Update app.py

app.py

@@ -6,11 +6,11 @@ import torch
 from transformers import RobertaTokenizerFast, RobertaForSequenceClassification
 
 # ---------------------------
-# Load your model directly (NO API, just from HuggingFace hub)
+# Load your model (conversation learning style)
 # ---------------------------
-MODEL_NAME = "Alifjo123/robertaBase_messaging_100k"
-tokenizer = RobertaTokenizerFast.from_pretrained(MODEL_NAME)
-model = RobertaForSequenceClassification.from_pretrained(MODEL_NAME)
+MODEL_PATH = "Alifjo123/robertaBase_messaging_100k"  # your HuggingFace model
+tokenizer = RobertaTokenizerFast.from_pretrained(MODEL_PATH)
+model = RobertaForSequenceClassification.from_pretrained(MODEL_PATH)
 model.eval()
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -20,7 +20,13 @@ model.to(device)
 # Util: classify a single text
 # ---------------------------
 def predict_label(text: str):
-    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
+    inputs = tokenizer(
+        text,
+        truncation=True,
+        padding=True,
+        max_length=512,
+        return_tensors="pt"
+    ).to(device)
     with torch.no_grad():
         outputs = model(**inputs)
         logits = outputs.logits
@@ -84,14 +90,14 @@ def clear_chat():
     return render_chat([]), []
 
 # ---------------------------
-# Custom CSS (Pink + Blue ONLY, NO WHITE)
+# Custom CSS (Pink + Blue ONLY)
 # ---------------------------
 CSS = """
 * { box-sizing: border-box; }
 :root {
-  --bg-gradient: linear-gradient(135deg, #ff99cc, #66ccff);  /* pink to blue */
-  --bubble-a: #ff66b2;   /* darker pink */
-  --bubble-b: #3399ff;   /* vibrant blue */
+  --bg-gradient: linear-gradient(135deg, #ff99cc, #66ccff);
+  --bubble-a: #ff66b2;
+  --bubble-b: #3399ff;
   --text-light: #f9f9f9;
   --chip-safe: #00e676;
   --chip-unsafe: #ff5252;