Update inference.py

inference.py

@@ -1,13 +1,7 @@
-# inference.py
-
+import os
 import tensorflow as tf
-import logging
 from preprocessing import VideoPreprocessor
 
-# Configure Logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
 class LipReadingModel:
     def __init__(self, model_path='best_model_1_WER.keras'):
         # Initialize character mappings before loading the model
@@ -22,10 +16,9 @@ class LipReadingModel:
                 model_path,
                 custom_objects={'CTCLoss': self.CTCLoss}  # Include custom loss if needed
             )
-            logger.info("Model loaded successfully.")
+            print("Model loaded successfully.")
         except Exception as e:
-            logger.error(f"Error loading model: {e}")
-            self.model = self.load_model()  # Fallback to building the model if loading fails
+            print(f"Error loading model: {e}")
 
     @staticmethod
     def CTCLoss(y_true, y_pred):
@@ -76,28 +69,24 @@ class LipReadingModel:
         # Final dense layer
         model.add(tf.keras.layers.Dense(self.char_to_num.vocabulary_size() + 1, kernel_initializer='he_normal', activation='softmax'))
 
-        logger.info("Built the model architecture successfully.")
         return model
 
     def predict(self, normalized_frames):
         if normalized_frames is None or int(tf.size(normalized_frames)) == 0:
             return "No frames extracted from the video. Please ensure the video contains a clear view of the face and lips."
-
-        # Add batch dimension
-        frames = tf.expand_dims(normalized_frames, axis=0)  # Shape: (1, num_frames, 85, 85, 1)
         
-        # Perform prediction
+        frames = tf.expand_dims(normalized_frames, axis=0)  # Add batch dimension
         yhat = self.model.predict(frames, verbose=0)
         
         input_length = [yhat.shape[1]]  # batch size of 1
         # Perform CTC decoding
         decoded_tf = tf.keras.backend.ctc_decode(yhat, input_length=input_length, greedy=True)[0][0]
         decoded = decoded_tf.numpy().flatten()  # Convert to numpy array and flatten
-
-        logger.debug(f"decoded type: {type(decoded)}")
-        logger.debug(f"decoded shape: {decoded.shape}")
-        logger.debug(f"decoded contents: {decoded}")
-
+        
+        print("decoded type:", type(decoded))
+        print("decoded shape:", decoded.shape)
+        print("decoded contents:", decoded)
+        
         # Convert numerical predictions to characters
         prediction = ''.join(
             [
@@ -106,34 +95,35 @@ class LipReadingModel:
                 if int(num) != -1
             ]
         )
-
+        
         return prediction
 
-    def predict_from_video(video_path=None, frames=None, model=None, preprocessor=None):
-        """
-        Predicts the text from a video file or webcam frames using the provided model.
-        Args:
-            video_path (str, optional): Path to the video file. Defaults to None.
-            frames (List[np.ndarray], optional): List of frames from webcam. Defaults to None.
-            model (LipReadingModel, optional): An instance of the LipReadingModel. Defaults to None.
-            preprocessor (VideoPreprocessor, optional): An instance of the VideoPreprocessor. Defaults to None.
-        Returns:
-            str: Predicted text.
-        """
-        if model is None:
-            model = LipReadingModel()
-    
-        if preprocessor is None:
-            preprocessor = VideoPreprocessor()
-    
-        if video_path:
-            # Preprocess video from file
-            normalized_frames = preprocessor.preprocess_video(video_path)
-        elif frames is not None:
-            # Preprocess frames from webcam
-            normalized_frames = preprocessor.preprocess_frames(frames)
-        else:
-            return "No video or frames provided for prediction."
-    
-        prediction = model.predict(normalized_frames)
-        return prediction
+
+
+
+def predict_from_video(video_path=None, frames=None, model=None):
+    """
+    Predicts the text from a video file or webcam frames using the provided model.
+    Args:
+        video_path (str, optional): Path to the video file. Defaults to None.
+        frames (List[np.ndarray], optional): List of frames from webcam. Defaults to None.
+        model (LipReadingModel, optional): An instance of the LipReadingModel. Defaults to None.
+    Returns:
+        str: Predicted text.
+    """
+    if model is None:
+        model = LipReadingModel()
+
+    preprocessor = VideoPreprocessor()
+
+    if video_path:
+        # Preprocess video from file
+        normalized_frames = preprocessor.preprocess_video(video_path)
+    elif frames is not None:
+        # Preprocess frames from webcam
+        normalized_frames = preprocessor.preprocess_frames(frames)
+    else:
+        return "No video or frames provided for prediction."
+
+    prediction = model.predict(normalized_frames)
+    return prediction