Update app.py

app.py

@@ -14,15 +14,21 @@ emotion_pipeline = hf_pipeline(
 )
 
 def get_emotion_profile(text):
+    """
+    Returns a dict of emotion scores for the input text.
+    """
     results = emotion_pipeline(text)
+    # Some pipelines return a list of lists
     if isinstance(results, list) and isinstance(results[0], list):
         results = results[0]
     return {r["label"].lower(): round(r["score"], 3) for r in results}
 
+# apology keywords for pleading concern
+APOLOGY_KEYWORDS = ["sorry", "apolog", "forgive"]
 
-# ——— 2) Abuse-patterns Model ——————————————————————————————————————————————
+# ——— 2) Abuse-Patterns Model ——————————————————————————————————————————————
 model_name = "SamanthaStorm/tether-multilabel-v3"
-model     = AutoModelForSequenceClassification.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
 
 LABELS = [
@@ -32,36 +38,23 @@ LABELS = [
 ]
 
 THRESHOLDS = {
-    "blame shifting": 0.28, "contradictory statements": 0.27, "control": 0.08, "dismissiveness": 0.32,
-    "gaslighting":     0.27, "guilt tripping":           0.31, "insults":  0.10, "obscure language": 0.55,
-    "projection":      0.09, "recovery phase":           0.33, "threat":    0.15
+    "blame shifting": 0.28,
+    "contradictory statements": 0.27,
+    "control": 0.08,
+    "dismissiveness": 0.32,
+    "gaslighting": 0.27,
+    "guilt tripping": 0.31,
+    "insults": 0.10,
+    "obscure language": 0.55,
+    "projection": 0.09,
+    "recovery phase": 0.33,
+    "threat": 0.15
 }
 
-# ——— 3) Emotional-tone tagging (detailed categories) —————————————————————————————
-def get_emotional_tone_tag(emotion_profile, patterns):
-    # unpack emotion scores
-    sadness = emotion_profile.get("sadness", 0)
-    joy     = emotion_profile.get("joy",     0)
-    neutral = emotion_profile.get("neutral", 0)
-    disgust = emotion_profile.get("disgust", 0)
-    anger   = emotion_profile.get("anger",   0)
-    fear    = emotion_profile.get("fear",    0)
-
+# ——— 3) Emotional-Tone Tagging —————————————————————————————————————————————
 def get_emotional_tone_tag(emotion_profile, patterns, text_lower):
     """
-    18 possible tone tags:
-      1–7  = original nuanced tones
-      8    = indignant reproach
-      9    = confrontational
-      10   = passive aggression
-      11   = sarcastic mockery
-      12   = menacing threat
-      13   = pleading concern
-      14   = fear-mongering
-      15   = disbelieving accusation
-      16   = empathetic solidarity
-      17   = assertive boundary
-      18   = stonewalling
+    Assigns one of 18 nuanced tone categories based on emotion scores, patterns, and text.
     """
     sadness  = emotion_profile.get("sadness",  0)
     joy      = emotion_profile.get("joy",      0)
@@ -73,57 +66,57 @@ def get_emotional_tone_tag(emotion_profile, patterns, text_lower):
 
     # 1. Performative Regret
     if (
-        sadness > 0.4
-        and any(p in patterns for p in ["blame shifting", "guilt tripping", "recovery phase"])
+        sadness > 0.4 and
+        any(p in patterns for p in ["blame shifting", "guilt tripping", "recovery phase"])
     ):
         return "performative regret"
 
     # 2. Coercive Warmth
     if (
-        (joy > 0.3 or sadness > 0.4)
-        and any(p in patterns for p in ["control", "gaslighting"])
+        (joy > 0.3 or sadness > 0.4) and
+        any(p in patterns for p in ["control", "gaslighting"])
     ):
         return "coercive warmth"
 
     # 3. Cold Invalidation
     if (
-        (neutral + disgust) > 0.5
-        and any(p in patterns for p in ["dismissiveness", "projection", "obscure language"])
+        (neutral + disgust) > 0.5 and
+        any(p in patterns for p in ["dismissiveness", "projection", "obscure language"])
     ):
         return "cold invalidation"
 
     # 4. Genuine Vulnerability
     if (
-        (sadness + fear) > 0.5
-        and all(p == "recovery phase" for p in patterns)
+        (sadness + fear) > 0.5 and
+        all(p == "recovery phase" for p in patterns)
     ):
         return "genuine vulnerability"
 
     # 5. Emotional Threat
     if (
-        (anger + disgust) > 0.5
-        and any(p in patterns for p in ["control", "threat", "insults", "dismissiveness"])
+        (anger + disgust) > 0.5 and
+        any(p in patterns for p in ["control", "threat", "insults", "dismissiveness"])
     ):
         return "emotional threat"
 
     # 6. Weaponized Sadness
     if (
-        sadness > 0.6
-        and any(p in patterns for p in ["guilt tripping", "projection"])
+        sadness > 0.6 and
+        any(p in patterns for p in ["guilt tripping", "projection"])
     ):
         return "weaponized sadness"
 
     # 7. Toxic Resignation
     if (
-        neutral > 0.5
-        and any(p in patterns for p in ["dismissiveness", "obscure language"])
+        neutral > 0.5 and
+        any(p in patterns for p in ["dismissiveness", "obscure language"])
     ):
         return "toxic resignation"
 
     # 8. Indignant Reproach
     if (
-        anger > 0.5
-        and any(p in patterns for p in ["guilt tripping", "contradictory statements"])
+        anger > 0.5 and
+        any(p in patterns for p in ["guilt tripping", "contradictory statements"])
     ):
         return "indignant reproach"
 
@@ -133,8 +126,8 @@ def get_emotional_tone_tag(emotion_profile, patterns, text_lower):
 
     # 10. Passive Aggression
     if (
-        neutral > 0.6
-        and any(p in patterns for p in ["dismissiveness", "projection"])
+        neutral > 0.6 and
+        any(p in patterns for p in ["dismissiveness", "projection"])
     ):
         return "passive aggression"
 
@@ -148,9 +141,9 @@ def get_emotional_tone_tag(emotion_profile, patterns, text_lower):
 
     # 13. Pleading Concern
     if (
-        sadness > 0.3
-        and any(k in text_lower for k in ["sorry", "apolog", "forgive"])
-        and not patterns
+        sadness > 0.3 and
+        any(k in text_lower for k in APOLOGY_KEYWORDS) and
+        not patterns
     ):
         return "pleading concern"
 
@@ -176,45 +169,31 @@ def get_emotional_tone_tag(emotion_profile, patterns, text_lower):
 
     return None
 
-
-
-# ——— 4) Single-message analysis —————————————————————————————————————————————
+# ——— 4) Single-message Analysis ———————————————————————————————————————————
 def analyze_message(text):
-    # 1) emotion profiling
+    text_lower = text.lower()
+    # 1) Emotion
     emotion_profile = get_emotion_profile(text)
-
-    # 2) abuse-pattern classification
+    # 2) Patterns
     toks = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
     with torch.no_grad():
         logits = model(**toks).logits.squeeze(0)
     scores = torch.sigmoid(logits).cpu().numpy()
-
-    # 3) identify active patterns
-    active_patterns = [
-        label for label, prob in zip(LABELS, scores)
-        if prob >= THRESHOLDS[label]
-    ]
-
-    # 4) add recovery-phase on apology keywords
-    low = text.lower()
-    if any(k in low for k in ["sorry", "apolog", "forgive"]):
-        if "recovery phase" not in active_patterns:
-            active_patterns.append("recovery phase")
-
-    # 5) tone tagging with detailed rules
-    tone_tag = get_emotional_tone_tag(emotion_profile, active_patterns)
-
+    active_patterns = [lab for lab, sc in zip(LABELS, scores) if sc >= THRESHOLDS[lab]]
+    # append recovery-phase if apology
+    if any(k in text_lower for k in APOLOGY_KEYWORDS) and "recovery phase" not in active_patterns:
+        active_patterns.append("recovery phase")
+    # 3) Tone
+    tone_tag = get_emotional_tone_tag(emotion_profile, active_patterns, text_lower)
     return {
         "emotion_profile": emotion_profile,
         "active_patterns": active_patterns,
         "tone_tag": tone_tag
     }
 
-
-# ——— 5) Composite wrapper (handles uploads + text boxes) ——————————————————————————
+# ——— 5) Composite Wrapper ————————————————————————————————————————————————
 def analyze_composite(uploaded_file, *texts):
     outputs = []
-
     # file upload
     if uploaded_file is not None:
         raw = uploaded_file.read()
@@ -227,7 +206,6 @@ def analyze_composite(uploaded_file, *texts):
                 content = raw.decode("utf-8")
             except UnicodeDecodeError:
                 content = raw.decode("latin-1")
-
         r = analyze_message(content)
         outputs.append(
             "── Uploaded File ──\n"
@@ -235,7 +213,6 @@ def analyze_composite(uploaded_file, *texts):
             f"Active Patterns : {r['active_patterns']}\n"
             f"Emotional Tone  : {r['tone_tag']}\n"
         )
-
     # text inputs
     for idx, txt in enumerate(texts, start=1):
         if not txt:
@@ -247,25 +224,19 @@ def analyze_composite(uploaded_file, *texts):
             f"Active Patterns : {r['active_patterns']}\n"
             f"Emotional Tone  : {r['tone_tag']}\n"
         )
-
     if not outputs:
         return "Please enter at least one message."
-
     return "\n".join(outputs)
 
-
-# ——— 6) Gradio interface ————————————————————————————————————————————————
+# ——— 6) Gradio Interface ————————————————————————————————————————————————
 message_inputs = [gr.Textbox(label=f"Message {i+1}") for i in range(3)]
-
 iface = gr.Interface(
     fn=analyze_composite,
-    inputs=[
-        gr.File(file_types=[".txt", ".png", ".jpg", ".jpeg"], label="Upload text or image")
-    ] + message_inputs,
+    inputs=[gr.File(file_types=[".txt", ".png", ".jpg", ".jpeg"], label="Upload text or image")] + message_inputs,
     outputs=gr.Textbox(label="Analysis"),
-    title="Tether Analyzer (detailed tone tags)",
-    description="Emotion profiling, pattern tags, and nuanced tone categories—no abuse score or DARVO."
+    title="Tether Analyzer (extended tone tags)",
+    description="Emotion profiling, pattern tags, and a wide set of nuanced tone categories—no abuse score or DARVO."
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()