Update app.py

app.py

@@ -52,32 +52,32 @@ EXPLANATIONS = {
 }
 
 def custom_sentiment(text):
-inputs = sentiment_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
-with torch.no_grad():
-outputs = sentiment_model(**inputs)
-probs = torch.nn.functional.softmax(outputs.logits, dim=1)
-label_idx = torch.argmax(probs).item()
+    inputs = sentiment_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+    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}
+    label_map = {0: "supportive", 1: "undermining"}
+    label = label_map[label_idx]
+    score = probs[0][label_idx].item()
+    return {"label": label, "score": score}
 
 def calculate_abuse_level(scores, thresholds):
-triggered_scores = [score for label, score in zip(LABELS, scores) if score > thresholds[label]]
-return round(np.mean(triggered_scores) * 100, 2) if triggered_scores else 0.0
+    triggered_scores = [score for label, score in zip(LABELS, scores) if score > thresholds[label]]
+    return round(np.mean(triggered_scores) * 100, 2) if triggered_scores else 0.0
 
 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"
-return "Very Low / Likely Safe"
+    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"
+    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."
+    input_text = input_text.strip()
+    if not input_text:
+    return "Please enter a message for analysis."
 
 sentiment = custom_sentiment(input_text)
 sentiment_label = sentiment['label']
@@ -151,4 +151,4 @@ title="Abuse Pattern Detector"
 )
 
 if name == "main":
-iface.queue().launch()
+    iface.queue().launch()