Update app.py

app.py

@@ -172,12 +172,32 @@ sst_pipeline = pipeline("sentiment-analysis", model="distilbert-base-uncased-fin
 # ——— Single-Message Analysis ———
 def analyze_single_message(text):
     # healthy bypass
-    h=healthy_detector(text)[0]
-    if h['label']=="POSITIVE" and h['score']>0.9:
-        return { "abuse_score":0, "labels":[], "sentiment":"supportive",
-                 "stage":4, "darvo_score":0.0, "top_patterns":[] }
-    # model
-    inp=tokenizer(text,return_tensors='pt',truncation=True,padding=True)
+    h = healthy_detector(text)[0]
+
+    # 1) Strongly positive → healthy
+    if h['label'] == "POSITIVE" and h['score'] > 0.8:
+        return {
+            "abuse_score": 0,
+            "labels": [],
+            "sentiment": "supportive",
+            "stage": 4,
+            "darvo_score": 0.0,
+            "top_patterns": []
+        }
+
+    # 2) Mildly negative/neutral → also healthy
+    elif h['label'] == "NEGATIVE" and h['score'] < 0.6:
+        return {
+            "abuse_score": 0,
+            "labels": [],
+            "sentiment": "supportive",
+            "stage": 4,
+            "darvo_score": 0.0,
+            "top_patterns": []
+        }
+
+    # — if neither healthy case, continue on to actual abuse detection —
+    inp = tokenizer(text, return_tensors='pt', truncation=True, padding=True)
     with torch.no_grad(): logits=model(**inp).logits.squeeze(0)
     probs=torch.sigmoid(logits).numpy()
     labels=[lab for lab,p in zip(LABELS,probs) if p>THRESHOLDS[lab]]