possible how to location move

app.py

@@ -95,7 +95,7 @@ were utilized to estimate the carbon impact.*
             st.warning('This is a warning')
             # Object notation
             st.subheader('🌲')
-            with st.expander("🌍 🌳"):
+            with st.expander("🌍"):
                 st.markdown('''
 
     - **Hardware Type:** 8 16GB V100
@@ -278,16 +278,63 @@ GPT-2 reaches a perplexity on the test set of 16.3 compared to 21.1 for DistilGP
 
     # Try App
 
-    col2.subheader('Try App')
-    col2.code('''[To:do add code]
+    col2.header('Try App')
+    col2.code('''[To:do add integration with HF
     ''')
 
+    #  How to Get Started
+
+    with col2.header('How to Get Started'):
+        col2.markdown(''' 
+         *Be sure to read the sections on in-scope and out-of-scope uses and limitations of the model for further information on how to use the model.*
+        ''')
+        with col2.expander(""):
+            st.markdown('''
+
+Using DistilGPT2 is similar to using GPT-2. DistilGPT2 can be used directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:
+
+```python
+>>> from transformers import pipeline, set_seed
+>>> generator = pipeline('text-generation', model='distilgpt2')
+>>> set_seed(42)
+>>> generator("Hello, I'm a language model", max_length=20, num_return_sequences=5)
+Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.
+[{'generated_text': "Hello, I'm a language model, I'm a language model. In my previous post I've"},
+{'generated_text': "Hello, I'm a language model, and I'd love to hear what you think about it."},
+{'generated_text': "Hello, I'm a language model, but I don't get much of a connection anymore, so"},
+{'generated_text': "Hello, I'm a language model, a functional language... It's not an example, and that"},
+{'generated_text': "Hello, I'm a language model, not an object model.\n\nIn a nutshell, I"}]
+``` 
+
+
+**Here is how to use this model to get the features of a given text in PyTorch**:
+
+```python
+from transformers import GPT2Tokenizer, GPT2Model
+tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
+model = GPT2Model.from_pretrained('distilgpt2')
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+```
+
+**And in TensorFlow:**
+
+```python
+from transformers import GPT2Tokenizer, TFGPT2Model
+tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
+model = TFGPT2Model.from_pretrained('distilgpt2')
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='tf')
+output = model(encoded_input)
+```
+
+            ''')
+
     # Visuals
 
-    col2.subheader('Visuals')
-    col2.code('''
-[temp]
-    ''')
+    
+