Update readme

README.md

@@ -1,10 +1,10 @@
 ---
 title: SPACEMIND
-colorFrom: indigo
-colorTo: blue
+colorFrom: red
+colorTo: indigo
 sdk: gradio
 sdk_version: 6.29.1
-python_version: "3.12"
+python_version: '3.12'
 app_file: app.py
 pinned: false
 short_description: Behavioural health signals in astronaut oral histories
@@ -12,10 +12,13 @@ short_description: Behavioural health signals in astronaut oral histories
 
 # SPACEMIND
 
-Final year project demo, Linaya Piyaratne (2401217), University of Buckingham.
 
-Paste or upload an astronaut oral history transcript and the app runs my pipeline step by step: word count, cleaning, removing the interviewer, segmenting, then all three of my models (VADER baseline, TF-IDF + logistic regression, BERT + LoRA) and a SHAP explanation for one segment. The test examples show where the pipeline breaks (short text, speaker names not in capitals, curly apostrophes).
+Paste or upload an astronaut oral history transcript and the app runs my pipeline step by step: word count, cleaning, removing the interviewer, segmenting, then all three of my models (VADER baseline, TF-IDF + logistic regression, BERT + LoRA) and a SHAP explanation for one segment. 
 
-Files: `app.py` (everything), my LoRA adapter (`adapter_config.json`, `adapter_model.safetensors`), the tokenizer (`tokenizer.json`, `tokenizer_config.json`), the TF-IDF models (`tfidf_vectorizer.pkl`, `tfidf_logistic_models.pkl`) and one test split transcript.
+Files: 
+`app.py` (everything)
+my LoRA adapter (`adapter_config.json`,`adapter_model.safetensors`)
+the tokenizer (`tokenizer.json`, `tokenizer_config.json`)
+the TF-IDF models (`tfidf_vectorizer.pkl`, `tfidf_logistic_models.pkl`) 
 
-Research prototype: it flags passages for a person to read and does not diagnose anyone.
+Research prototype: it flags passages for a person to read and does not diagnose anyone.