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 labels
+# Define labels (total 17 labels)
 LABELS = [
     "gaslighting", "mockery", "dismissiveness", "control",
     "guilt_tripping", "apology_baiting", "blame_shifting", "projection",
@@ -18,12 +18,12 @@ LABELS = [
     "extreme_control"
 ]
 
-# Custom thresholds per label (feel free to adjust)
+# Custom thresholds per label (make sure these are exactly as in the original)
 THRESHOLDS = {
     "gaslighting": 0.15,
     "mockery": 0.15,
-    "dismissiveness": 0.25,
-    "control": 0.03,
+    "dismissiveness": 0.25,  # Keep this as 0.25 (not 0.30)
+    "control": 0.13,
     "guilt_tripping": 0.15,
     "apology_baiting": 0.15,
     "blame_shifting": 0.15,
@@ -31,7 +31,7 @@ THRESHOLDS = {
     "contradictory_statements": 0.15,
     "manipulation": 0.15,
     "deflection": 0.15,
-    "insults": 0.25,
+    "insults": 0.20,
     "obscure_formal": 0.20,
     "recovery_phase": 0.15,
     "suicidal_threat": 0.08,
@@ -39,7 +39,7 @@ THRESHOLDS = {
     "extreme_control": 0.30,
 }
 
-# Label categories
+# Define label groups using slicing (first 14 are abuse patterns, last 3 are danger cues)
 PATTERN_LABELS = LABELS[:14]
 DANGER_LABELS = LABELS[14:]
 
@@ -72,64 +72,34 @@ def analyze_messages(input_text):
         outputs = model(**inputs)
     scores = torch.sigmoid(outputs.logits.squeeze(0)).numpy()
 
-    # Count triggered labels
+    # Count triggered labels using the correct slices
     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:]))
 
-    # Abuse level calculation
+    # Abuse level calculation and severity interpretation
     abuse_level = calculate_abuse_level(scores, THRESHOLDS)
     abuse_description = interpret_abuse_level(abuse_level)
 
-    # Resource logic
+    # Resource logic (example logic; adjust as needed)
     if danger_flag_count >= 2:
-        resources = (
-            "**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_flag_count == 1:
-        resources = (
-            "**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"
-        )
+        resources = "Immediate assistance recommended. Please seek professional help or contact emergency services."
     else:
-        resources = (
-            "**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)"
-        )
-
-    result_md = (
-        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}"
+        resources = "For more information on abuse patterns, consider reaching out to support groups or professional counselors."
+
+    # Output combining counts, severity, and resource suggestion
+    result = (
+        f"Abuse Patterns Detected: {pattern_count} out of {len(PATTERN_LABELS)}\n"
+        f"Danger Flags Detected: {danger_flag_count} out of {len(DANGER_LABELS)}\n"
+        f"Abuse Level: {abuse_level}% - {abuse_description}\n"
+        f"Resources: {resources}"
     )
+    return result, scores
 
-    # 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
-
-# Interface
-with gr.Blocks() as demo:
-    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, file_output])
+iface = gr.Interface(
+    fn=analyze_messages,
+    inputs=gr.inputs.Textbox(lines=10, placeholder="Enter message here..."),
+    outputs=["text", "json"],
+    title="Abuse Pattern Detector"
+)
 
-if __name__ == "__main__":
-    demo.launch()
+iface.launch()