Update app.py

app.py

@@ -2,14 +2,13 @@ import gradio as gr
 import torch
 from transformers import RobertaForSequenceClassification, RobertaTokenizer
 import numpy as np
-import tempfile
 
-# Load model and tokenizer
+# Load model and tokenizer with trust_remote_code in case it's needed
 model_name = "SamanthaStorm/abuse-pattern-detector-v2"
-model = RobertaForSequenceClassification.from_pretrained(model_name)
-tokenizer = RobertaTokenizer.from_pretrained(model_name)
+model = RobertaForSequenceClassification.from_pretrained(model_name, trust_remote_code=True)
+tokenizer = RobertaTokenizer.from_pretrained(model_name, trust_remote_code=True)
 
-# Define labels (total 17 labels)
+# Define labels (17 total)
 LABELS = [
     "gaslighting", "mockery", "dismissiveness", "control",
     "guilt_tripping", "apology_baiting", "blame_shifting", "projection",
@@ -18,11 +17,11 @@ LABELS = [
     "extreme_control"
 ]
 
-# Custom thresholds per label (make sure these are exactly as in the original)
+# Custom thresholds for each label (make sure these match your original settings)
 THRESHOLDS = {
     "gaslighting": 0.15,
     "mockery": 0.15,
-    "dismissiveness": 0.25,  # Keep this as 0.25 (not 0.30)
+    "dismissiveness": 0.25,  # original value, not 0.30
     "control": 0.13,
     "guilt_tripping": 0.15,
     "apology_baiting": 0.15,
@@ -39,7 +38,7 @@ THRESHOLDS = {
     "extreme_control": 0.30,
 }
 
-# Define label groups using slicing (first 14 are abuse patterns, last 3 are danger cues)
+# Define label groups using slicing (first 14: abuse patterns, last 3: danger cues)
 PATTERN_LABELS = LABELS[:14]
 DANGER_LABELS = LABELS[14:]
 
@@ -66,40 +65,47 @@ def analyze_messages(input_text):
     if not input_text:
         return "Please enter a message for analysis.", None
 
-    # Tokenize and predict
+    # Tokenize input and generate model predictions
     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()
 
-    # Count triggered labels using the correct slices
+    # Count the number of triggered abuse pattern and danger flags based on thresholds
     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 and severity interpretation
+    # Calculate overall abuse level and interpret it
     abuse_level = calculate_abuse_level(scores, THRESHOLDS)
     abuse_description = interpret_abuse_level(abuse_level)
 
-    # Resource logic (example logic; adjust as needed)
+    # Resource logic based on the number of danger cues
     if danger_flag_count >= 2:
         resources = "Immediate assistance recommended. Please seek professional help or contact emergency services."
     else:
         resources = "For more information on abuse patterns, consider reaching out to support groups or professional counselors."
 
-    # Output combining counts, severity, and resource suggestion
+    # Prepare the result summary and detailed scores
     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
+    
+    # Return both a text summary and a JSON-like dict of scores per label
+    return result, {"scores": dict(zip(LABELS, scores))}
 
+# Updated Gradio Interface using new component syntax
 iface = gr.Interface(
     fn=analyze_messages,
-    inputs=gr.inputs.Textbox(lines=10, placeholder="Enter message here..."),
-    outputs=[gr.Textbox(), gr.JSON()],
+    inputs=gr.Textbox(lines=10, placeholder="Enter message here..."),
+    outputs=[
+        gr.Textbox(label="Analysis Result"),
+        gr.JSON(label="Scores")
+    ],
     title="Abuse Pattern Detector"
 )
 
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()