Update app.py

app.py

@@ -1,8 +1,8 @@
 try:
-    import spaces          #only on hugging face zerogpu, has to come before torch
+    import spaces         
     GPU = spaces.GPU
 except ImportError:
-    GPU = lambda f: f       #running locally --> no decorator needed
+    GPU = lambda f: f     
 
 import gradio as gr
 import torch
@@ -32,9 +32,9 @@ NAMES = {"WORKLOAD": "Sleep and workload", "TEAM": "Team dynamics",
          "ADJUSTMENT": "Adjustment and coping", "OUTSIDE_COMMS": "Outside communication"}
 
 
-# ---------------- the three models ----------------
+#the three models
 
-#1. vader baseline: keyword lists + sentiment gate (thresholds picked on the validation split in layer 6)
+#1. vader baseline: keyword lists + sentiment gate 
 KEYWORDS = {
     "WORKLOAD": ["exhausted", "exhaustion", "tired", "fatigue", "overworked", "sleep", "schedule", "timeline",
                  "overtime", "demanding", "heavy", "restless", "shift", "tasks", "pressure"],
@@ -55,7 +55,7 @@ tf_models = joblib.load(os.path.join(HERE, "tfidf_logistic_models.pkl"))
 STOP = set(stopwords.words("english"))
 stemmer = SnowballStemmer("english")
 
-#3. bert-base-uncased + my lora adapter (the adapter files sit next to this app.py)
+#3. bert-base-uncased + my lora adapter 
 #thresholds tuned on the validation split in layer 7
 BERT_THRESHOLDS = {"WORKLOAD": 0.65, "TEAM": 0.90, "ADJUSTMENT": 0.85, "OUTSIDE_COMMS": 0.90}
 tok = AutoTokenizer.from_pretrained(HERE)
@@ -97,7 +97,7 @@ def bert_probs(segs):
     return np.vstack(out)
 
 
-# ---------------- the text pipeline (layers 2 and 3) ----------------
+# he text pipeline
 
 #five cleaning rules in a fixed order
 WRAPPED_RE = re.compile(r"\n([A-Z])\n([A-Z][A-Za-z\-]{1,30}:)")
@@ -194,7 +194,7 @@ def segment(text):
     return [c for c in chunks if len(c.split()) >= MIN_WORDS]
 
 
-# ---------------- running everything ----------------
+# running everything 
 
 def read_input(file, text):
     if file is None:
@@ -288,7 +288,7 @@ def explain(state, choice, category):
             f"<div class='seg' style='line-height:1.9'>{spans}</div>")
 
 
-# ---------------- examples (the last four are the tests that show where it breaks) ----------------
+# examples
 
 GOOD = """NASA ORAL HISTORY TRANSCRIPT (DEMO TEXT)
 JANE DOE
@@ -325,7 +325,7 @@ TECHNICAL = ("We configured the solar array according to the procedural checklis
              "prep at eighteen hundred hours. All telemetry values were nominal and the ground confirmed the readings.")
 
 
-# ---------------- the page ----------------
+# HF page 
 
 CSS = """
 .gradio-container {background: radial-gradient(ellipse at top, #141b33 0%, #05070f 70%) !important;}
@@ -341,7 +341,7 @@ with gr.Blocks(title="SPACEMIND") as demo:
                 "the full pipeline, three models compared, and a SHAP explanation. Linaya Piyaratne (2401217)")
 
     with gr.Row():
-        #left side: inputs
+        #inputs
         with gr.Column():
             textInput = gr.Textbox(label="Paste a transcript or a passage", lines=10)
             fileInput = gr.File(label="...or upload a .txt / .pdf transcript", file_types=[".txt", ".pdf"])
@@ -358,7 +358,7 @@ with gr.Blocks(title="SPACEMIND") as demo:
                 gr.Examples([[os.path.join(HERE, "ridesk-12-6-02.txt")]], inputs=fileInput,
                             label="Real transcript from my test split")
 
-        #right side: the stages
+        #the stages
         with gr.Column():
             out1 = gr.Textbox(label="1. Input (word count)")
             out2 = gr.Textbox(label="2. Cleaning (five rules, removed counts)")