app.py

import gradio as gr
import spaces
import torch
import numpy as np
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import re
import matplotlib.pyplot as plt
import io
from PIL import Image
from datetime import datetime
from torch.nn.functional import sigmoid
from collections import Counter
import logging
# Add this after imports
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Set up logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)



# Model initialization with error handling
# Model initialization
model_name = "SamanthaStorm/tether-multilabel-v4"
model = AutoModelForSequenceClassification.from_pretrained(model_name).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)

# Tone model
tone_model = AutoModelForSequenceClassification.from_pretrained("SamanthaStorm/tone-tag-multilabel-v1").to(device)
tone_tokenizer = AutoTokenizer.from_pretrained("SamanthaStorm/tone-tag-multilabel-v1", use_fast=False)

# Sentiment model
sentiment_model = AutoModelForSequenceClassification.from_pretrained("SamanthaStorm/tether-sentiment").to(device)
sentiment_tokenizer = AutoTokenizer.from_pretrained("SamanthaStorm/tether-sentiment", use_fast=False)

# DARVO model
darvo_model = AutoModelForSequenceClassification.from_pretrained("SamanthaStorm/tether-darvo-regressor-v1").to(device)
darvo_tokenizer = AutoTokenizer.from_pretrained("SamanthaStorm/tether-darvo-regressor-v1", use_fast=False)


# Constants and Labels
LABELS = [
    "recovery", "control", "gaslighting", "guilt tripping", "dismissiveness", 
    "blame shifting", "nonabusive", "projection", "insults", 
    "contradictory statements", "obscure language"
]

TONE_LABELS = [
    "cold invalidation", "coercive warmth", "contradictory gaslight",
    "deflective hostility", "emotional instability", "nonabusive",
    "performative regret", "emotional threat", "forced accountability flip"
]

SENTIMENT_LABELS = ["undermining", "supportive"]

THRESHOLDS = {
    "recovery": 0.4,
    "control": 0.45,
    "gaslighting": 0.25,
    "guilt tripping": 0.20,
    "dismissiveness": 0.25,
    "blame shifting": 0.25,
    "projection": 0.25,
    "insults": 0.05,
    "contradictory statements": 0.25,
    "obscure language": 0.15,
    "nonabusive": 1.0
}

PATTERN_WEIGHTS = {
    "recovery": 0.7,
    "control": 1.4,
    "gaslighting": 1.50,
    "guilt tripping": 1.2,
    "dismissiveness": 0.9,
    "blame shifting": 0.8,
    "projection": 0.5,
    "insults": 1.4,
    "contradictory statements": 1.0,
    "obscure language": 0.9,
    "nonabusive": 0.0
}

ESCALATION_QUESTIONS = [
    ("Partner has access to firearms or weapons", 4),
    ("Partner threatened to kill you", 3),
    ("Partner threatened you with a weapon", 3),
    ("Partner has ever choked you, even if you considered it consensual at the time", 4),
    ("Partner injured or threatened your pet(s)", 3),
    ("Partner has broken your things, punched or kicked walls, or thrown things ", 2),
    ("Partner forced or coerced you into unwanted sexual acts", 3),
    ("Partner threatened to take away your children", 2),
    ("Violence has increased in frequency or severity", 3),
    ("Partner monitors your calls/GPS/social media", 2)
]

RISK_STAGE_LABELS = {
    1: "🌀 Risk Stage: Tension-Building\nThis message reflects rising emotional pressure or subtle control attempts.",
    2: "🔥 Risk Stage: Escalation\nThis message includes direct or aggressive patterns, suggesting active harm.",
    3: "🌧️ Risk Stage: Reconciliation\nThis message reflects a reset attempt—apologies or emotional repair without accountability.",
    4: "🌸 Risk Stage: Calm / Honeymoon\nThis message appears supportive but may follow prior harm, minimizing it."
}

# Threat Motifs
THREAT_MOTIFS = [
    "i'll kill you", "i'm going to hurt you", "you're dead", "you won't survive this",
    "i'll break your face", "i'll bash your head in", "i'll snap your neck",
    "i'll come over there and make you shut up", "i'll knock your teeth out",
    "you're going to bleed", "you want me to hit you?", "i won't hold back next time",
    "i swear to god i'll beat you", "next time, i won't miss", "i'll make you scream",
    "i know where you live", "i'm outside", "i'll be waiting", "i saw you with him",
    "you can't hide from me", "i'm coming to get you", "i'll find you", "i know your schedule",
    "i watched you leave", "i followed you home", "you'll regret this", "you'll be sorry",
    "you're going to wish you hadn't", "you brought this on yourself", "don't push me",
    "you have no idea what i'm capable of", "you better watch yourself",
    "i don't care what happens to you anymore", "i'll make you suffer", "you'll pay for this",
    "i'll never let you go", "you're nothing without me", "if you leave me, i'll kill myself",
    "i'll ruin you", "i'll tell everyone what you did", "i'll make sure everyone knows",
    "i'm going to destroy your name", "you'll lose everyone", "i'll expose you", 
    "your friends will hate you", "i'll post everything", "you'll be cancelled",
    "you'll lose everything", "i'll take the house", "i'll drain your account",
    "you'll never see a dime", "you'll be broke when i'm done", "i'll make sure you lose your job",
    "i'll take your kids", "i'll make sure you have nothing", "you can't afford to leave me",
    "don't make me do this", "you know what happens when i'm mad", "you're forcing my hand",
    "if you just behaved, this wouldn't happen", "this is your fault",
    "you're making me hurt you", "i warned you", "you should have listened"
]

def predict_darvo_score(text):
    """Predict DARVO score for given text"""
    try:
        inputs = darvo_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
        inputs = {k: v.to(device) for k, v in inputs.items()}
        with torch.no_grad():
            logits = darvo_model(**inputs).logits
        return round(sigmoid(logits.cpu()).item(), 4)
    except Exception as e:
        logger.error(f"Error in DARVO prediction: {e}")
        return 0.0

def detect_weapon_language(text):
    """Detect weapon-related language in text"""
    weapon_keywords = ["knife", "gun", "bomb", "weapon", "kill", "stab"]
    t = text.lower()
    return any(w in t for w in weapon_keywords)

def get_risk_stage(patterns, sentiment):
    """Determine risk stage based on patterns and sentiment"""
    try:
        if "insults" in patterns:
            return 2
        elif "recovery" in patterns:
            return 3
        elif "control" in patterns or "guilt tripping" in patterns:
            return 1
        elif sentiment == "supportive" and any(p in patterns for p in ["projection", "dismissiveness"]):
            return 4
        return 1
    except Exception as e:
        logger.error(f"Error determining risk stage: {e}")
        return 1

def get_emotional_tone_tag(text, emotions, sentiment, patterns, abuse_score):
    """Get emotional tone tag for text"""
    try:
        inputs = tone_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
        inputs = {k: v.to(device) for k, v in inputs.items()}
        with torch.no_grad():
            logits = tone_model(**inputs).logits[0]
        probs = torch.sigmoid(logits).cpu().numpy()
        scores = dict(zip(TONE_LABELS, np.round(probs, 3)))
        return max(scores, key=scores.get)
    except Exception as e:
        logger.error(f"Error in emotional tone analysis: {e}")
        return "unknown"

@spaces.GPU
def compute_abuse_score(matched_scores, sentiment):
    """
    Compute abuse score from matched patterns and sentiment
    """
    try:
        if not matched_scores:
            return 0.0
            
        # Calculate weighted score
        total_weight = sum(weight for _, _, weight in matched_scores)
        if total_weight == 0:
            return 0.0
        
        pattern_scores = [(label, score) for label, score, _ in matched_scores]
        sorted_scores = sorted(pattern_scores, key=lambda x: x[1], reverse=True)
        
        weighted_sum = sum(score * weight for _, score, weight in matched_scores)
        base_score = (weighted_sum / total_weight) * 100
        
        # Apply multipliers
        if len(matched_scores) >= 3:
            base_score *= 1.2
        
        high_severity_patterns = {'gaslighting', 'control', 'blame shifting'}
        if any(label in high_severity_patterns for label, _, _ in matched_scores):
            base_score *= 1.15
        
        if any(score > 0.6 for _, score, _ in matched_scores):
            base_score *= 1.1
            
        high_scores = len([score for _, score, _ in matched_scores if score > 0.5])
        if high_scores >= 2:
            base_score *= 1.15
        
        # Apply sentiment modifiers
        if sentiment == "supportive":
            if any(label in high_severity_patterns for label, _, _ in matched_scores):
                base_score *= 0.9
            else:
                base_score *= 0.85
        elif sentiment == "undermining":
            base_score *= 1.15
        
        if any(score > 0.6 for _, score, _ in matched_scores):
            base_score = max(base_score, 65.0)
        
        return min(round(base_score, 1), 100.0)
    except Exception as e:
        logger.error(f"Error computing abuse score: {e}")
        return 0.0


@spaces.GPU
def analyze_single_message(text, thresholds):
    """Analyze a single message for abuse patterns"""
    logger.debug("\n=== DEBUG START ===")
    logger.debug(f"Input text: {text}")
    
    try:
        if not text.strip():
            logger.debug("Empty text, returning zeros")
            return 0.0, [], [], {"label": "none"}, 1, 0.0, None

        # Check for explicit abuse
        explicit_abuse_words = ['fuck', 'bitch', 'shit', 'ass', 'dick']
        explicit_abuse = any(word in text.lower() for word in explicit_abuse_words)
        logger.debug(f"Explicit abuse detected: {explicit_abuse}")
        
        # Abuse model inference 
        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
        inputs = {k: v.to(device) for k, v in inputs.items()}  # Move to GPU
        
        with torch.no_grad():
            outputs = model(**inputs)
        raw_scores = torch.sigmoid(outputs.logits.squeeze(0)).cpu().numpy()
        
        # Log raw model outputs
        logger.debug("\nRaw model scores:")
        for label, score in zip(LABELS, raw_scores):
            logger.debug(f"{label}: {score:.3f}")

        # Get predictions and sort them
        predictions = list(zip(LABELS, raw_scores))
        sorted_predictions = sorted(predictions, key=lambda x: x[1], reverse=True)
        logger.debug("\nTop 3 predictions:")
        for label, score in sorted_predictions[:3]:
            logger.debug(f"{label}: {score:.3f}")
        
        # Apply thresholds
        threshold_labels = []
        if explicit_abuse:
            threshold_labels.append("insults")
            logger.debug("\nForced inclusion of 'insults' due to explicit abuse")
            
        for label, score in sorted_predictions:
            base_threshold = thresholds.get(label, 0.25)
            if explicit_abuse:
                base_threshold *= 0.5
            if score > base_threshold:
                if label not in threshold_labels:  # Avoid duplicates
                    threshold_labels.append(label)
        
        logger.debug("\nLabels that passed thresholds:", threshold_labels)

        # Calculate matched scores
        matched_scores = []
        for label in threshold_labels:
            score = raw_scores[LABELS.index(label)]
            weight = PATTERN_WEIGHTS.get(label, 1.0)
            if explicit_abuse and label == "insults":
                weight *= 1.5
            matched_scores.append((label, score, weight))
        
        logger.debug("\nMatched scores (label, score, weight):", matched_scores)

        # Get sentiment
        sent_inputs = sentiment_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
        sent_inputs = {k: v.to(device) for k, v in sent_inputs.items()}
        with torch.no_grad():
            sent_logits = sentiment_model(**sent_inputs).logits[0]
        sent_probs = torch.softmax(sent_logits, dim=-1).cpu().numpy()
        sentiment = SENTIMENT_LABELS[int(np.argmax(sent_probs))]
        logger.debug(f"\nDetected sentiment: {sentiment}")

        # Get tone
        tone_inputs = tone_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
        tone_inputs = {k: v.to(device) for k, v in tone_inputs.items()}
        with torch.no_grad():
            tone_logits = tone_model(**tone_inputs).logits[0]
        tone_probs = torch.sigmoid(tone_logits).cpu().numpy()
        tone_tag = TONE_LABELS[int(np.argmax(tone_probs))]
        logger.debug(f"Detected tone: {tone_tag}")

        # Get DARVO score
        darvo_score = predict_darvo_score(text)
        logger.debug(f"DARVO score: {darvo_score}")

        # Calculate abuse score
        if not matched_scores:
            logger.debug("No matched scores, returning 0")
            return 0.0, [], [], {"label": "undermining"}, 2 if explicit_abuse else 1, 0.0, None

        abuse_score = compute_abuse_score(matched_scores, sentiment)
        
        if explicit_abuse:
            abuse_score = max(abuse_score, 70.0)
        
        logger.debug(f"\nCalculated abuse score: {abuse_score}")

        # Set stage
        stage = 2 if explicit_abuse or abuse_score > 70 else 1
        logger.debug(f"Final stage: {stage}")
        
         logger.debug("=== DEBUG END ===\n")
        return abuse_score, threshold_labels, matched_scores, {"label": sentiment}, stage, darvo_score, tone_tag

    except Exception as e:
        logger.error(f"Error in analyze_single_message: {e}")
        return 0.0, [], [], {"label": "error"}, 1, 0.0, None
def generate_abuse_score_chart(dates, scores, patterns):
    """Generate a timeline chart of abuse scores"""
    try:
        plt.figure(figsize=(10, 6))
        plt.clf()
        
        # Create new figure
        fig, ax = plt.subplots(figsize=(10, 6))
        
        # Plot points and lines
        x = range(len(scores))
        plt.plot(x, scores, 'bo-', linewidth=2, markersize=8)
        
        # Add labels for each point
        for i, (score, pattern) in enumerate(zip(scores, patterns)):
            plt.annotate(
                f'{pattern}\n{score:.0f}%',
                (i, score),
                textcoords="offset points",
                xytext=(0, 10),
                ha='center',
                bbox=dict(
                    boxstyle='round,pad=0.5',
                    fc='white',
                    ec='gray',
                    alpha=0.8
                )
            )
        
        # Customize the plot
        plt.ylim(-5, 105)
        plt.grid(True, linestyle='--', alpha=0.7)
        plt.title('Abuse Pattern Timeline', pad=20, fontsize=12)
        plt.ylabel('Abuse Score %')
        
        # X-axis labels
        plt.xticks(x, dates, rotation=45)
        
        # Risk level bands
        plt.axhspan(0, 50, color='#90EE90', alpha=0.2)   # light green
        plt.axhspan(50, 70, color='#FFD700', alpha=0.2)  # gold
        plt.axhspan(70, 85, color='#FFA500', alpha=0.2)  # orange
        plt.axhspan(85, 100, color='#FF6B6B', alpha=0.2) # light red
        
        # Add risk level labels
        plt.text(-0.2, 25, 'Low Risk', rotation=90, va='center')
        plt.text(-0.2, 60, 'Moderate Risk', rotation=90, va='center')
        plt.text(-0.2, 77.5, 'High Risk', rotation=90, va='center')
        plt.text(-0.2, 92.5, 'Critical Risk', rotation=90, va='center')
        
        # Adjust layout
        plt.tight_layout()
        
        # Convert plot to image
        buf = io.BytesIO()
        plt.savefig(buf, format='png', bbox_inches='tight')
        buf.seek(0)
        plt.close('all')  # Close all figures to prevent memory leaks
        
        return Image.open(buf)
    except Exception as e:
        logger.error(f"Error generating abuse score chart: {e}")
        return None

def analyze_composite(msg1, msg2, msg3, *answers_and_none):
    """Analyze multiple messages and checklist responses"""
    try:
        # Process checklist responses
        none_selected_checked = answers_and_none[-1]
        responses_checked = any(answers_and_none[:-1])
        none_selected = not responses_checked and none_selected_checked

        # Determine escalation score
        if none_selected:
            escalation_score = 0
            escalation_note = "Checklist completed: no danger items reported."
            escalation_completed = True
        elif responses_checked:
            escalation_score = sum(w for (_, w), a in zip(ESCALATION_QUESTIONS, answers_and_none[:-1]) if a)
            escalation_note = "Checklist completed."
            escalation_completed = True
        else:
            escalation_score = None
            escalation_note = "Checklist not completed."
            escalation_completed = False

        # Process messages
        messages = [msg1, msg2, msg3]
        active = [(m, f"Message {i+1}") for i, m in enumerate(messages) if m.strip()]
        if not active:
            return "Please enter at least one message.", None

        # Detect threats
        def normalize(text):
            import unicodedata
            text = text.lower().strip()
            text = unicodedata.normalize("NFKD", text)
            text = text.replace("'", "'")
            return re.sub(r"[^a-z0-9 ]", "", text)

        def detect_threat_motifs(message, motif_list):
            norm_msg = normalize(message)
            return [motif for motif in motif_list if normalize(motif) in norm_msg]

        # Analyze threats and patterns
        immediate_threats = [detect_threat_motifs(m, THREAT_MOTIFS) for m, _ in active]
        flat_threats = [t for sublist in immediate_threats for t in sublist]
        threat_risk = "Yes" if flat_threats else "No"
        
        # Analyze each message
        results = [(analyze_single_message(m, THRESHOLDS.copy()), d) for m, d in active]

        # Extract scores and metadata
        abuse_scores = [r[0][0] for r in results]
        stages = [r[0][4] for r in results]
        darvo_scores = [r[0][5] for r in results]
        tone_tags = [r[0][6] for r in results]
        dates_used = [r[1] for r in results]

        # Analyze patterns
        predicted_labels = [label for r in results for label in r[0][1]]
        high = {'control'}
        moderate = {'gaslighting', 'dismissiveness', 'obscure language', 'insults', 
                   'contradictory statements', 'guilt tripping'}
        low = {'blame shifting', 'projection', 'recovery phase'}
        
        counts = {'high': 0, 'moderate': 0, 'low': 0}
        for label in predicted_labels:
            if label in high:
                counts['high'] += 1
            elif label in moderate:
                counts['moderate'] += 1
            elif label in low:
                counts['low'] += 1

        # Determine pattern escalation risk
        if counts['high'] >= 2 and counts['moderate'] >= 2:
            pattern_escalation_risk = "Critical"
        elif (counts['high'] >= 2 and counts['moderate'] >= 1) or \
             (counts['moderate'] >= 3) or \
             (counts['high'] >= 1 and counts['moderate'] >= 2):
            pattern_escalation_risk = "High"
        elif (counts['moderate'] == 2) or \
             (counts['high'] == 1 and counts['moderate'] == 1) or \
             (counts['moderate'] == 1 and counts['low'] >= 2) or \
             (counts['high'] == 1 and sum(counts.values()) == 1):
            pattern_escalation_risk = "Moderate"
        else:
            pattern_escalation_risk = "Low"

        # Calculate escalation risk
        checklist_escalation_risk = "Unknown" if escalation_score is None else (
            "Critical" if escalation_score >= 20 else
            "Moderate" if escalation_score >= 10 else
            "Low"
        )

        # Calculate escalation bump
        escalation_bump = 0
        for result, _ in results:
            abuse_score, _, _, sentiment, stage, darvo_score, tone_tag = result
            if darvo_score > 0.65:
                escalation_bump += 3
            if tone_tag in ["forced accountability flip", "emotional threat"]:
                escalation_bump += 2
            if abuse_score > 80:
                escalation_bump += 2
            if stage == 2:
                escalation_bump += 3

        # Calculate combined risk
        def rank(label):
            return {"Low": 0, "Moderate": 1, "High": 2, "Critical": 3, "Unknown": 0}.get(label, 0)

        combined_score = rank(pattern_escalation_risk) + rank(checklist_escalation_risk) + escalation_bump
        escalation_risk = (
            "Critical" if combined_score >= 6 else
            "High" if combined_score >= 4 else
            "Moderate" if combined_score >= 2 else
            "Low"
        )

        # Build escalation text
        if escalation_score is None:
            escalation_text = (
                "🚫 **Escalation Potential: Unknown** (Checklist not completed)\n"
                "⚠️ This section was not completed. Escalation potential is estimated using message data only.\n"
            )
            hybrid_score = 0
        elif escalation_score == 0:
            escalation_text = (
                "✅ **Escalation Checklist Completed:** No danger items reported.\n"
                "🧭 **Escalation potential estimated from detected message patterns only.**\n"
                f"• Pattern Risk: {pattern_escalation_risk}\n"
                f"• Checklist Risk: None reported\n"
                f"• Escalation Bump: +{escalation_bump} (from DARVO, tone, intensity, etc.)"
            )
            hybrid_score = escalation_bump
        else:
            hybrid_score = escalation_score + escalation_bump
            escalation_text = (
                f"📈 **Escalation Potential: {escalation_risk} ({hybrid_score}/29)**\n"
                "📋 This score combines your safety checklist answers *and* detected high-risk behavior.\n"
                f"• Pattern Risk: {pattern_escalation_risk}\n"
                f"• Checklist Risk: {checklist_escalation_risk}\n"
                f"• Escalation Bump: +{escalation_bump} (from DARVO, tone, intensity, etc.)"
            )

        # Calculate composite abuse score
        composite_abuse = int(round(sum(abuse_scores) / len(abuse_scores)))

        # Get most common stage
        most_common_stage = max(set(stages), key=stages.count)
        stage_text = RISK_STAGE_LABELS[most_common_stage]

        # Get top labels
        top_labels = []
        for result, _ in results:
            threshold_labels = result[1]
            if threshold_labels:
                top_labels.append(threshold_labels[0])
            else:
                top_labels.append("none")

        # Calculate average DARVO score
        avg_darvo = round(sum(darvo_scores) / len(darvo_scores), 3)
        darvo_blurb = ""
        if avg_darvo > 0.25:
            level = "moderate" if avg_darvo < 0.65 else "high"
            darvo_blurb = f"\n\n🎭 **DARVO Score: {avg_darvo}** → This indicates a **{level} likelihood** of narrative reversal (DARVO), where the speaker may be denying, attacking, or reversing blame."

        # Build output text
        out = f"Abuse Intensity: {composite_abuse}%\n"
        out += "📊 This reflects the strength and severity of detected abuse patterns in the message(s).\n\n"
        out += generate_risk_snippet(composite_abuse, top_labels[0], hybrid_score, most_common_stage)
        out += f"\n\n{stage_text}"
        out += darvo_blurb
        out += "\n\n🎭 **Emotional Tones Detected:**\n"
        for i, tone in enumerate(tone_tags):
            out += f"• Message {i+1}: *{tone or 'none'}*\n"

        # Add threat section
        if flat_threats:
            out += "\n\n🚨 **Immediate Danger Threats Detected:**\n"
            for t in set(flat_threats):
                out += f"• \"{t}\"\n"
            out += "\n⚠️ These phrases may indicate an imminent risk to physical safety."
        else:
            out += "\n\n🧩 **Immediate Danger Threats:** None explicitly detected.\n"
            out += "This does *not* rule out risk, but no direct threat phrases were matched."

        # Generate timeline
        pattern_labels = [
            pats[0][0] if (pats := r[0][2]) else "none"
            for r in results
        ]
        timeline_image = generate_abuse_score_chart(dates_used, abuse_scores, pattern_labels)
        
        # Add escalation text
        out += "\n\n" + escalation_text
        return out, timeline_image

    except Exception as e:
        logger.error(f"Error in analyze_composite: {e}")
        return "An error occurred during analysis.", None

# Gradio Interface Setup
def create_interface():
    try:
        textbox_inputs = [gr.Textbox(label=f"Message {i+1}") for i in range(3)]
        quiz_boxes = [gr.Checkbox(label=q) for q, _ in ESCALATION_QUESTIONS]
        none_box = gr.Checkbox(label="None of the above")

        demo = gr.Interface(
            fn=analyze_composite,
            inputs=textbox_inputs + quiz_boxes + [none_box],
            outputs=[
                gr.Textbox(label="Results"),
                gr.Image(label="Abuse Score Timeline", type="pil")
            ],
            title="Abuse Pattern Detector + Escalation Quiz",
            description=(
                "Enter up to three messages that concern you. "
                "For the most accurate results, include messages from a recent emotionally intense period."
            ),
            flagging_mode="manual"
        )
        return demo
    except Exception as e:
        logger.error(f"Error creating interface: {e}")
        raise


if __name__ == "__main__":
    try:
        demo = create_interface()
        demo.launch(
            server_name="0.0.0.0",
            server_port=7860,
            share=False
        )
    except Exception as e:
        print(f"Error launching app: {e}")