dev check working

app.py

@@ -202,4 +202,66 @@ class UnifiedDeepfakeDetector:
 
         except Exception as e:
             print(f"Analysis error: {e}")
-            return None, None, None
+            return None, None, None
+
+#--------------------------------------------------------------------------------------------------------------------
+
+import torchaudio
+import torch
+import numpy as np
+from scipy.stats import skew, kurtosis, median_abs_deviation
+import os
+import torch.nn.functional as F
+
+
+import os
+os.environ["TORCH_HOME"] = "/tmp/torch_cache"
+
+
+
+from torchaudio.pipelines import WAV2VEC2_BASE
+bundle = WAV2VEC2_BASE
+
+model = bundle.get_model()
+print("Model downloaded successfully!")
+
+
+def extract_features(file_path):
+    if os.path.exists(file_path):
+        print(f"File successfully written: {file_path}")
+    else:
+        print("File writing failed.")
+    waveform, sample_rate = torchaudio.load(file_path)
+    if sample_rate != bundle.sample_rate:
+        waveform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=bundle.sample_rate)(waveform)
+
+    with torch.inference_mode():
+        features, _ = model.extract_features(waveform)
+
+    pooled_features = []
+    for f in features:
+        if f.dim() == 3:
+            f = f.permute(0, 2, 1)
+            pooled_f = F.adaptive_avg_pool1d(f[0].unsqueeze(0), 1).squeeze(0)
+            pooled_features.append(pooled_f)
+
+    final_features = torch.cat(pooled_features, dim=0).numpy()
+    final_features = (final_features - np.mean(final_features)) / (np.std(final_features) + 1e-10)
+
+    return final_features
+
+def additional_features(features):
+    mad = median_abs_deviation(features)
+    features_clipped = np.clip(features, 1e-10, None)
+    entropy = -np.sum(features_clipped * np.log(features_clipped))
+    return mad, entropy
+
+def classify_audio(features):
+
+    _, entropy = additional_features(features)
+    print(entropy)
+
+    if  entropy > 150:
+        return True, entropy
+    else:
+        return False, entropy