Update app.py

app.py

@@ -6,20 +6,20 @@ pipe_1 = pipeline("text-classification", model="mavinsao/mi-roberta-base-finetun
 pipe_2 = pipeline("text-classification", model="mavinsao/roberta-mental-finetuned")
 
 
-def ensemble_predict(text):
-    results_1 = pipe_1(text)
+def ensemble_predict(text): 
+    # Store results from each model
+    results_1 = pipe_1(text)    
     results_2 = pipe_2(text)
 
-    # Implement your chosen ensemble strategy here. 
-    # Example with simple averaging: 
-    ensemble_scores = {}
-    for result in results_1 + results_2:
-        label = result['label']
-        score = result['score']
-        ensemble_scores[label] = ensemble_scores.get(label, 0) + score / 2  
+    ensemble_scores = {} 
+    for results in [results_1, results_2]:  # Iterate through predictions
+        for result in results:
+            label = result['label']
+            score = result['score']
+            ensemble_scores[label] = ensemble_scores.get(label, 0) + score / 2  
 
-    predicted_label = max(ensemble_scores, key=ensemble_scores.get)
-    confidence = ensemble_scores[predicted_label] 
+    predicted_label = max(ensemble_scores, key=ensemble_scores.get) 
+    confidence = ensemble_scores[predicted_label]  # Ensemble confidence
 
     return predicted_label, confidence