Update app.py

app.py

@@ -1,99 +1,14 @@
-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_result = sst_classifier(preprocessed)[0]  # <- this line was missing!
-    
     sst_output = sst_classifier(preprocessed)
     sentiment = sst_output[0]
     sentiment_label = "supportive" if sentiment["label"] == "POSITIVE" else "undermining"
+    sentiment_score = round(sentiment["score"] * 100, 2)
 
     emotions = get_emotion_profile(text)
     emotion_summary = "\n".join([f"{k.title()}: {v:.2f}" for k, v in emotions.items()])
 
+    # Temporarily pass empty abuse pattern list until Tether model is added
     tone_tag = get_emotional_tone_tag(emotions, sentiment_label, patterns=[])
     tone_output = tone_tag if tone_tag else "None detected"
 
@@ -101,14 +16,4 @@ 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()
+    )