Update smolagents-can-see.md (#2618)

fix: correct grammar, usage, and article omissions in smolagents-can-see.md

- Inserted the definite article "the" before references to "ReAct framework" and "singular `MultiStepAgent` class," ensuring proper article usage.
  - Rationale: “ReAct framework” and “singular `MultiStepAgent` class” are specific concepts, requiring “the” for clarity.

- Fixed subject–verb agreement in the clause describing how variables and knowledge are incorporated into agent logs.
  - Rationale: Since “variables and knowledge” are plural, the verb must match that plurality.

- Added the missing “d” in “defined above” (“define” → “defined”).
  - Rationale: Simple past participle form was needed for correct grammar.

- Replaced “the glimpse” with “a glimpse” to fix article usage before a non-specific noun.
  - Rationale: “Glimpse” is an indefinite instance, so “a” is the more appropriate article.

- Changed “looking forward to see” to “looking forward to seeing.”
  - Rationale: “Looking forward to” is followed by a gerund (“seeing”) rather than an infinitive.

"Woe to you, scribes of the keyboard and Pharisees of the syntax, for you tithe mint and dill and cumin but neglect the weightier matters of proofreading! Blind guides, straining out a gnat but swallowing a camel."
(Matthew 23:23-24)

Author: CharlesCNorton

smolagents-can-see.md

@@ -54,7 +54,7 @@ How to dynamically add images into the agent’s memory?
 
 To find out, we first need to understand how our agents work.
 
-All agents in `smolagents` are based on singular `MultiStepAgent` class, which is an abstraction of ReAct framework. On a basic level, this class performs actions on a cycle of following steps, where existing variables and knowledge is incorporated into the agent logs as follows: 
+All agents in `smolagents` are based on the singular `MultiStepAgent` class, which is an abstraction of the ReAct framework. On a basic level, this class performs actions on a cycle of following steps, where existing variables and knowledge are incorporated into the agent logs as follows: 
 
 - **Initialization:** the system prompt is stored in a `SystemPromptStep`, and the user query is logged into a `TaskStep`.
 - **ReAct Loop (While):**
@@ -219,7 +219,7 @@ model = OpenAIServerModel(
 ```
 
 Now let’s move on to defining our agent. We set the highest `verbosity_level` to display the LLM’s full output messages to view its thoughts, and we increased `max_steps` to 20 to give the agent more steps to explore the web.
-We also provide it with our callback `save_screenshot` define above.
+We also provide it with our callback `save_screenshot` defined above.
 
 ```python
 agent = CodeAgent(
@@ -313,10 +313,10 @@ Note, however, that this task is really hard: depending on the VLM that you use,
 
 ## Next Steps
 
-This will give you the glimpse of the capabilities of a vision-enabled `CodeAgent`, but there’s much more to do!
+This will give you a glimpse of the capabilities of a vision-enabled `CodeAgent`, but there’s much more to do!
 
 - You can get started with the agentic web browser [here](https://github.com/huggingface/smolagents/blob/main/examples/vlm_web_browser.py).
 - Read more about smolagents [in our announcement blog post](https://huggingface.co/blog/smolagents).
 - Read [the smolagents documentation](https://huggingface.co/docs/smolagents/index).
 
-We are looking forward to see what you will build with vision language models and smolagents!
+We are looking forward to seeing what you will build with vision language models and smolagents!