added LFTK tool to description

app.py

@@ -217,24 +217,32 @@ def llama_strategies_respond(strategy, task_name, task_ling_ent, message, chat_h
 
 def interface():
         with gr.Tab("Linguistic Entities"):
-            gr.Markdown("""
-                        ## 📜 Step-By-Step Instructions
+            with gr.Row():
+                gr.Markdown("""
+                            ## 📜 Step-By-Step Instructions
+
+                            - Enter a sentence for three models to process (Vicuna-7b, LLaMA-7b and GPT-3.5).
+                            - If you own an OpenAI API key, select 'Yes' in the dropdown. If you don't own one, select 'No'.
+                                - If you selected 'Yes', enter your OpenAI API Key [Link to your OpenAI keys](https://platform.openai.com/api-keys).
+                                - If you selected 'No', leave the 'OpenAI Key' field blank and continue with the rest.
+                            - Select a Linguistic Entity from the Dropdown or enter a custom one.
+                            - Click 'Submit' to send your inputs to the models.
+                            - To submit a new prompt, repeat all the steps above and click 'Submit' again. Your new prompt should appear on the top of previous ones.
 
-                        - Enter a sentence for three models to process (Vicuna-7b, LLaMA-7b and GPT-3.5).
-                        - If you own an OpenAI API key, select 'Yes' in the dropdown. If you don't own one, select 'No'.
-                            - If you selected 'Yes', enter your OpenAI API Key [Link to your OpenAI keys](https://platform.openai.com/api-keys).
-                            - If you selected 'No', leave the 'OpenAI Key' field blank and continue with the rest.
-                        - Select a Linguistic Entity from the Dropdown or enter a custom one.
-                        - Click 'Submit' to send your inputs to the models.
-                        - To submit a new prompt, repeat all the steps above and click 'Submit' again. Your new prompt should appear on the top of previous ones.
+                            ### ⏳ After you click 'Submit', the models will take a couple seconds to process your inputs. 
+                            ### 🤖 Then, the models will output the linguistic entity found in your prompt based on your selection!
 
-                        ### ⏳ After you click 'Submit', the models will take a couple seconds to process your inputs. 
-                        ### 🤖 Then, the models will output the linguistic entity found in your prompt based on your selection!
+                            Note: If you get an 'Error' in the gpt-3.5 model, check the following:
+                            - Check that you entered your key correctly without any extra characters.
+                            - If you used a free key, it means you exceeded your quota from the free API Key.
+                            """)
+                
+                gr.Markdown("""
+                            ### 📊 Linguistic Complexity
 
-                        Note: If you get an 'Error' in the gpt-3.5 model, check the following:
-                        - Check that you entered your key correctly without any extra characters.
-                        - If you used a free key, it means you exceeded your quota from the free API Key.
-                        """)
+                            - We use existing tool, [LFTK](https://github.com/brucewlee/lftk?tab=readme-ov-file), to estimate the liguistic complexity of input sentences.
+                            - For more information regarding the meanings of each feature keyword, please reference their documentation [here](https://docs.google.com/spreadsheets/d/1uXtQ1ah0OL9cmHp2Hey0QcHb4bifJcQFLvYlVIAWWwQ/edit#gid=693915416).
+                            """)
 
             # Inputs
             ling_ents_prompt = gr.Textbox(show_label=False, placeholder="Write a prompt and press enter")
@@ -302,15 +310,23 @@ def interface():
                             - If you used a free key, it means you exceeded your quota from the free API Key.
                             """)
                 
-                gr.Markdown("""
-                            ### 🛠️ How each Strategy works
-
-                            - Strategy 1 - QA-Based Prompting
-                                - The model is prompted with a question-answer format. The input consists of a question, and the model generates a response based on the understanding of the question and its knowledge.
-                            - Strategy 2 - Instruction-Based Prompting
-                                - Involves providing the model with explicit instructions on how to generate a response. Instead of relying solely on context or previous knowledge, the instructions guide the model in generating content that aligns with specific criteria.
-                            - Strategy 3 - Structured Prompting
-                                - Involves presenting information to the model in a structured format, often with defined sections or categories. The model then generates responses following the given structure. 
+                with gr.Column():
+                    gr.Markdown("""
+                                ### 🛠️ How each Strategy works
+
+                                - Strategy 1 - QA-Based Prompting
+                                    - The model is prompted with a question-answer format. The input consists of a question, and the model generates a response based on the understanding of the question and its knowledge.
+                                - Strategy 2 - Instruction-Based Prompting
+                                    - Involves providing the model with explicit instructions on how to generate a response. Instead of relying solely on context or previous knowledge, the instructions guide the model in generating content that aligns with specific criteria.
+                                - Strategy 3 - Structured Prompting
+                                    - Involves presenting information to the model in a structured format, often with defined sections or categories. The model then generates responses following the given structure. 
+                                """)
+                    
+                    gr.Markdown("""
+                            ### 📊 Linguistic Complexity
+
+                            - We use existing tool, [LFTK](https://github.com/brucewlee/lftk?tab=readme-ov-file), to estimate the liguistic complexity of input sentences.
+                            - For more information regarding the meanings of each feature keyword, please reference their documentation [here](https://docs.google.com/spreadsheets/d/1uXtQ1ah0OL9cmHp2Hey0QcHb4bifJcQFLvYlVIAWWwQ/edit#gid=693915416).
                             """)
             
             # Inputs