Update app.py

app.py

@@ -52,8 +52,7 @@ def generate_abuse_score_chart(dates, scores, labels):
     plt.savefig(buf, format='png')
     buf.seek(0)
     return Image.open(buf)
-# --- SST Sentiment Model ---
-sst_pipeline = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+
 
 # --- Abuse Model ---
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
@@ -275,9 +274,12 @@ def compute_abuse_score(matched_scores, sentiment):
     return min(adjusted_score, 100)
 def analyze_single_message(text, thresholds):
     motif_hits, matched_phrases = detect_motifs(text)
-    result = sst_pipeline(text)[0]
-    sentiment = "supportive" if result['label'] == "POSITIVE" else "undermining"
-    sentiment_score = result['score'] if sentiment == "undermining" else 0.0
+    emotion_profile = get_emotion_profile(text)
+
+    # Derive sentiment from emotion intensity
+    sentiment_score = emotion_profile.get("anger", 0) + emotion_profile.get("disgust", 0)
+    sentiment = "undermining" if sentiment_score > 0.5 else "supportive"
+
     weapon_flag = detect_weapon_language(text)
 
     adjusted_thresholds = {
@@ -307,14 +309,12 @@ def analyze_single_message(text, thresholds):
         contradiction_flag=contradiction_flag
     )
 
-    # Top 2 patterns for display (raw scores)
     top_patterns = sorted(
         [(label, score) for label, score in zip(LABELS, scores)],
         key=lambda x: x[1],
         reverse=True
     )[:2]
 
-    # ✅ Final abuse score: only use patterns that passed threshold
     matched_scores = [
         (label, score, PATTERN_WEIGHTS.get(label, 1.0))
         for label, score in zip(LABELS, scores)
@@ -324,7 +324,6 @@ def analyze_single_message(text, thresholds):
     abuse_score_raw = compute_abuse_score(matched_scores, sentiment)
     abuse_score = abuse_score_raw
 
-    # ✅ Always assign stage
     stage = get_risk_stage(threshold_labels, sentiment) if threshold_labels else 1
     if weapon_flag and stage < 2:
         stage = 2
@@ -333,14 +332,14 @@ def analyze_single_message(text, thresholds):
         abuse_score_raw = min(abuse_score_raw + 25, 100)
 
     abuse_score = min(abuse_score_raw, 100 if "threat" in threshold_labels or "control" in threshold_labels else 95)
-    # New: Emotion Profile
-    emotion_profile = get_emotion_profile(text)
+
+    # Debug logging
     print("Emotion Profile:")
     for emotion, score in emotion_profile.items():
         print(f"  {emotion.capitalize():10}: {score}")
     print("\n--- Debug Info ---")
     print(f"Text: {text}")
-    print(f"Sentiment: {sentiment} (raw: {result['label']}, score: {result['score']:.3f})")
+    print(f"Sentiment (via emotion): {sentiment} (score: {round(sentiment_score, 3)})")
     print("Abuse Pattern Scores:")
     for label, score in zip(LABELS, scores):
         passed = "✅" if score > adjusted_thresholds[label] else "❌"
@@ -351,7 +350,7 @@ def analyze_single_message(text, thresholds):
     print(f"Contradiction: {contradiction_flag}")
     print("------------------\n")
 
-    return abuse_score, threshold_labels, top_patterns, result, stage, darvo_score
+    return abuse_score, threshold_labels, top_patterns, {"label": sentiment}, stage, darvo_score
 
 def analyze_composite(msg1, date1, msg2, date2, msg3, date3, *answers_and_none):
     none_selected_checked = answers_and_none[-1]