Merge pull request #30 from karenwky/unit-1-unit-2-update

Unit 1 & Unit 2 update

Author: burtenshaw

units/en/unit1/gradio-mcp.mdx

@@ -1,6 +1,6 @@
 # Gradio MCP Integration
 
-We've now explored the core concepts of the MCP protocol and how to implement an MCP Servers and Clients. In this section, we're going to make things slightly easier by using Gradio to create an MCP Server!
+We've now explored the core concepts of the MCP protocol and how to implement MCP Servers and Clients. In this section, we're going to make things slightly easier by using Gradio to create an MCP Server!
 
 <Tip>
 
@@ -151,4 +151,4 @@ Gradio's integration with MCP provides an accessible entry point to the MCP ecos
 
 As we progress through this course, we'll explore more sophisticated MCP implementations, but Gradio offers an excellent starting point for understanding and experimenting with the protocol.
 
-In the next unit, we'll dive deeper into building MCP applications, focusing on setting up development environments, exploring SDKs, and implementing more advanced MCP Servers and Clients. 
+In the next unit, we'll dive deeper into building MCP applications, focusing on setting up development environments, exploring SDKs, and implementing more advanced MCP Servers and Clients. 

units/en/unit1/mcp-clients.mdx

@@ -277,9 +277,8 @@ sepia: Apply a sepia filter to the input image.
 Now, let's see how we can use the MCP Client in a code agent. 
 
 ```python
-from smolagents import ToolCollection, CodeAgent
+from smolagents import InferenceClientModel, CodeAgent, ToolCollection
 from mcp.client.stdio import StdioServerParameters
-from smolagents import CodeAgent, InferenceClientModel
 
 model = InferenceClientModel()
 

units/en/unit2/gradio-client.mdx

@@ -25,8 +25,7 @@ Now, we can import the necessary libraries and create a simple Gradio interface
 import gradio as gr
 
 from mcp.client.stdio import StdioServerParameters
-from smolagents import ToolCollection, CodeAgent
-from smolagents import CodeAgent, InferenceClientModel
+from smolagents import InferenceClientModel, CodeAgent, ToolCollection
 from smolagents.mcp_client import MCPClient
 ```
 
@@ -79,8 +78,7 @@ Here's the complete example of the MCP Client in Gradio:
 import gradio as gr
 
 from mcp.client.stdio import StdioServerParameters
-from smolagents import ToolCollection, CodeAgent
-from smolagents import CodeAgent, InferenceClientModel
+from smolagents import InferenceClientModel, CodeAgent, ToolCollection
 from smolagents.mcp_client import MCPClient
 
 

units/en/unit2/gradio-server.mdx

@@ -167,7 +167,6 @@ textblob
 
 3. Push your code to the Space:
 ```bash
-cp server.py app.py
 git init
 git add app.py requirements.txt
 git commit -m "Initial commit"

units/en/unit2/tiny-agents.mdx

@@ -1,6 +1,6 @@
 # Tiny Agents: an MCP-powered agent in 50 lines of code
 
-Now that we've built MCP servers in Gradio let's explore MCP clients even further. This section builds on the experimental project [Tiny Agents](https://huggingface.co/blog/tiny-agents), which demonstrates a super simple way of deploying MCP clients that can connect to services like our Gradio sentiment analysis server.
+Now that we've built MCP servers in Gradio, let's explore MCP clients even further. This section builds on the experimental project [Tiny Agents](https://huggingface.co/blog/tiny-agents), which demonstrates a super simple way of deploying MCP clients that can connect to services like our Gradio sentiment analysis server.
 
 In this short exercise, we will walk you through how to implement a TypeScript (JS) MCP client that can communicate with any MCP server, including the Gradio-based sentiment analysis server we built in the previous section. You'll see how MCP standardizes the way agents interact with tools, making Agentic AI development significantly simpler.