added some screenshots

Author: Anagha Jamthe

tutorials/Fine_Tuning_Vision/02-evaluation.md

@@ -5,7 +5,7 @@ To try this part of the tutorial, we will use the fine-tuned model `best.pt` cre
 Your fine-tuned model will be accessible through Jupyter in this location
 `$WORK -> vista -> jobUUID -> train -> weights -> best.pt`
 
-You should already have the generated code that we tested accuracy on the base line model.
+You should already have the generated code that we tested accuracy on the baseline model.
 
 Now, just change the path of your model to the fine-tuned model path and re-run the code to test the accuracy.
 
@@ -22,4 +22,4 @@ False Negatives: 21
 Overall detection accuracy: 0.75
 ```
 
-As an exercise, you may train your model with more EPOCHS and see if this accuracy can be further improved.
+As an exercise, you may train your model with more epochs and see if this accuracy can be further improved.

tutorials/Tapis_FlexServ/01b-running-flexserv.md

@@ -375,8 +375,8 @@ Step 5b) Once the `tapisjob.out` opens, look at the ACCESS INFORMATION Section t
 If you made it this far, you are successfully running FlexServ, you can explore the FlexServ UI next and try to send your first chat. 
 
 ### Using the FlexServ UI 
-Todo: 
-* Setting the FlexServ API Key and Sending your first chat 
-* Public and Private Model Pools 
-* Working with Images 
-* Working with Some other Examples 
+Go to the URL hhtps://vista.tacc.utexas.edu:`Port number from above` and enter the TAP token from the tapisjob.out as shown in figure below.
+
+![FlexServ UI](/tutorials/images/FlexservUI.png)
+
+Next, we will use the flexserv to generate a code for small animal detection using vision models and evaluate the model performance.

tutorials/Tapis_FlexServ/01c-code-gen-flexserv.md

@@ -7,6 +7,9 @@ To test the capabilities of the FlexServ inference server, we can provide a comp
 
 ### On FlexServ UI
 
+- Refresh the Model pool so you can see public and private models available for you to run.
+![Model Pool](/tutorials/images/Model_pool.png)
+
 - Copy and paste the following prompt into the FlexServ UI in the `Responses API`, `Input(Markdown)` section, shown in the image below.