Update app.py

app.py

@@ -28,18 +28,21 @@ def predict():
             input_text = f"{item['category']} - {item['subcategory']} in {item['area']}. {item.get('comments', '')}"
             text_lower = input_text.lower()
 
-            # Critical and low-urgency keyword lists
+            # --- Keyword heuristics ---
             critical_keywords = [
                 "umuusok", "sunog", "amoy sunog", "spark", "kuryente",
                 "leak", "baha", "gas", "short circuit", "smoke"
             ]
-
+            medium_keywords = [
+                "crack", "tagas", "sira", "damage", "malfunction", "no power",
+                "no water", "not working", "problem", "repair"
+            ]
             low_keywords = [
                 "lightbulb", "bumbilya", "ilaw", "palitan", "replace bulb",
-                "painting", "door knob", "hinge", "minor", "cosmetic"
+                "painting", "door knob", "hinge", "minor", "cosmetic", "linis", "clean"
             ]
 
-            # ✅ Priority rules
+            # --- Immediate overrides ---
             if any(word in text_lower for word in critical_keywords):
                 results.append({"priority_score": 5})
                 continue
@@ -47,24 +50,25 @@ def predict():
                 results.append({"priority_score": 2})
                 continue
 
-            # Tokenize & predict
+            # --- Model prediction ---
             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()
 
-            # ✅ Convert back to 1–5 scale
-            priority_score = predicted_class + 1
+            priority_score = predicted_class + 1  # convert 0–4 → 1–5
 
-            # Soft correction: cap too-high or too-low predictions
-            if priority_score >= 5 and not any(word in text_lower for word in critical_keywords):
-                priority_score = 4  # reduce overly high unless it's critical
+            # --- Intelligent moderation ---
+            if any(word in text_lower for word in medium_keywords):
+                priority_score = min(max(priority_score, 3), 4)
+            elif priority_score >= 5:
+                priority_score = 4
             elif priority_score <= 1:
-                priority_score = 2  # lift up if too low
+                priority_score = 2
 
             results.append({"priority_score": priority_score})
 
+
         return jsonify(results)
 
     except Exception as e: