Update app.py

app.py

@@ -60,7 +60,7 @@ def update_description(task_name: str) -> str:
 
 with gr.Blocks() as demo:
     gr.Markdown("# 🔬 LLM-Microscope — Understanding Token Representations in Transformers")
-    gr.Markdown("Select a model, a mode of analysis, and a sentence. The tool will visualize what’s happening **inside** the language model — layer by layer, token by token.")
+    gr.Markdown("Select a model, analysis mode, and input — then peek inside the black box to see which layers matter most, which tokens carry the most memory, and how predictions evolve.")
 
     with gr.Row():
         model_selector = gr.Dropdown(
@@ -114,6 +114,12 @@ This heatmap shows **how each token is processed** across layers of a language m
 - `Tokenwise loss without i-th layer`: shows how much each token depends on a specific layer. Red means performance drops if we skip this layer.
 
 Use this tool to **peek inside the black box** — it reveals which layers matter most, which tokens carry the most memory, and how LLMs evolve their predictions.
+
+You can also use `llm-microscope` as a Python library to run these analyses on **your own models and data**.
+
+Just install it with: `pip install llm-microscope`
+
+More details provided in [GitHub repo](https://github.com/AIRI-Institute/LLM-Microscope).
 """)
 
     task_selector.change(fn=update_description, inputs=[task_selector], outputs=[task_description])