diff --git a/README.md b/README.md
index 0f67b46..f08d6b4 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-![](https://i.imgur.com/Okdo3qc.jpg)
+![](https://i.imgur.com/9ASYY1n.jpg)
 
 ![](https://camo.githubusercontent.com/d38e6cc39779250a2835bf8ed3a72d10dbe3b05fa6527baa3f6f1e8e8bd056bf/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f436f64652d507974686f6e2d696e666f726d6174696f6e616c3f7374796c653d666c6174266c6f676f3d707974686f6e266c6f676f436f6c6f723d776869746526636f6c6f723d326262633861) ![](https://badgen.net/badge/status/WIP/blue) 
 
@@ -7,7 +7,6 @@
 ***
 
 - **mllibs** is a Machine Learning (ML) library which utilises natural language processing (NLP)
-- In this notebook, we look at implementing an **NLP interpreter** module for ML related operations
 - Development of such helper modules are motivated by the fact that everyones understanding of coding & subject matter (ML in this case) may be different 
 - Often we see people create **functions** and **classes** to simplify the process of achieving something (which is good practice)
 - Likewise, **NLP interpreters** follow this trend as well, except, in this case our only inputs for activating certain code is **natural language**
@@ -67,7 +66,7 @@ nlp_interpreter(input) # [0, 1, 1, 2, 3]
 
 <br>
 
-#### **3. LETS LOOK AT AN EXAMPLE**
+#### **3. LETS LOOK AT AN EXAMPLE USING MLLIBS**
 
 ***
 
@@ -123,14 +122,7 @@ Here are some anwsers:
 
 (2) custom added modules, for mllibs these library are associated with **machine learning**
 
-- `loader` - file management module, who's role is to access data, so it can be used (testing module)
-- `simple_eda` - exploratory data analysis module (testing module)
-- `eda_plot` - exploratory data analysis module associated with static graph plots (utilises seaborn)
-- `encoder` - nlp related module for converting text to numeric representation (text encoding)
-- `embedding` - nlp related module for generating embeddings for tokenised text data
-- `cleantext` - nlp related module for cleaning input text data
-- `sklinear` - Linear Regression module (testing module)
-- `hf_pipeline` - nlp related module for accessing huggingface pipelines
+You can check all the activations functions using <code>session.fl()</code> as shown in the sample notebooks
 
 <br>
 
@@ -187,6 +179,8 @@ configure_sample = {'corpus':corpus_sample,'info':info_sample}
 
 #### **8. CREATING A COLLECTION**
 
+There are two ways to start an interpreter session, manually importing and grouping modules or using  <code>interface</code> class
+
 ***
 
 First we need to combine all our module components together, this will link all passed modules together
@@ -214,14 +208,22 @@ collection.train()
 Lastly, pass the collection of modules (`nlpm` instance) to the interpreter `nlpi` 
 
 ```python
-interpreter = nlpi(collection)
+session = nlpi(collection)
 ```
 
 class `nlpi` can be used with method `exec` for user input interpretation
 
 ```python
 
-interpreter.exec('create a scatterplot using data with x dimension1 y dimension2')
+session.exec('create a scatterplot using data with x dimension1 y dimension2')
 
 ```
 
+***
+
+The faster way, includes all loaded modules and groups them together for us:
+
+```python
+from mllibs.interface import interface
+session = interface()
+```