app.py

import gradio as gr
import torch
from transformers import pipeline as hf_pipeline, AutoModelForSequenceClassification, AutoTokenizer
from PIL import Image
import pytesseract
import io

# ——— 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):
    results = emotion_pipeline(text)
    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 (detailed categories) —————————————————————————————
def get_emotional_tone_tag(emotion_profile, patterns):
    # unpack emotion scores
    sadness = emotion_profile.get("sadness", 0)
    joy     = emotion_profile.get("joy",     0)
    neutral = emotion_profile.get("neutral", 0)
    disgust = emotion_profile.get("disgust", 0)
    anger   = emotion_profile.get("anger",   0)
    fear    = emotion_profile.get("fear",    0)

    # 1. Performative Regret
    if (
        sadness > 0.4 and
        any(p in patterns for p in ["blame shifting", "guilt tripping", "recovery phase"])
    ):
        return "performative regret"

    # 2. Coercive Warmth
    if (
        (joy > 0.3 or sadness > 0.4) and
        any(p in patterns for p in ["control", "gaslighting"])
    ):
        return "coercive warmth"

    # 3. Cold Invalidation
    if (
        (neutral + disgust) > 0.5 and
        any(p in patterns for p in ["dismissiveness", "projection", "obscure language"])
    ):
        return "cold invalidation"

    # 4. Genuine Vulnerability
    if (
        (sadness + fear) > 0.5 and
        all(p == "recovery phase" for p in patterns)
    ):
        return "genuine vulnerability"

    # 5. Emotional Threat
    if (
        (anger + disgust) > 0.5 and
        any(p in patterns for p in ["control", "threat", "insults", "dismissiveness"])
    ):
        return "emotional threat"

    # 6. Weaponized Sadness
    if (
        sadness > 0.6 and
        any(p in patterns for p in ["guilt tripping", "projection"])
    ):
        return "weaponized sadness"

    # 7. Toxic Resignation
    if (
        neutral > 0.5 and
        any(p in patterns for p in ["dismissiveness", "obscure language"])
    ):
        return "toxic resignation"
        
    # 8. Indignant Reproach
    if (
        anger > 0.5 and
        any(p in patterns for p in ["guilt tripping","contradictory statements"]
     ):
        return "indignant reproach"
    if (
        anger > 0.6 and patterns
    ):
        return "confrontational"
    # 8. Passive Aggression
    if neutral > 0.6 and any(p in patterns for p in ["dismissiveness","projection"]):
        return "passive aggression"
    # 9. Sarcastic Mockery
    if joy > 0.3 and "insults" in patterns:
        return "sarcastic mockery"

    return None


# ——— 4) Single-message analysis —————————————————————————————————————————————
def analyze_message(text):
    # 1) emotion profiling
    emotion_profile = get_emotion_profile(text)

    # 2) abuse-pattern classification
    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()

    # 3) identify active patterns
    active_patterns = [
        label for label, prob in zip(LABELS, scores)
        if prob >= THRESHOLDS[label]
    ]

    # 4) add recovery-phase on apology keywords
    low = text.lower()
    if any(k in low for k in ["sorry", "apolog", "forgive"]):
        if "recovery phase" not in active_patterns:
            active_patterns.append("recovery phase")

    # 5) tone tagging with detailed rules
    tone_tag = get_emotional_tone_tag(emotion_profile, active_patterns)

    return {
        "emotion_profile": emotion_profile,
        "active_patterns": active_patterns,
        "tone_tag": tone_tag
    }


# ——— 5) Composite wrapper (handles uploads + text boxes) ——————————————————————————
def analyze_composite(uploaded_file, *texts):
    outputs = []

    # file upload
    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"
        )

    # text inputs
    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"
        )

    if not outputs:
        return "Please enter at least one message."

    return "\n".join(outputs)


# ——— 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 (detailed tone tags)",
    description="Emotion profiling, pattern tags, and nuanced tone categories—no abuse score or DARVO."
)

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