still used to trim the list of candidates).
+
+## How is the artist list calculated?
+
+Each artist is represented by a "pseudo-document" composed of all the tags from their uploaded images, treating these tags similarly to words in a text document. 
+Similarly, when you input a set of tags, the system creates a pseudo-document for your query out of all the tags. 
+It then uses a technique called cosine similarity to compare your tags against each artist's collection, essentially finding which artist's tags are most "similar" to yours.
+This method helps identify artists whose work is closely aligned with the themes or elements you're interested in.
+For those curious about the underlying mechanics of comparing text-like data, we employ the TF-IDF (Term Frequency-Inverse Document Frequency) method, a standard approach in information retrieval. 
+You can read more about TF-IDF on its [Wikipedia page](https://en.wikipedia.org/wiki/Tf%E2%80%93idf).  
+
+## How do the sample images work?
+
+For each artist in the dataset, we generated a sample image with the model Fluffyrock Unleashed using the prompt "by artist, soyjak, anthro, male, bust portrait, meme, grin".
+The simplicity of the prompt, the the simplicty of the default style, and the recognizability of the character make it easier to understand how artist names affect generated image styles.
+The first image returned is a baseline, generated with the same prompt, but with no artist name.
+You should compare all the images to the first to see how the artist names affect the output.
 """
 
 
@@ -460,7 +467,7 @@ with gr.Blocks() as app:
             top_artists = gr.Textbox(label="Top Artists", info="These are the artists most strongly associated with your tags. The number in parentheses is a similarity score between 0 and 1, with higher numbers indicating greater similarity.")
             dynamic_prompts = gr.Textbox(label="Dynamic Prompts Format", info="For if you're using the Automatic1111 webui (https://github.com/AUTOMATIC1111/stable-diffusion-webui) with the Dynamic Prompts extension activated (https://github.com/adieyal/sd-dynamic-prompts) and want to try them all individually.")
         with gr.Column(scale=3):
-            styles = gr.Gallery(label="Styles", info="This is what Fluffyrock Unleashed, an e621 model present in many mixes, thinks the recommended artists' styles look like.", allow_preview=True, preview=True)
+            styles = gr.Gallery(label="Styles", allow_preview=True, preview=True)
     
     
     submit_button.click(