From 4fe85055ec9f2bf130e8fee9f513dbdb67519328 Mon Sep 17 00:00:00 2001
From: Jeremiah Lowin <153965+jlowin@users.noreply.github.com>
Date: Tue, 16 Jan 2024 22:48:20 -0500
Subject: [PATCH] Whitespace update

---
 README.md                          | 19 ++++++++++---------
 docs/docs/vision/captioning.md     |  3 ++-
 docs/docs/vision/transformation.md |  6 +++++-
 3 files changed, 17 insertions(+), 11 deletions(-)

diff --git a/README.md b/README.md
index adb6fb4bd..5b93690ac 100644
--- a/README.md
+++ b/README.md
@@ -96,12 +96,14 @@ Learn more about classification [here](https://askmarvin.ai/docs/text/classifica
 Marvin can `extract` structured entities from text:
 
 ```python
-from pydantic import BaseModel
+import pydantic
 
-class Location(BaseModel):
+
+class Location(pydantic.BaseModel):
     city: str
     state: str
 
+
 marvin.extract("I moved from NY to CHI", target=Location)
 
 # [
@@ -129,12 +131,11 @@ Learn more about entity extraction [here](https://askmarvin.ai/docs/text/extract
 Marvin can `generate` synthetic data for you, following instructions and an optional schema:
 
 ```python
-from pydantic import BaseModel
-
-class Location(BaseModel):
+class Location(pydantic.BaseModel):
     city: str
     state: str
 
+
 marvin.generate(
     n=4,
     target=Location,
@@ -164,12 +165,11 @@ marvin.cast("one two three", list[int])
 This is useful for standardizing text inputs or matching natural language to a schema:
 
 ```python
-from pydantic import BaseModel
-
-class Location(BaseModel):
+class Location(pydantic.BaseModel):
     city: str
     state: str
 
+
 marvin.cast("The Big Apple", Location)
 
 # Location(city="New York", state="New York")
@@ -179,10 +179,11 @@ For a class-based approach, Marvin's `@model` decorator can be applied to any Py
 
 ```python
 @marvin.model
-class Location(BaseModel):
+class Location(pydantic.BaseModel):
     city: str
     state: str
 
+
 Location("The Big Apple")
 
 # Location(city="New York", state="New York")
diff --git a/docs/docs/vision/captioning.md b/docs/docs/vision/captioning.md
index 3234caf7b..cf9abe54e 100644
--- a/docs/docs/vision/captioning.md
+++ b/docs/docs/vision/captioning.md
@@ -25,10 +25,11 @@ Marvin can use OpenAI's vision API to process images as inputs.
     import marvin
     from pathlib import Path
 
-    marvin.beta.caption(image=Path('/path/to/marvin.png'))
+    caption = marvin.beta.caption(image=Path('/path/to/marvin.png'))
     ```
 
     !!! success "Result"
+    
         "This is a digital illustration featuring a stylized, cute character resembling a Funko Pop vinyl figure with large, shiny eyes and a square-shaped head, sitting on abstract wavy shapes that simulate a landscape. The whimsical figure is set against a dark background with sparkling, colorful bokeh effects, giving it a magical, dreamy atmosphere."
     
 
diff --git a/docs/docs/vision/transformation.md b/docs/docs/vision/transformation.md
index 4d85ea46a..950354afc 100644
--- a/docs/docs/vision/transformation.md
+++ b/docs/docs/vision/transformation.md
@@ -41,10 +41,12 @@ The `marvin.beta.cast` function is an enhanced version of `marvin.cast` that acc
     import marvin
     from pydantic import BaseModel, Field
 
+
     class Location(BaseModel):
         city: str
         state: str = Field(description="2-letter state abbreviation")
-    
+
+
     img = marvin.beta.Image('https://images.unsplash.com/photo-1568515387631-8b650bbcdb90')
     result = marvin.beta.cast(img, target=Location)
     ```
@@ -67,10 +69,12 @@ The `marvin.beta.cast` function is an enhanced version of `marvin.cast` that acc
     import marvin
     from pydantic import BaseModel
 
+
     class Book(BaseModel):
         title: str
         subtitle: str
         authors: list[str]
+
     
     img = marvin.beta.Image('https://hastie.su.domains/ElemStatLearn/CoverII_small.jpg')
     result = marvin.beta.cast(img, target=Book)