Update app.py

app.py

@@ -9,7 +9,7 @@ model_name = "SamanthaStorm/abuse-pattern-detector-v2"
 model = RobertaForSequenceClassification.from_pretrained(model_name)
 tokenizer = RobertaTokenizer.from_pretrained(model_name)
 
-# Define the final label order your model used
+# Define labels
 LABELS = [
     "gaslighting", "mockery", "dismissiveness", "control",
     "guilt_tripping", "apology_baiting", "blame_shifting", "projection",
@@ -17,120 +17,119 @@ LABELS = [
     "obscure_formal", "recovery_phase", "suicidal_threat", "physical_threat",
     "extreme_control"
 ]
-TOTAL_LABELS = 17
 
-# Our model outputs 17 labels:
-# - First 14 are abuse pattern categories
-# - Last 3 are Danger Assessment cues
-TOTAL_LABELS = 17
-# Individual thresholds for each of the 17 labels
+# Custom thresholds per label (feel free to adjust)
 THRESHOLDS = {
     "gaslighting": 0.15,
     "mockery": 0.15,
-    "dismissiveness": 0.30,
-    "control": 0.13,
+    "dismissiveness": 0.15,
+    "control": 0.10,
     "guilt_tripping": 0.15,
     "apology_baiting": 0.15,
     "blame_shifting": 0.15,
-    "projection": 0.20,
+    "projection": 0.15,
     "contradictory_statements": 0.15,
     "manipulation": 0.15,
     "deflection": 0.15,
-    "insults": 0.20,
-    "obscure_formal": 0.20,
+    "insults": 0.15,
+    "obscure_formal": 0.15,
     "recovery_phase": 0.15,
-    "suicidal_threat": 0.09,
-    "physical_threat": 0.50,
-    "extreme_control": 0.30
+    "suicidal_threat": 0.20,
+    "physical_threat": 0.20,
+    "extreme_control": 0.20,
 }
+
+# Label categories
+PATTERN_LABELS = LABELS[:14]
+DANGER_LABELS = LABELS[14:]
+
+def calculate_abuse_level(scores, thresholds):
+    triggered_scores = [score for label, score in zip(LABELS, scores) if score > thresholds[label]]
+    if not triggered_scores:
+        return 0.0
+    return round(np.mean(triggered_scores) * 100, 2)
+
+def interpret_abuse_level(score):
+    if score > 80:
+        return "Extreme / High Risk"
+    elif score > 60:
+        return "Severe / Harmful Pattern Present"
+    elif score > 40:
+        return "Likely Abuse"
+    elif score > 20:
+        return "Mild Concern"
+    else:
+        return "Very Low / Likely Safe"
+
 def analyze_messages(input_text):
     input_text = input_text.strip()
     if not input_text:
-        return "Please enter a message for analysis."
+        return "Please enter a message for analysis.", None
 
-    # Tokenize
+    # Tokenize and predict
     inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
     with torch.no_grad():
         outputs = model(**inputs)
+    scores = torch.sigmoid(outputs.logits.squeeze(0)).numpy()
 
-    # Squeeze out batch dimension
-    logits = outputs.logits.squeeze(0)
-
-    # Convert to probabilities
-    scores = torch.sigmoid(logits).numpy()
-    print("Scores:", scores)
-    print("Danger Scores:", scores[14:])  # suicidal, physical, extreme
+    # Count triggered labels
+    pattern_count = sum(score > THRESHOLDS[label] for label, score in zip(PATTERN_LABELS, scores[:14]))
+    danger_flag_count = sum(score > THRESHOLDS[label] for label, score in zip(DANGER_LABELS, scores[14:]))
 
-    pattern_count = 0
-    danger_flag_count = 0
+    # Abuse level calculation
+    abuse_level = calculate_abuse_level(scores, THRESHOLDS)
+    abuse_description = interpret_abuse_level(abuse_level)
 
-    for i, (label, score) in enumerate(zip(LABELS, scores)):
-        if score > THRESHOLDS[label]:
-            if i < 14:
-                pattern_count += 1
-            else:
-                danger_flag_count += 1
-
-    # Optional debug print
-    for i, s in enumerate(scores):
-        print(LABELS[i], "=", round(s, 3))
-
-    danger_assessment = (
-        "High" if danger_flag_count >= 2 else
-        "Moderate" if danger_flag_count == 1 else
-        "Low"
-    )
-    # Treat high-scoring danger cues as abuse patterns as well
-    for danger_label in ["suicidal_threat", "physical_threat", "extreme_control"]:
-        if scores[LABELS.index(danger_label)] > THRESHOLDS[danger_label]:
-            pattern_count += 1
-    # Set resources
-    if danger_assessment == "High":
+    # Resource logic
+    if danger_flag_count >= 2:
         resources = (
-            "**Immediate Help:** If you are in immediate danger, please call 911.\n\n"
-            "**Crisis Support:** National DV Hotline – Safety Planning: [thehotline.org/plan-for-safety](https://www.thehotline.org/plan-for-safety)\n"
-            "**Legal Assistance:** WomensLaw – Legal Help for Survivors: [womenslaw.org](https://www.womenslaw.org)\n"
-            "**Specialized Support:** For LGBTQ+, immigrants, and neurodivergent survivors, please consult local services."
+            "**Immediate Help:** Call 911 if in danger.\n\n"
+            "**Crisis Support:** National DV Hotline – [thehotline.org/plan-for-safety](https://www.thehotline.org/plan-for-safety/)\n"
+            "**Legal Support:** WomensLaw – [womenslaw.org](https://www.womenslaw.org/)\n"
+            "**Specialized Services:** RAINN, StrongHearts, LGBTQ+, immigrant, neurodivergent resources"
         )
-    elif danger_assessment == "Moderate":
+    elif danger_flag_count == 1:
         resources = (
-            "**Safety Planning:** The Hotline – What Is Emotional Abuse?: [thehotline.org/resources](https://www.thehotline.org/resources)\n"
-            "**Relationship Health:** One Love Foundation – Digital Relationship Health: [joinonelove.org](https://www.joinonelove.org)\n"
-            "**Support Chat:** National Domestic Violence Hotline Chat: [thehotline.org](https://www.thehotline.org)\n"
-            "**Specialized Groups:** Look for support groups tailored for LGBTQ+, immigrant, and neurodivergent communities."
+            "**Emotional Abuse Info:** [thehotline.org/resources](https://www.thehotline.org/resources/what-is-emotional-abuse/)\n"
+            "**Relationship Education:** [joinonelove.org](https://www.joinonelove.org/)\n"
+            "**Support Chat:** [thehotline.org](https://www.thehotline.org/)\n"
+            "**Community Groups:** LGBTQ+, immigrant, and neurodivergent spaces"
         )
     else:
         resources = (
-            "**Educational Resources:** Love Is Respect – Healthy Relationships: [loveisrespect.org](https://www.loveisrespect.org)\n"
-            "**Therapy Finder:** Psychology Today – Find a Therapist: [psychologytoday.com](https://www.psychologytoday.com)\n"
-            "**Relationship Tools:** Relate – Relationship Health Tools: [relate.org.uk](https://www.relate.org.uk)\n"
-            "**Community Support:** Consider community-based and online support groups, especially those focused on LGBTQ+, immigrant, and neurodivergent survivors."
+            "**Healthy Relationships:** [loveisrespect.org](https://www.loveisrespect.org/)\n"
+            "**Find a Therapist:** [psychologytoday.com](https://www.psychologytoday.com/us/therapists)\n"
+            "**Relationship Tools:** [relate.org.uk](https://www.relate.org.uk/)\n"
+            "**Online Peer Support:** (including identity-focused groups)"
         )
 
-    # Output
     result_md = (
-        f"**Abuse Pattern Count:** {pattern_count}\n\n"
-        f"**Support Resources:**\n{resources}"
+        f"### 📋 Analysis Summary\n\n"
+        f"**Abuse Pattern Count:** {pattern_count}\n"
+        f"**Danger Cues Detected:** {danger_flag_count}\n"
+        f"**Abuse Level:** {abuse_level}% ({abuse_description})\n\n"
+        f"### 🛟 Suggested Support Resources\n{resources}"
     )
 
+    # Save to .txt
     with tempfile.NamedTemporaryFile(delete=False, suffix=".txt", mode="w") as f:
         f.write(result_md)
         report_path = f.name
 
     return result_md, report_path
 
-# Build the Gradio interface
+# Interface
 with gr.Blocks() as demo:
-    gr.Markdown("# Abuse Pattern Detector - Risk Analysis")
-    gr.Markdown("Enter one or more messages (separated by newlines) for analysis.")
-    
-    text_input = gr.Textbox(label="Input Messages", lines=10, placeholder="Type your message(s) here...")
-    result_output = gr.Markdown(label="Analysis Result")
-    download_output = gr.File(label="Download Report (.txt)")
-    
-    text_input.submit(analyze_messages, inputs=text_input, outputs=[result_output, download_output])
+    gr.Markdown("# 🔍 Abuse Pattern Detector")
+    gr.Markdown("Paste one or more messages for analysis (multi-line supported).")
+
+    text_input = gr.Textbox(label="Text Message(s)", lines=10, placeholder="Paste messages here...")
+    result_output = gr.Markdown()
+    file_output = gr.File(label="📥 Download Analysis (.txt)")
+
+    text_input.submit(analyze_messages, inputs=text_input, outputs=[result_output, file_output])
     analyze_btn = gr.Button("Analyze")
-    analyze_btn.click(analyze_messages, inputs=text_input, outputs=[result_output, download_output])
-    
+    analyze_btn.click(analyze_messages, inputs=text_input, outputs=[result_output, file_output])
+
 if __name__ == "__main__":
     demo.launch()