app.py

import gradio as gr
import re
from transformers import pipeline as hf_pipeline

# Load SST sentiment classifier
sst_classifier = hf_pipeline(
    "text-classification",
    model="SamanthaStorm/tether-sst",
    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
)

# Load Tether's abuse pattern classifier
abuse_classifier = hf_pipeline(
    "text-classification",
    model="SamanthaStorm/tether-multilabel-v2",
    top_k=None,
    truncation=True
)

# Extract abuse pattern labels
def get_abuse_patterns(text, threshold=0.5):
    results = abuse_classifier(text)
    if isinstance(results, list) and isinstance(results[0], list):
        results = results[0]
    return [r['label'] for r in results if r['score'] >= threshold]

# Emotion extraction
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}

# Emotional tone tagging based on behavior + emotion + sentiment
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

# Analysis logic
def analyze_message(text):
    # Sentiment
    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
    emotions = get_emotion_profile(text)
    emotion_summary = "\n".join([f"{k.title()}: {v:.2f}" for k, v in emotions.items()])

    # Abuse patterns (used internally)
    patterns = get_abuse_patterns(text)

    # Tone tag
    tone_tag = get_emotional_tone_tag(emotions, sentiment_label, patterns)
    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 UI
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="Detects Supportive vs Undermining sentiment, emotion profile, and custom tone tags using behavioral logic."
)

iface.launch()