Update app.py

app.py

@@ -1,57 +1,108 @@
 import gradio as gr
-from transformers import pipeline
 import re
+from transformers import pipeline as hf_pipeline
 
-# Load your SST model pipeline
-sst_classifier = pipeline(
+# Load models
+sst_classifier = hf_pipeline(
     "text-classification",
-    model="SamanthaStorm/tether-sst",  # replace with your model name
+    model="SamanthaStorm/tether-sst",
     top_k=None,
     truncation=True
 )
 
-# Define lexicons
-negations = {"not", "never", "no", "none", "hardly", "rarely", "barely", "doesn’t", "isn’t", "wasn’t"}
-amplifiers = {"very", "extremely", "really", "absolutely", "so", "incredibly", "deeply", "profoundly"}
-diminishers = {"somewhat", "slightly", "barely", "kind of", "sort of", "a little"}
-
-def apply_lexicon_rules(text):
-    tokens = text.lower().split()
-    lexicon_score = 0
-
-    for i, token in enumerate(tokens):
-        if token in negations:
-            lexicon_score -= 1
-        if token in amplifiers:
-            lexicon_score += 1
-        if token in diminishers:
-            lexicon_score -= 0.5
-
-    return lexicon_score
-
-def classify_sentiment(text):
-    lex_score = apply_lexicon_rules(text)
-    result = sst_classifier(text)[0]
-    label = result["label"]
-    base_score = result["score"]
-
-    # Adjust model score based on lexicon features
-    adjusted_score = min(max(base_score + (lex_score * 0.05), 0), 1)
-    score_pct = round(adjusted_score * 100, 2)
-
-    if label == "LABEL_0":
-        return f"Supportive ({score_pct}%)"
-    elif label == "LABEL_1":
-        return f"Undermining ({score_pct}%)"
-    else:
-        return f"Unknown label ({label})"
+emotion_pipeline = hf_pipeline(
+    "text-classification",
+    model="j-hartmann/emotion-english-distilroberta-base",
+    top_k=None,
+    truncation=True
+)
+
+# Functions
+def get_emotion_profile(text):
+    emotions = emotion_pipeline(text)
+    if isinstance(emotions, list) and isinstance(emotions[0], list):
+        emotions = emotions[0]
+    return {e['label'].lower(): round(e['score'], 3) for e in emotions}
+
+def get_emotional_tone_tag(emotions, sentiment, patterns, abuse_score):
+    sadness = emotions.get("sadness", 0)
+    joy = emotions.get("joy", 0)
+    neutral = emotions.get("neutral", 0)
+    disgust = emotions.get("disgust", 0)
+    anger = emotions.get("anger", 0)
+    fear = emotions.get("fear", 0)
+
+    if (
+        sadness > 0.4 and
+        any(p in patterns for p in ["blame shifting", "guilt tripping", "recovery phase"]) and
+        (sentiment == "undermining" or abuse_score > 40)
+    ):
+        return "performative regret"
+    if (
+        (joy > 0.3 or sadness > 0.4) and
+        any(p in patterns for p in ["control", "gaslighting"]) and
+        sentiment == "undermining"
+    ):
+        return "coercive warmth"
+    if (
+        (neutral + disgust) > 0.5 and
+        any(p in patterns for p in ["dismissiveness", "projection", "obscure language"]) and
+        sentiment == "undermining"
+    ):
+        return "cold invalidation"
+    if (
+        (sadness + fear) > 0.5 and
+        sentiment == "supportive" and
+        all(p in ["recovery phase"] for p in patterns)
+    ):
+        return "genuine vulnerability"
+    if (
+        (anger + disgust) > 0.5 and
+        any(p in patterns for p in ["control", "threat", "insults", "dismissiveness"]) and
+        sentiment == "undermining"
+    ):
+        return "emotional threat"
+    if (
+        sadness > 0.6 and
+        any(p in patterns for p in ["guilt tripping", "projection"]) and
+        sentiment == "undermining"
+    ):
+        return "weaponized sadness"
+    if (
+        neutral > 0.5 and
+        any(p in patterns for p in ["dismissiveness", "obscure language"]) and
+        sentiment == "undermining"
+    ):
+        return "toxic resignation"
+
+    return None
+
+# Main interface function
+def analyze_message(text):
+    sst_result = sst_classifier(text)[0]
+    sentiment_label = "supportive" if sst_result["label"] == "LABEL_0" else "undermining"
+    sentiment_score = round(sst_result["score"] * 100, 2)
+
+    emotions = get_emotion_profile(text)
+    emotion_summary = "\n".join([f"{k.title()}: {v:.2f}" for k, v in emotions.items()])
+
+
+    tone_tag = get_emotional_tone_tag(emotions, sentiment_label, patterns, abuse_score)
+    tone_output = tone_tag if tone_tag else "None detected"
+
+    return (
+        f"🧠 Sentiment: {sentiment_label.title()} ({sentiment_score}%)\n\n"
+        f"🎭 Emotional Profile:\n{emotion_summary}\n\n"
+        f"🔍 Tone Tag: {tone_output}"
+    )
 
+# Gradio app
 iface = gr.Interface(
-    fn=classify_sentiment,
-    inputs=gr.Textbox(lines=4, placeholder="Paste message here..."),
+    fn=analyze_message,
+    inputs=gr.Textbox(lines=4, placeholder="Paste a message here..."),
     outputs="text",
-    title="Tether SST (Lexicon-Enhanced)",
-    description="Combines transformer model with lexicon-based adjustment to classify Supportive or Undermining tone."
+    title="Tether SST + Emotional Tone Tagger",
+    description="Detects Supportive vs Undermining sentiment, emotion profile, and custom tone tags using behavioral logic."
 )
 
 iface.launch()