Update model_utilis.py

model_utilis.py

@@ -2,58 +2,54 @@ import numpy as np
 import tensorflow as tf
 
 def load_model(model_path):
-print(f"Loading model from {model_path} ...")
-model = tf.keras.models.load_model(model_path)
-print("Model loaded. Input shape =", model.input_shape)
-return model
+    print(f"Loading model from {model_path} ...")
+    model = tf.keras.models.load_model(model_path)
+    print("Model loaded. Input shape =", model.input_shape)
+    return model
 
 def prepare_input_for_model(features, model):
-features = np.asarray(features, dtype=np.float32)
-input_shape = model.input_shape[1:]
-
-```
-if len(input_shape) == 2:
-    target_T, target_D = input_shape
-    if target_D is not None and target_D != features.shape[1]:
-        if target_D == features.shape[0]:
-            features = features.T
+    features = np.asarray(features, dtype=np.float32)
+    input_shape = model.input_shape[1:]
+
+    if len(input_shape) == 2:
+        target_T, target_D = input_shape
+        if target_D is not None and target_D != features.shape[1]:
+            if target_D == features.shape[0]:
+                features = features.T
+            else:
+                raise ValueError(f"Model expects feature dim {target_D}, got {features.shape[1]}")
+
+        if target_T is not None:
+            cur_T = features.shape[0]
+            if cur_T < target_T:
+                pad = target_T - cur_T
+                features = np.pad(features, ((0, pad), (0, 0)), mode="constant")
+            elif cur_T > target_T:
+                features = features[:target_T, :]
+
+    elif len(input_shape) == 1:
+        flat = features.flatten()
+        target_len = input_shape[0]
+        if flat.shape[0] < target_len:
+            flat = np.pad(flat, (0, target_len - flat.shape[0]), mode="constant")
         else:
-            raise ValueError(f"Model expects feature dim {target_D}, got {features.shape[1]}")
-
-    if target_T is not None:
-        cur_T = features.shape[0]
-        if cur_T < target_T:
-            pad = target_T - cur_T
-            features = np.pad(features, ((0, pad), (0, 0)), mode="constant")
-        elif cur_T > target_T:
-            features = features[:target_T, :]
-
-elif len(input_shape) == 1:
-    flat = features.flatten()
-    target_len = input_shape[0]
-    if flat.shape[0] < target_len:
-        flat = np.pad(flat, (0, target_len - flat.shape[0]), mode="constant")
-    else:
-        flat = flat[:target_len]
-    features = flat
+            flat = flat[:target_len]
+        features = flat
 
-else:
-    raise ValueError(f"Unsupported model input shape: {input_shape}")
+    else:
+        raise ValueError(f"Unsupported model input shape: {input_shape}")
 
-return np.expand_dims(features, axis=0)
-```
+    return np.expand_dims(features, axis=0)
 
 def interpret_prediction(raw_pred):
-raw = np.asarray(raw_pred).squeeze()
-if raw.size == 1:
-val = float(raw)
-prob_fake = val if 0.0 <= val <= 1.0 else 1.0 / (1.0 + np.exp(-val))
-else:
-probs = tf.nn.softmax(raw).numpy()
-prob_fake = float(probs[1]) if probs.size >= 2 else float(probs.max())
-
-```
-prob_fake = float(np.clip(prob_fake, 0.0, 1.0))
-prob_real = 1.0 - prob_fake
-return {"Fake": prob_fake, "Real": prob_real}
-```
+    raw = np.asarray(raw_pred).squeeze()
+    if raw.size == 1:
+        val = float(raw)
+        prob_fake = val if 0.0 <= val <= 1.0 else 1.0 / (1.0 + np.exp(-val))
+    else:
+        probs = tf.nn.softmax(raw).numpy()
+        prob_fake = float(probs[1]) if probs.size >= 2 else float(probs.max())
+
+    prob_fake = float(np.clip(prob_fake, 0.0, 1.0))
+    prob_real = 1.0 - prob_fake
+    return {"Fake": prob_fake, "Real": prob_real}