Update app.py

app.py

@@ -2,51 +2,19 @@ import gradio as gr
 import torch
 import numpy as np
 from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
-from motif_tagging import detect_motifs
-import re
 import matplotlib.pyplot as plt
 import io
 from PIL import Image
 from datetime import datetime
 
-# --- Timeline Visualization Function ---
-def generate_abuse_score_chart(dates, scores, labels):
-    import matplotlib.pyplot as plt
-    import io
-    from PIL import Image
-    from datetime import datetime
-
-    try:
-        parsed_dates = [datetime.strptime(d, "%Y-%m-%d") for d in dates]
-    except Exception:
-        parsed_dates = list(range(len(dates)))
-
-    fig, ax = plt.subplots(figsize=(8, 3))
-    ax.plot(parsed_dates, scores, marker='o', linestyle='-', color='darkred', linewidth=2)
-
-    for i, (x, y) in enumerate(zip(parsed_dates, scores)):
-        label = labels[i]
-        ax.text(x, y + 2, f"{label}\n{int(y)}%", ha='center', fontsize=8, color='black')
-
-    ax.set_title("Abuse Intensity Over Time")
-    ax.set_xlabel("Date")
-    ax.set_ylabel("Abuse Score (%)")
-    ax.set_ylim(0, 105)
-    ax.grid(True)
-    plt.tight_layout()
-
-    buf = io.BytesIO()
-    plt.savefig(buf, format='png')
-    buf.seek(0)
-    return Image.open(buf)
-# --- SST Sentiment Model ---
-sst_pipeline = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
-
-# --- Abuse Model ---
-model_name = "SamanthaStorm/tether-multilabel-v1"
+# --- Load models ---
+model_name = "SamanthaStorm/tether-multilabel-v2"  # UPDATE if needed
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
+healthy_detector = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
+sst_pipeline = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+
 LABELS = [
     "blame shifting", "contradictory statements", "control", "dismissiveness",
     "gaslighting", "guilt tripping", "insults", "obscure language",
@@ -54,253 +22,103 @@ LABELS = [
 ]
 
 THRESHOLDS = {
-    "blame shifting": 0.3, "contradictory statements": 0.36, "control": 0.48, "dismissiveness": 0.45,
-    "gaslighting": 0.30, "guilt tripping": 0.20, "insults": 0.34, "obscure language": 0.25,
-    "projection": 0.35, "recovery phase": 0.25, "threat": 0.25
-}
-
-PATTERN_WEIGHTS = {
-    "gaslighting": 1.3,
-    "control": 1.2,
-    "dismissiveness": 0.8,
-    "blame shifting": 0.8,
-    "contradictory statements": 0.75,
-    "threat": 1.5  # 🔧 New: raise weight for threat
-}
-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."
+    "blame shifting": 0.3, "contradictory statements": 0.3, "control": 0.35, "dismissiveness": 0.4,
+    "gaslighting": 0.3, "guilt tripping": 0.3, "insults": 0.3, "obscure language": 0.4,
+    "projection": 0.4, "recovery phase": 0.35, "threat": 0.3
 }
 
 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 has ever choked you", 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 has broken your things, punched walls, or thrown objects", 2),
+    ("Partner forced 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)
+    ("Partner monitors your calls, GPS, or social media", 2)
 ]
-DARVO_PATTERNS = {
-    "blame shifting", "projection", "dismissiveness", "guilt tripping", "contradictory statements"
-}
-DARVO_MOTIFS = [
-    "I never said that.", "You’re imagining things.", "That never happened.",
-    "You’re making a big deal out of nothing.", "It was just a joke.", "You’re too sensitive.",
-    "I don’t know what you’re talking about.", "You’re overreacting.", "I didn’t mean it that way.",
-    "You’re twisting my words.", "You’re remembering it wrong.", "You’re always looking for something to complain about.",
-    "You’re just trying to start a fight.", "I was only trying to help.", "You’re making things up.",
-    "You’re blowing this out of proportion.", "You’re being paranoid.", "You’re too emotional.",
-    "You’re always so dramatic.", "You’re just trying to make me look bad.",
-
-    "You’re crazy.", "You’re the one with the problem.", "You’re always so negative.",
-    "You’re just trying to control me.", "You’re the abusive one.", "You’re trying to ruin my life.",
-    "You’re just jealous.", "You’re the one who needs help.", "You’re always playing the victim.",
-    "You’re the one causing all the problems.", "You’re just trying to make me feel guilty.",
-    "You’re the one who can’t let go of the past.", "You’re the one who’s always angry.",
-    "You’re the one who’s always complaining.", "You’re the one who’s always starting arguments.",
-    "You’re the one who’s always making things worse.", "You’re the one who’s always making me feel bad.",
-    "You’re the one who’s always making me look like the bad guy.",
-    "You’re the one who’s always making me feel like a failure.",
-    "You’re the one who’s always making me feel like I’m not good enough.",
-
-    "I can’t believe you’re doing this to me.", "You’re hurting me.",
-    "You’re making me feel like a terrible person.", "You’re always blaming me for everything.",
-    "You’re the one who’s abusive.", "You’re the one who’s controlling.", "You’re the one who’s manipulative.",
-    "You’re the one who’s toxic.", "You’re the one who’s gaslighting me.",
-    "You’re the one who’s always putting me down.", "You’re the one who’s always making me feel bad.",
-    "You’re the one who’s always making me feel like I’m not good enough.",
-    "You’re the one who’s always making me feel like I’m the problem.",
-    "You’re the one who’s always making me feel like I’m the bad guy.",
-    "You’re the one who’s always making me feel like I’m the villain.",
-    "You’re the one who’s always making me feel like I’m the one who needs to change.",
-    "You’re the one who’s always making me feel like I’m the one who’s wrong.",
-    "You’re the one who’s always making me feel like I’m the one who’s crazy.",
-    "You’re the one who’s always making me feel like I’m the one who’s abusive.",
-    "You’re the one who’s always making me feel like I’m the one who’s toxic."
-]
-def detect_contradiction(message):
-    patterns = [
-        (r"\b(i love you).{0,15}(i hate you|you ruin everything)", re.IGNORECASE),
-        (r"\b(i’m sorry).{0,15}(but you|if you hadn’t)", re.IGNORECASE),
-        (r"\b(i’m trying).{0,15}(you never|why do you)", re.IGNORECASE),
-        (r"\b(do what you want).{0,15}(you’ll regret it|i always give everything)", re.IGNORECASE),
-        (r"\b(i don’t care).{0,15}(you never think of me)", re.IGNORECASE),
-        (r"\b(i guess i’m just).{0,15}(the bad guy|worthless|never enough)", re.IGNORECASE)
-    ]
-    return any(re.search(p, message, flags) for p, flags in patterns)
-    
-def calculate_darvo_score(patterns, sentiment_before, sentiment_after, motifs_found, contradiction_flag=False):
-    pattern_hits = len([p for p in patterns if p in DARVO_PATTERNS])
-    pattern_score = pattern_hits / len(DARVO_PATTERNS)
-    
-    sentiment_shift_score = max(0.0, sentiment_after - sentiment_before)
 
-    motif_hits = len([
-        motif for motif in motifs_found
-        if any(phrase.lower() in motif.lower() for phrase in DARVO_MOTIFS)
-    ])
-    motif_score = motif_hits / len(DARVO_MOTIFS)
+# --- Functions ---
 
-    contradiction_score = 1.0 if contradiction_flag else 0.0
+def is_healthy_message(text, threshold=0.9):
+    result = healthy_detector(text)[0]
+    return result['label'] == "POSITIVE" and result['score'] > threshold
 
-    return round(min(
-        0.3 * pattern_score +
-        0.3 * sentiment_shift_score +
-        0.25 * motif_score +
-        0.15 * contradiction_score, 1.0
-    ), 3)
-def detect_weapon_language(text):
-    weapon_keywords = [
-        "knife", "knives", "stab", "cut you", "cutting",
-        "gun", "shoot", "rifle", "firearm", "pistol",
-        "bomb", "blow up", "grenade", "explode",
-        "weapon", "armed", "loaded", "kill you", "take you out"
-    ]
-    text_lower = text.lower()
-    return any(word in text_lower for word in weapon_keywords)
-def get_risk_stage(patterns, sentiment):
-    if "threat" in patterns or "insults" in patterns:
-        return 2
-    elif "recovery phase" 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
-
-def generate_risk_snippet(abuse_score, top_label, escalation_score, stage):
-    if abuse_score >= 85 or escalation_score >= 16:
-        risk_level = "high"
-    elif abuse_score >= 60 or escalation_score >= 8:
-        risk_level = "moderate"
-    elif stage == 2 and abuse_score >= 40:
-        risk_level = "moderate"  # 🔧 New rule for escalation stage
-    else:
-        risk_level = "low"
-    pattern_label = top_label.split(" – ")[0]
-    pattern_score = top_label.split(" – ")[1] if " – " in top_label else ""
-
-    WHY_FLAGGED = {
-        "control": "This message may reflect efforts to restrict someone’s autonomy, even if it's framed as concern or care.",
-        "gaslighting": "This message could be manipulating someone into questioning their perception or feelings.",
-        "dismissiveness": "This message may include belittling, invalidating, or ignoring the other person’s experience.",
-        "insults": "Direct insults often appear in escalating abusive dynamics and can erode emotional safety.",
-        "threat": "This message includes threatening language, which is a strong predictor of harm.",
-        "blame shifting": "This message may redirect responsibility to avoid accountability, especially during conflict.",
-        "guilt tripping": "This message may induce guilt in order to control or manipulate behavior.",
-        "recovery phase": "This message may be part of a tension-reset cycle, appearing kind but avoiding change.",
-        "projection": "This message may involve attributing the abuser’s own behaviors to the victim.",
-        "default": "This message contains language patterns that may affect safety, clarity, or emotional autonomy."
-    }
+def generate_abuse_score_chart(dates, scores, labels):
+    try:
+        parsed_dates = [datetime.strptime(d, "%Y-%m-%d") for d in dates]
+    except Exception:
+        parsed_dates = list(range(len(dates)))
 
-    explanation = WHY_FLAGGED.get(pattern_label.lower(), WHY_FLAGGED["default"])
+    fig, ax = plt.subplots(figsize=(8, 3))
+    ax.plot(parsed_dates, scores, marker='o', linestyle='-', color='darkred', linewidth=2)
 
-    base = f"\n\n🛑 Risk Level: {risk_level.capitalize()}\n"
-    base += f"This message shows strong indicators of **{pattern_label}**. "
+    for i, (x, y) in enumerate(zip(parsed_dates, scores)):
+        label = labels[i]
+        ax.text(x, y + 2, f"{label}\n{int(y)}%", ha='center', fontsize=8, color='black')
 
-    if risk_level == "high":
-        base += "The language may reflect patterns of emotional control, even when expressed in soft or caring terms.\n"
-    elif risk_level == "moderate":
-        base += "There are signs of emotional pressure or indirect control that may escalate if repeated.\n"
-    else:
-        base += "The message does not strongly indicate abuse, but it's important to monitor for patterns.\n"
+    ax.set_title("Abuse Intensity Over Time")
+    ax.set_xlabel("Date")
+    ax.set_ylabel("Abuse Score (%)")
+    ax.set_ylim(0, 105)
+    ax.grid(True)
+    plt.tight_layout()
 
-    base += f"\n💡 *Why this might be flagged:*\n{explanation}\n"
-    base += f"\nDetected Pattern: **{pattern_label} ({pattern_score})**\n"
-    base += "🧠 You can review the pattern in context. This tool highlights possible dynamics—not judgments."
+    buf = io.BytesIO()
+    plt.savefig(buf, format='png')
+    buf.seek(0)
+    return Image.open(buf)
 
-    return base
-def analyze_single_message(text, thresholds):
-        # NEW: Quick healthy check
+def analyze_single_message(text):
     if is_healthy_message(text):
-        print("✅ Message detected as healthy. Skipping abuse detection.")
-        abuse_score = 0
-        threshold_labels = []
-        top_patterns = []
-        sentiment_result = {"label": "POSITIVE", "score": 1.0}
-        stage = 4  # Calm/Honeymoon
-        darvo_score = 0.0
-        return abuse_score, threshold_labels, top_patterns, sentiment_result, stage, darvo_score
-    motif_hits, matched_phrases = detect_motifs(text)
-    result = sst_pipeline(text)[0]
-    sentiment = "supportive" if result['label'] == "POSITIVE" else "undermining"
-    sentiment_score = result['score'] if sentiment == "undermining" else 0.0
-    weapon_flag = detect_weapon_language(text)
-    adjusted_thresholds = {
-        k: v + 0.05 if sentiment == "supportive" else v
-        for k, v in thresholds.items()
-    }
-
-    contradiction_flag = detect_contradiction(text)
+        return {
+            "abuse_score": 0,
+            "labels": [],
+            "sentiment": "supportive",
+            "stage": 4,
+            "top_patterns": [],
+        }
 
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
     with torch.no_grad():
         outputs = model(**inputs)
-    scores = torch.sigmoid(outputs.logits.squeeze(0)).numpy()
+    logits = outputs.logits.squeeze(0)
+    probs = torch.sigmoid(logits).numpy()
 
-    threshold_labels = [
-        label for label, score in zip(LABELS, scores)
-        if score > adjusted_thresholds[label]
-]
+    detected_labels = [
+        label for label, prob in zip(LABELS, probs)
+        if prob > THRESHOLDS.get(label, 0.3)
+    ]
 
-    motifs = [phrase for _, phrase in matched_phrases]
+    abuse_score = (sum(probs[i] for i, label in enumerate(LABELS) if label in detected_labels) / len(LABELS)) * 100
+
+    sentiment_result = sst_pipeline(text)[0]
+    sentiment = "supportive" if sentiment_result['label'] == "POSITIVE" else "undermining"
+
+    if "threat" in detected_labels or "insults" in detected_labels:
+        stage = 2  # Escalation
+    elif "control" in detected_labels or "guilt tripping" in detected_labels:
+        stage = 1  # Tension building
+    elif "recovery phase" in detected_labels:
+        stage = 3  # Reconciliation
+    else:
+        stage = 1
 
-    darvo_score = calculate_darvo_score(
-        threshold_labels,
-        sentiment_before=0.0,
-        sentiment_after=sentiment_score,
-        motifs_found=motifs,
-        contradiction_flag=contradiction_flag
-)
     top_patterns = sorted(
-        [(label, score) for label, score in zip(LABELS, scores)],
+        [(label, prob) for label, prob in zip(LABELS, probs)],
         key=lambda x: x[1],
         reverse=True
     )[:2]
 
-    # Compute weighted average across all patterns (not just top 2)
-    weighted_total = 0.0
-    weight_sum = 0.0
-    for label, score in zip(LABELS, scores):
-        weight = PATTERN_WEIGHTS.get(label, 1.0)
-        weighted_total += score * weight
-        weight_sum += weight
-
-    abuse_score_raw = (weighted_total / weight_sum) * 100
-    stage = get_risk_stage(threshold_labels, sentiment)
-    if weapon_flag:
-        abuse_score_raw = min(abuse_score_raw + 25, 100)  # boost intensity
-    if weapon_flag and stage < 2:
-        stage = 2
-    if weapon_flag:
-        print("⚠️ Weapon-related language detected.")
-
-    if "threat" in threshold_labels or "control" in threshold_labels or "insults" in threshold_labels:
-        abuse_score = min(abuse_score_raw, 100)
-    else:
-        abuse_score = min(abuse_score_raw, 95)
-
-    
-
-    print("\n--- Debug Info ---")
-    print(f"Text: {text}")
-    print(f"Sentiment: {sentiment} (raw: {result['label']}, score: {result['score']:.3f})")
-    print("Abuse Pattern Scores:")
-    for label, score in zip(LABELS, scores):
-        passed = "✅" if score > adjusted_thresholds[label] else "❌"
-        print(f"  {label:25} → {score:.3f} {passed}")
-    print(f"Motifs: {motifs}")
-    print(f"Contradiction: {contradiction_flag}")
-    print("------------------\n")
-
-    return abuse_score, threshold_labels, top_patterns, result, stage, darvo_score
+    return {
+        "abuse_score": int(abuse_score),
+        "labels": detected_labels,
+        "sentiment": sentiment,
+        "stage": stage,
+        "top_patterns": top_patterns,
+    }
 
 def analyze_composite(msg1, date1, msg2, date2, msg3, date3, *answers_and_none):
     none_selected_checked = answers_and_none[-1]
@@ -324,45 +142,25 @@ def analyze_composite(msg1, date1, msg2, date2, msg3, date3, *answers_and_none):
     if not active:
         return "Please enter at least one message."
 
-    results = [(analyze_single_message(m, THRESHOLDS.copy()), d) for m, d in active]
-    abuse_scores = [r[0][0] for r in results]
-    top_labels = [r[0][2][0][0] for r in results]
-    top_scores = [r[0][2][0][1] for r in results]
-    sentiments = [r[0][3]['label'] for r in results]
-    stages = [r[0][4] for r in results]
-    darvo_scores = [r[0][5] for r in results]
-    dates_used = [r[1] or "Undated" for r in results]  # Store dates for future mapping
+    results = [(analyze_single_message(m), d) for m, d in active]
+    abuse_scores = [r[0]["abuse_score"] for r in results]
+    top_labels = [r[0]["top_patterns"][0][0] if r[0]["top_patterns"] else "None" for r in results]
+    dates_used = [r[1] or "Undated" for r in results]
 
     composite_abuse = int(round(sum(abuse_scores) / len(abuse_scores)))
-    top_label = f"{top_labels[0]} – {int(round(top_scores[0] * 100))}%"
-
-    most_common_stage = max(set(stages), key=stages.count)
-    stage_text = RISK_STAGE_LABELS[most_common_stage]
-
-    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."
+    most_common_stage = max(set(r[0]["stage"] for r in results), key=lambda x: [r[0]["stage"] for r in results].count)
 
     out = f"Abuse Intensity: {composite_abuse}%\n"
-    out += "📊 This reflects the strength and severity of detected abuse patterns in the message(s).\n\n"
-
     if escalation_score is None:
         out += "Escalation Potential: Unknown (Checklist not completed)\n"
-        out += "🔍 *This section was not completed. Escalation potential is unknown.*\n\n"
     else:
         out += f"Escalation Potential: {risk_level} ({escalation_score}/{sum(w for _, w in ESCALATION_QUESTIONS)})\n"
-        out += "🚨 This indicates how many serious risk factors are present based on your answers to the safety checklist.\n"
-
-    out += generate_risk_snippet(composite_abuse, top_label, escalation_score if escalation_score is not None else 0, most_common_stage)
-    out += f"\n\n{stage_text}"
-    out += darvo_blurb
 
-    pattern_labels = [r[0][2][0][0] for r in results]  # top label for each message
-    timeline_image = generate_abuse_score_chart(dates_used, abuse_scores, pattern_labels)
+    timeline_image = generate_abuse_score_chart(dates_used, abuse_scores, top_labels)
     return out, timeline_image
-    
+
+# --- Gradio Interface ---
+
 message_date_pairs = [
     (
         gr.Textbox(label=f"Message {i+1}"),
@@ -381,9 +179,9 @@ iface = gr.Interface(
         gr.Textbox(label="Results"),
         gr.Image(label="Risk Stage Timeline", type="pil")
     ],
-    title="Abuse Pattern Detector + Escalation Quiz",
+    title="Tether Abuse Pattern Detector v2",
     allow_flagging="manual"
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()