app.py

import gradio as gr
import torch
from transformers import pipeline as hf_pipeline, AutoModelForSequenceClassification, AutoTokenizer


# ——— 1) Emotion Pipeline ————————————————————————————————————————————————
emotion_pipeline = hf_pipeline(
    "text-classification",
    model="j-hartmann/emotion-english-distilroberta-base",
    top_k=None,
    truncation=True
)

def get_emotion_profile(text):
    """
    Returns a dict of { emotion_label: score } for the input text.
    """
    results = emotion_pipeline(text)
    # some pipelines return [[…]]
    if isinstance(results, list) and isinstance(results[0], list):
        results = results[0]
    return {r["label"].lower(): round(r["score"], 3) for r in results}


# ——— 2) Abuse-patterns Model ——————————————————————————————————————————————
model_name = "SamanthaStorm/tether-multilabel-v3"
model     = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)

LABELS = [
    "blame shifting", "contradictory statements", "control", "dismissiveness",
    "gaslighting", "guilt tripping", "insults", "obscure language",
    "projection", "recovery phase", "threat"
]

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
}


# ——— 3) Single-message analysis ——————————————————————————————————————————————
def analyze_message(text):
    """
    Runs motif detection, emotion profiling, and the abuse-pattern classifier.
    Returns a dict with:
      - matched_phrases: list of raw regex hits
      - emotion_profile: { emotion: score }
      - active_patterns: [ labels above their threshold ]
    """
    motif_hits, matched_phrases = detect_motifs(text)
    emotion_profile = get_emotion_profile(text)

    # get raw model scores
    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()

    # pick up all labels whose score >= threshold
    active = [lab for lab, sc in zip(LABELS, scores) if sc >= THRESHOLDS[lab]]

    return {
        "matched_phrases": matched_phrases,
        "emotion_profile": emotion_profile,
        "active_patterns": active
    }


# ——— 4) Composite wrapper (for multiple inputs) ———————————————————————————————————
def analyze_composite(*texts):
    """
    Accept multiple text inputs, run analyze_message on each, and
    return a single formatted string.
    """
    outputs = []
    for idx, txt in enumerate(texts, start=1):
        if not txt:
            continue
        r = analyze_message(txt)
        block = (
            f"── Message {idx} ──\n"
            f"Matched Phrases: {r['matched_phrases']}\n"
            f"Emotion Profile: {r['emotion_profile']}\n"
            f"Active Patterns: {r['active_patterns']}\n"
        )
        outputs.append(block)
    return "\n".join(outputs) if outputs else "Please enter at least one message."


# ——— 5) Gradio interface ————————————————————————————————————————————————
# adjust how many message inputs you want here:
message_inputs = [gr.Textbox(label=f"Message {i+1}") for i in range(3)]

iface = gr.Interface(
    fn=analyze_composite,
    inputs=message_inputs,
    outputs=gr.Textbox(label="Analysis"),
    title="Tether Analyzer (no abuse-score / no DARVO)",
    description="Detects motifs, emotions, and active abuse-patterns only."
)

if __name__ == "__main__":
    iface.launch()