Update app.py

app.py

@@ -60,32 +60,37 @@ def process_audio(input_data, segment_duration=10):
         audio = audio[0]
     segments = segment_audio(audio, sr, segment_duration)
     segment_predictions = []
-    output_lines = []
-    eer_threshold = thresh - 5e-3 # small margin error due to feature extractor space differences
+    confidence_scores = []
+    eer_threshold = thresh - 5e-3  # small margin of error
+
     for idx, segment in enumerate(segments):
         features = FEATURE_EXTRACTOR(segment, sr)
-        features_avg = torch.mean(features, dim=1).cpu().numpy()
-        features_avg = features_avg.reshape(1, -1)
+        features_avg = torch.mean(features, dim=1).cpu().numpy().reshape(1, -1)
         decision_score = classifier.decision_function(features_avg)
         decision_score_scaled = scaler.transform(decision_score.reshape(-1, 1)).flatten()
         decision_value = decision_score_scaled[0]
         pred = 1 if decision_value >= eer_threshold else 0
+
         if pred == 1:
             confidence_percentage = expit(decision_score).item()
         else:
             confidence_percentage = 1 - expit(decision_score).item()
+
         segment_predictions.append(pred)
-        output_dict = {
-    "label": "real" if sum(segment_predictions) > (len(segment_predictions) / 2) else "fake",
-    "segments": [
-        {
-            "segment": idx + 1,
-            "prediction": "real" if pred == 1 else "fake",
-            "confidence": round(conf * 100, 2)
-        }
-        for idx, (pred, conf) in enumerate(zip(segment_predictions, confidence_scores))
-    ]
-}
+        confidence_scores.append(confidence_percentage)
+
+    output_dict = {
+        "label": "real" if sum(segment_predictions) > (len(segment_predictions) / 2) else "fake",
+        "segments": [
+            {
+                "segment": idx + 1,
+                "prediction": "real" if pred == 1 else "fake",
+                "confidence": round(conf * 100, 2)
+            }
+            for idx, (pred, conf) in enumerate(zip(segment_predictions, confidence_scores))
+        ]
+    }
+
     json_output = json.dumps(output_dict, indent=4)
     print(json_output)
     return json_output