29

app.py

@@ -171,19 +171,37 @@ def load_model():
 def predict_allergen(model, vectorizer, input_text):
     X_input = vectorizer.transform([input_text])
     prediction = model.predict(X_input)
+    
     try:
+        # Untuk multi-label classification, predict_proba mengembalikan list probabilitas
         probabilities = model.predict_proba(X_input)
-        return prediction[0], probabilities[0]
-    except:
-        # Fallback jika predict_proba tidak tersedia atau error
-        # Buat dummy probabilities berdasarkan prediction
-        dummy_probs = []
-        for pred in prediction[0]:
-            if pred == 1:
-                dummy_probs.append([0.1, 0.9])  # [prob_negative, prob_positive]
-            else:
-                dummy_probs.append([0.9, 0.1])
-        return prediction[0], dummy_probs
+        
+        # Jika probabilities adalah list of arrays (multi-label)
+        if isinstance(probabilities, list):
+            # Ambil probabilitas untuk kelas positif dari setiap classifier
+            positive_probs = []
+            for i, prob_array in enumerate(probabilities):
+                if prob_array.shape[1] == 2:  # Binary classification
+                    positive_probs.append(prob_array[0][1])  # Probabilitas kelas positif
+                else:
+                    positive_probs.append(prob_array[0][0])  # Jika hanya 1 kelas
+            return prediction[0], positive_probs
+        else:
+            # Single output
+            return prediction[0], probabilities[0]
+            
+    except Exception as e:
+        # Jika predict_proba gagal, gunakan decision_function jika tersedia
+        try:
+            decision_scores = model.decision_function(X_input)
+            # Convert decision scores to probabilities using sigmoid
+            import numpy as np
+            probabilities = 1 / (1 + np.exp(-decision_scores[0]))
+            return prediction[0], probabilities
+        except:
+            # Last fallback - return predictions as confidence (0 or 1 -> 0% or 100%)
+            confidence_scores = [float(pred) for pred in prediction[0]]
+            return prediction[0], confidence_scores
 
 # === Scraping bahan dari Cookpad ===
 def get_ingredients_from_cookpad(url):
@@ -229,17 +247,18 @@ def display_results(results, probabilities, labels):
     for i, (allergen, status) in enumerate(results.items()):
         emoji = allergen_emojis.get(allergen, '📋')
         
-        # Handle different probability formats
+        # Get actual probability from model
         try:
-            if isinstance(probabilities[i], (list, tuple)) and len(probabilities[i]) >= 2:
-                confidence = probabilities[i][1] * 100  # Get probability for positive class
-            elif isinstance(probabilities[i], (int, float)):
+            if isinstance(probabilities, list) and i < len(probabilities):
+                confidence = probabilities[i] * 100
+            elif hasattr(probabilities, '__getitem__') and i < len(probabilities):
                 confidence = probabilities[i] * 100
             else:
-                confidence = 50.0  # Default fallback
+                # If no probability available, show based on prediction
+                confidence = 100.0 if status == 1 else 0.0
         except (IndexError, TypeError):
-            # Fallback confidence based on prediction
-            confidence = 85.0 if status == 1 else 15.0
+            # Fallback to prediction-based confidence
+            confidence = 100.0 if status == 1 else 0.0
         
         if status == 1:  # Detected
             detected_allergens.append(allergen)
@@ -249,9 +268,11 @@ def display_results(results, probabilities, labels):
             </div>
             ''', unsafe_allow_html=True)
         else:  # Not detected
+            # For negative cases, show (100 - confidence) to represent "not detected" confidence
+            negative_confidence = 100 - confidence if confidence > 50 else confidence
             st.markdown(f'''
             <div class="allergen-result allergen-safe">
-                {emoji} {allergen}: Tidak Terdeteksi ✓ ({confidence:.2f}%)
+                {emoji} {allergen}: Tidak Terdeteksi ✓ ({negative_confidence:.2f}%)
             </div>
             ''', unsafe_allow_html=True)