Update app.py

app.py

@@ -1,3 +1,88 @@
+: import gradio as gr
+import re
+from transformers import pipeline as hf_pipeline
+
+# Load SST model (temporary baseline)
+sst_classifier = hf_pipeline(
+    "text-classification",
+    model="distilbert-base-uncased-finetuned-sst-2-english",
+    top_k=None,
+    truncation=True
+)
+
+# Load emotion classifier
+emotion_pipeline = hf_pipeline(
+    "text-classification",
+    model="j-hartmann/emotion-english-distilroberta-base",
+    top_k=None,
+    truncation=True
+)
+
+# Lexicon enhancement preprocessing
+negations = {"not", "never", "no", "none", "nobody", "nothing", "neither", "nowhere", "hardly", "scarcely", "barely"}
+amplifiers = {"very", "really", "extremely", "so", "totally", "completely", "absolutely", "utterly", "super"}
+softeners = {"slightly", "somewhat", "a bit", "a little", "mildly", "fairly", "kind of"}
+
+def preprocess_sentiment_text(text):
+    words = text.lower().split()
+    modified = []
+    negate = False
+
+    for word in words:
+        stripped = re.sub(r'\W+', '', word)
+
+        if stripped in negations:
+            negate = True
+            modified.append("<NEG>")
+            continue
+        if stripped in amplifiers:
+            modified.append(f"<AMP>{word}")
+            continue
+        if stripped in softeners:
+            modified.append(f"<SOFT>{word}")
+            continue
+        if negate:
+            modified.append(f"<NEG>{word}")
+            negate = False
+        else:
+            modified.append(word)
+
+    return " ".join(modified)
+
+# Emotion mapping
+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}
+
+# Tone tagging logic
+def get_emotional_tone_tag(emotions, sentiment, patterns, abuse_score=0):
+    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 function
 def analyze_message(text):
     preprocessed = preprocess_sentiment_text(text)
     sst_output = sst_classifier(preprocessed)
@@ -16,4 +101,14 @@ def analyze_message(text):
         f"🧠 Sentiment: {sentiment_label.title()} ({sentiment_score}%)\n\n"
         f"🎭 Emotional Profile:\n{emotion_summary}\n\n"
         f"🔍 Tone Tag: {tone_output}"
-    )
+    )
+# Interface
+iface = gr.Interface(
+    fn=analyze_message,
+    inputs=gr.Textbox(lines=4, placeholder="Paste a message here..."),
+    outputs="text",
+    title="Tether SST + Emotional Tone Tagger",
+    description="Applies lexicon-enhanced preprocessing, classifies sentiment, profiles emotion, and infers tone tags based on behavior logic."
+)
+
+iface.launch()