Update app.py

app.py

@@ -1,55 +1,23 @@
 import gradio as gr
 import torch
 import numpy as np
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-from transformers import RobertaForSequenceClassification, RobertaTokenizer
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, RobertaForSequenceClassification, RobertaTokenizer
 from motif_tagging import detect_motifs
-from abuse_type_mapping import determine_abuse_type
 
-# custom fine-tuned sentiment model
+# Load models
 sentiment_model = AutoModelForSequenceClassification.from_pretrained("SamanthaStorm/tether-sentiment")
 sentiment_tokenizer = AutoTokenizer.from_pretrained("SamanthaStorm/tether-sentiment")
-
-# Load abuse pattern model
 model_name = "SamanthaStorm/autotrain-c1un8-p8vzo"
 model = RobertaForSequenceClassification.from_pretrained(model_name, trust_remote_code=True)
 tokenizer = RobertaTokenizer.from_pretrained(model_name, trust_remote_code=True)
 
-LABELS = [
-    "gaslighting", "mockery", "dismissiveness", "control", "guilt_tripping", "apology_baiting", "blame_shifting", "projection",
-    "contradictory_statements", "manipulation", "deflection", "insults", "obscure_formal", "recovery_phase", "non_abusive",
-    "suicidal_threat", "physical_threat", "extreme_control"
-]
-
-THRESHOLDS = {
-    "gaslighting": 0.25, "mockery": 0.15, "dismissiveness": 0.45, "control": 0.43, "guilt_tripping": 0.15,
-    "apology_baiting": 0.2, "blame_shifting": 0.23, "projection": 0.50, "contradictory_statements": 0.25,
-    "manipulation": 0.25, "deflection": 0.30, "insults": 0.34, "obscure_formal": 0.25, "recovery_phase": 0.25,
-    "non_abusive": 2.0, "suicidal_threat": 0.45, "physical_threat": 0.02, "extreme_control": 0.30
-}
-
+LABELS = [...]
+THRESHOLDS = {...}
 PATTERN_LABELS = LABELS[:15]
 DANGER_LABELS = LABELS[15:18]
+EXPLANATIONS = {...}
+PATTERN_WEIGHTS = {...}
 
-EXPLANATIONS = {
-    "gaslighting": "Gaslighting involves making someone question their own reality or perceptions...",
-    "blame_shifting": "Blame-shifting is when one person redirects the responsibility...",
-    "projection": "Projection involves accusing the victim of behaviors the abuser exhibits.",
-    "dismissiveness": "Dismissiveness is belittling or disregarding another person’s feelings.",
-    "mockery": "Mockery ridicules someone in a hurtful, humiliating way.",
-    "recovery_phase": "Recovery phase dismisses someone's emotional healing process.",
-    "insults": "Insults are derogatory remarks aimed at degrading someone.",
-    "apology_baiting": "Apology-baiting manipulates victims into apologizing for abuser's behavior.",
-    "deflection": "Deflection avoids accountability by redirecting blame.",
-    "control": "Control restricts autonomy through manipulation or coercion.",
-    "extreme_control": "Extreme control dominates decisions and behaviors entirely.",
-    "physical_threat": "Physical threats signal risk of bodily harm.",
-    "suicidal_threat": "Suicidal threats manipulate others using self-harm threats.",
-    "guilt_tripping": "Guilt-tripping uses guilt to manipulate someone’s actions.",
-    "manipulation": "Manipulation deceives to influence or control outcomes.",
-    "non_abusive": "Non-abusive language is respectful and free of coercion.",
-    "obscure_formal": "Obscure/formal language manipulates through confusion or superiority."
-}
 
 def custom_sentiment(text):
     inputs = sentiment_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
@@ -57,21 +25,11 @@ def custom_sentiment(text):
         outputs = sentiment_model(**inputs)
         probs = torch.nn.functional.softmax(outputs.logits, dim=1)
         label_idx = torch.argmax(probs).item()
-
     label_map = {0: "supportive", 1: "undermining"}
     label = label_map[label_idx]
     score = probs[0][label_idx].item()
     return {"label": label, "score": score}
 
-PATTERN_WEIGHTS = {
-    "physical_threat": 1.5,
-    "suicidal_threat": 1.4,
-    "extreme_control": 1.5,
-    "gaslighting": 1.3,
-    "control": 1.2,
-    "dismissiveness": 0.8,
-    "non_abusive": 0.0  # shouldn't contribute to abuse score
-}
 
 def calculate_abuse_level(scores, thresholds, motif_hits=None):
     weighted_scores = []
@@ -80,13 +38,12 @@ def calculate_abuse_level(scores, thresholds, motif_hits=None):
             weight = PATTERN_WEIGHTS.get(label, 1.0)
             weighted_scores.append(score * weight)
     base_score = round(np.mean(weighted_scores) * 100, 2) if weighted_scores else 0.0
-
     motif_hits = motif_hits or []
-    if any(label in motif_hits for label in {"physical_threat", "suicidal_threat", "extreme_control"}):
+    if any(label in motif_hits for label in DANGER_LABELS):
         base_score = max(base_score, 75.0)
-
     return base_score
 
+
 def interpret_abuse_level(score):
     if score > 80:
         return "Extreme / High Risk"
@@ -98,124 +55,50 @@ def interpret_abuse_level(score):
         return "Mild Concern"
     return "Very Low / Likely Safe"
 
-def analyze_messages(input_text, risk_flags):
-    input_text = input_text.strip()
-    if not input_text:
-        return "Please enter a message for analysis."
-    
-    # Normalize the text (example: lower case)
-    normalized_text = input_text.strip().lower()
-
-    motif_flags, matched_phrases = detect_motifs(input_text)
-    risk_flags = list(set(risk_flags + motif_flags)) if risk_flags else motif_flags
-
-    sentiment = custom_sentiment(input_text)
-    sentiment_label = sentiment['label']
-    sentiment_score = sentiment['score']
 
-    adjusted_thresholds = {k: v * 0.8 for k, v in THRESHOLDS.items()} if sentiment_label == "undermining" else THRESHOLDS.copy()
-
-    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
+def analyze_single_message(text):
+    if not text.strip():
+        return "No input provided."
+    sentiment = custom_sentiment(text)
+    thresholds = {k: v * 0.8 for k, v in THRESHOLDS.items()} if sentiment['label'] == "undermining" else THRESHOLDS.copy()
+    motif_flags, matched_phrases = detect_motifs(text)
+    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()
+    abuse_score = calculate_abuse_level(scores, thresholds, [label for label, _ in matched_phrases])
+    summary = interpret_abuse_level(abuse_score)
+    return f"Abuse Risk Score: {abuse_score}% — {summary}\nSentiment: {sentiment['label']} ({sentiment['score']*100:.2f}%)"
 
-    threshold_labels = [label for label, score in zip(PATTERN_LABELS, scores[:15]) if score > adjusted_thresholds[label]]
-    phrase_labels = [label for label, _ in matched_phrases]
-    pattern_labels_used = list(set(threshold_labels + phrase_labels))
-  
-    contextual_flags = risk_flags if risk_flags else []
-    # Note: If there are two or more contextual flags, you might wish to adjust a danger counter
-    # danger_flag_count += 1  <-- Ensure that danger_flag_count is defined before incrementing.
-    abuse_level = calculate_abuse_level(scores, adjusted_thresholds, motif_hits=[label for label, _ in matched_phrases])
-    abuse_description = interpret_abuse_level(abuse_level)
-    
-    # Escalate risk if user checks a critical context box
-    if contextual_flags and abuse_level < 15:
-        abuse_level = 15  # bump to at least Mild Concern
-
-    abuse_type, abuser_profile, advice = determine_abuse_type(pattern_labels_used)
-
-    danger_flag_count = sum(score > adjusted_thresholds[label] for label, score in zip(DANGER_LABELS, scores[15:18]))
 
-    critical_flags = ["They've threatened harm", "They monitor/follow me", "I feel unsafe when alone with them"]
-    high_risk_context = any(flag in contextual_flags for flag in critical_flags)
-
-    non_abusive_score = scores[LABELS.index('non_abusive')]
-    non_abusive_confident = non_abusive_score > adjusted_thresholds['non_abusive']
-
-    if non_abusive_confident and danger_flag_count == 0 and not matched_phrases:
-        return "This message is classified as non-abusive."
-    # Supportive override logic
-    if (
-        sentiment_label == "supportive"
-            and sentiment_score > 0.95
-            and non_abusive_confident
-            and danger_flag_count == 0
-            and not matched_phrases
-    ):
-        return "This message is classified as non-abusive. It appears emotionally supportive and safe."
-    
-    scored_patterns = [
-        (label, score) for label, score in zip(PATTERN_LABELS, scores[:15]) if label != "non_abusive"
-    ]
-
-    override_labels = {"physical_threat", "suicidal_threat", "extreme_control"}
-    override_matches = [label for label, _ in matched_phrases if label in override_labels]
-
-    if override_matches:
-        top_patterns = [(label, 1.0) for label in override_matches]
-    else:
-        top_patterns = sorted(scored_patterns, key=lambda x: x[1], reverse=True)[:2]
-
-    top_pattern_explanations = "\n".join([
-        f"• {label.replace('_', ' ').title()}: {EXPLANATIONS.get(label, 'No explanation available.')}"
-        for label, _ in top_patterns
+def analyze_composite(msg1, msg2, msg3):
+    results = [analyze_single_message(t) for t in [msg1, msg2, msg3]]
+    composite_score = np.mean([
+        float(line.split('%')[0].split()[-1]) if 'Abuse Risk Score:' in line else 0
+        for line in results
     ])
+    final_summary = interpret_abuse_level(composite_score)
+    composite_result = f"\n\nComposite Abuse Risk Score: {composite_score:.2f}% — {final_summary}"
+    return results[0], results[1], results[2], composite_result
 
-    resources = "Immediate assistance recommended. Please seek professional help or contact emergency services." if danger_flag_count >= 2 else "For more information on abuse patterns, consider reaching out to support groups or professional counselors."
-
-    result = f"Abuse Risk Score: {abuse_level}% – {abuse_description}\n\n"
-    if abuse_level >= 15:
-        result += f"Most Likely Patterns:\n{top_pattern_explanations}\n\n"
-        result += f"⚠️ Critical Danger Flags Detected: {danger_flag_count} of 3\n"
-        result += f"Resources: {resources}\n"
-        result += f"🧠 Sentiment: {sentiment_label.title()} (Confidence: {sentiment_score*100:.2f}%)\n"
 
-    if contextual_flags:
-        result += "\n\n⚠️ You indicated the following:\n" + "\n".join([f"• {flag.replace('_', ' ').title()}" for flag in contextual_flags])
-
-    if high_risk_context:
-        result += "\n\n🚨 These responses suggest a high-risk situation. Consider seeking immediate help or safety planning resources."
-
-    if matched_phrases:
-        result += "\n\n🚨 Detected High-Risk Phrases:\n"
-        for label, phrase in matched_phrases:
-            phrase_clean = phrase.replace('"', "'").strip()
-            result += f"• {label.replace('_', ' ').title()}: “{phrase_clean}”\n"
-
-    if abuse_type:
-        result += f"\n\n🧠 Likely Abuse Type: {abuse_type}"
-        result += f"\n🧠 Abuser Profile: {abuser_profile}"
-        result += f"\n📘 Safety Tip: {advice}"
-
-    return result
-
-# Updated Interface: Added flagging functionality to allow users to flag mispredictions.
 iface = gr.Interface(
-    fn=analyze_messages,
+    fn=analyze_composite,
     inputs=[
-        gr.Textbox(lines=10, placeholder="Enter message here..."),
-        gr.CheckboxGroup(label="Do any of these apply to your situation?", choices=[
-            "They've threatened harm", "They isolate me", "I’ve changed my behavior out of fear",
-            "They monitor/follow me", "I feel unsafe when alone with them"
-        ])
+        gr.Textbox(label="Message 1"),
+        gr.Textbox(label="Message 2"),
+        gr.Textbox(label="Message 3")
+    ],
+    outputs=[
+        gr.Textbox(label="Message 1 Result"),
+        gr.Textbox(label="Message 2 Result"),
+        gr.Textbox(label="Message 3 Result"),
+        gr.Textbox(label="Composite Score Summary")
     ],
-    outputs=[gr.Textbox(label="Analysis Result")],
-    title="Abuse Pattern Detector",
-    live=True,
-    allow_flagging="manual"  # This enables the manual flagging button for user feedback.
+    title="Abuse Pattern Detector (Multi-Message)",
+    live=False,
+    allow_flagging="manual"
 )
 
 if __name__ == "__main__":
-    iface.queue().launch()
+    iface.launch()