Update JSON structure: Remove winner field, rename fields, add new prediction metrics

app.py

@@ -934,12 +934,13 @@ def predict_single(control_image, variant_image, business_model, customer_type,
         
         # Create enhanced output with confidence scores and training data info
         result = {
-            "winner": winner,
             "probability": f"{probability:.3f}",
-            "model_confidence": f"{confidence_percentage:.1f}%",
-            "training_data_samples": confidence_data['training_data_count'],
-            "historical_accuracy": f"{confidence_data['correct_predictions']}/{confidence_data['count']} correct",
-            "win_loss_ratio": f"{confidence_data['actual_wins']} wins in {confidence_data['count']} tests"
+            "mobileConfidence": f"{confidence_percentage:.1f}%",
+            "trainingDataSamples": confidence_data['training_data_count'],
+            "totalPredictions": confidence_data['count'],
+            "correctPredictions": confidence_data['correct_predictions'],
+            "totalWinsPrediction": confidence_data['actual_wins'],
+            "totalLosePrediction": confidence_data['count'] - confidence_data['actual_wins']
         }
         
         print(f"🎯 Final result: {result}")
@@ -954,10 +955,12 @@ def predict_single(control_image, variant_image, business_model, customer_type,
         # Return error result with fallback confidence data
         return {
             "error": f"Prediction failed: {str(e)}",
-            "model_confidence": "50.0%",
-            "training_data_samples": 0,
-            "historical_accuracy": "0/0 correct",
-            "win_loss_ratio": "0 wins in 0 tests"
+            "mobileConfidence": "50.0%",
+            "trainingDataSamples": 0,
+            "totalPredictions": 0,
+            "correctPredictions": 0,
+            "totalWinsPrediction": 0,
+            "totalLosePrediction": 0
         }
 
 @spaces.GPU