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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) Emotional-tone tagging (no abuse_score / DARVO) ————————————————————————————— | |
def get_tone_tag(emotion_profile, patterns): | |
anger = emotion_profile.get("anger", 0) | |
disgust = emotion_profile.get("disgust", 0) | |
sadness = emotion_profile.get("sadness", 0) | |
joy = emotion_profile.get("joy", 0) | |
neutral = emotion_profile.get("neutral", 0) | |
fear = emotion_profile.get("fear", 0) | |
# 1) Vulnerable: sadness high + recovery-phase | |
if sadness > 0.4 and "recovery phase" in patterns: | |
return "vulnerable" | |
# 2) Supportive: joy very high + no other patterns (or only recovery-phase) | |
if joy > 0.5 and (not patterns or patterns == ["recovery phase"]): | |
return "supportive" | |
# 3) Confrontational: anger/disgust high + aggressive patterns | |
if (anger + disgust) > 0.5 and any(p in patterns for p in ["insults", "control", "threat"]): | |
return "confrontational" | |
# 4) Manipulative: neutral high + classic manipulation patterns | |
if neutral > 0.4 and any(p in patterns for p in ["gaslighting", "dismissiveness", "projection", "guilt tripping", "blame shifting"]): | |
return "manipulative" | |
# 5) Feigned Warmth: joy high but manipulative patterns present | |
if joy > 0.5 and any(p in patterns for p in ["gaslighting", "dismissiveness", "projection", "guilt tripping", "blame shifting"]): | |
return "feigned warmth" | |
# 6) Defensive: anger high + contradictory statements | |
if anger > 0.4 and "contradictory statements" in patterns: | |
return "defensive" | |
# 7) Neutral: pure neutral dominates all | |
if neutral > max(anger, disgust, sadness, joy, fear): | |
return "neutral" | |
return None | |
# ——— 3) Single-message analysis —————————————————————————————————————————————— | |
def analyze_message(text): | |
""" | |
Runs emotion profiling, and the abuse-pattern classifier. | |
Returns a dict with: | |
- emotion_profile: { emotion: score } | |
- active_patterns: [ labels above their threshold ] | |
""" | |
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 { | |
"emotion_profile": emotion_profile, | |
"active_patterns": active, | |
"tone_tag": tone_tag | |
} | |
# ——— 5) Composite wrapper (handles .txt or image + text boxes) —————————————————————— | |
def analyze_composite(uploaded_file, *texts): | |
outputs = [] | |
if uploaded_file is not None: | |
raw = uploaded_file.read() | |
name = uploaded_file.name.lower() | |
if name.endswith((".png", ".jpg", ".jpeg", ".tiff", ".bmp", ".gif")): | |
img = Image.open(io.BytesIO(raw)) | |
content = pytesseract.image_to_string(img) | |
else: | |
try: | |
content = raw.decode("utf-8") | |
except UnicodeDecodeError: | |
content = raw.decode("latin-1") | |
r = analyze_message(content) | |
outputs.append( | |
"── Uploaded File ──\n" | |
f"Emotion Profile : {r['emotion_profile']}\n" | |
f"Active Patterns : {r['active_patterns']}\n" | |
f"Emotional Tone : {r['tone_tag']}\n" | |
) | |
for idx, txt in enumerate(texts, start=1): | |
if not txt: | |
continue | |
r = analyze_message(txt) | |
outputs.append( | |
f"── Message {idx} ──\n" | |
f"Emotion Profile : {r['emotion_profile']}\n" | |
f"Active Patterns : {r['active_patterns']}\n" | |
f"Emotional Tone : {r['tone_tag']}\n" | |
) | |
return "\n".join(outputs) if outputs else "Please enter at least one message." | |
# ——— 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, | |
outputs=gr.Textbox(label="Analysis"), | |
title="Tether Analyzer (with Tone Tags)", | |
description="Extracts motifs, emotions, patterns—and now an emotional tone tag—no abuse score or DARVO." | |
) | |
if __name__ == "__main__": | |
iface.launch() |