update readme file

README.md

@@ -1,16 +1,24 @@
 ---
-title: Lit2vec Tldr Bart Space
-emoji: 💬
-colorFrom: yellow
-colorTo: purple
+title: Lit2Vec TL;DR Summarizer
+emoji: 🧪
+colorFrom: blue
+colorTo: green
 sdk: gradio
 sdk_version: 5.42.0
 app_file: app.py
 pinned: false
 hf_oauth: true
 hf_oauth_scopes:
-- inference-api
-short_description: chemistry abstract summarizer
+  - inference-api
+short_description: Abstractive TL;DR summarizer for chemistry research abstracts.
 ---
 
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+Lit2Vec TL;DR is an abstractive summarization tool for chemistry research abstracts, built using Gradio and a fine-tuned DistilBART model. It generates concise, structured summaries capturing the **methods**, **results**, and **significance** of scientific papers.
+
+This app uses models hosted on the 🤗 [Hugging Face Hub](https://huggingface.co/Bocklitz-Lab/lit2vec-tldr-bart-model) and supports reproducible, legally compliant summarization data. Ideal for knowledge graph construction, semantic indexing, and literature triage in chemical sciences.
+
+- 🔬 Model: [`Bocklitz-Lab/lit2vec-tldr-bart-model`](https://huggingface.co/Bocklitz-Lab/lit2vec-tldr-bart-model)  
+- 📁 Dataset: [`lit2vec-tldr-bart-dataset`](https://huggingface.co/datasets/Bocklitz-Lab/lit2vec-tldr-bart-dataset)  
+- 💻 Code: [GitHub Repo](https://github.com/Bocklitz-Lab/lit2vec-tldr-bart)
+
+Paste any chemistry abstract to get a TL;DR-style structured summary with just one click.

app.py

@@ -53,7 +53,7 @@ with gr.Blocks() as demo:
     gr.Markdown("## TL;DR Summarizer")
 
     min_length_slider = gr.Slider(minimum=0, maximum=100, step=1, label="Minimum Summary Length (%)", value=10)
-    max_length_slider = gr.Slider(minimum=0, maximum=100, step=1, label="Maximum Summary Length (%)", value=20)
+    max_length_slider = gr.Slider(minimum=0, maximum=100, step=1, label="Maximum Summary Length (%)", value=50)
 
     input_text = gr.Textbox(label="Input text to summarize", lines=6, value=example_text)
     summarize_button = gr.Button("Summarize Text")