Update app.py

app.py

@@ -24,15 +24,31 @@ def predict():
         results = []
 
         for item in data:
-            # Build input text (you can adjust formatting if needed)
+            # Build input text
             input_text = f"{item['category']} - {item['subcategory']} in {item['area']}. {item.get('comments', '')}"
+
+            # Smart keyword override for critical incidents
+            text_lower = input_text.lower()
+            critical_keywords = [
+                "umuusok", "sunog", "amoy sunog", "spark", "kuryente",
+                "leak", "baha", "gas", "short circuit", "smoke"
+            ]
+            if any(word in text_lower for word in critical_keywords):
+                results.append({"priority_score": 5})
+                continue
+
+            # Tokenize & predict
             inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
 
             with torch.no_grad():
                 outputs = model(**inputs)
                 predicted_class = torch.argmax(outputs.logits, dim=1).item()
 
-            results.append({"priority_score": predicted_class})
+            # ✅ Convert back to 1–5 scale
+            priority_score = predicted_class + 1
+
+            # ✅ Here's where this line goes
+            results.append({"priority_score": priority_score})
 
         return jsonify(results)