Attempt to fix JIT error by forcing CNN on CPU

app.py

@@ -7,6 +7,7 @@ import os
 from werkzeug.utils import secure_filename
 import sys
 import traceback
+import tensorflow as tf
 from tensorflow.keras.models import load_model
 from tensorflow.keras.preprocessing import image
 import time
@@ -92,13 +93,21 @@ cnn_model = load_model(cnn_model_path)
 cnn_class_labels = ['side_chest', 'front_double_biceps', 'back_double_biceps', 'front_lat_spread', 'back_lat_spread']
 
 def predict_pose_cnn(img_path):
-    img = image.load_img(img_path, target_size=(150, 150))
-    img_array = image.img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0) / 255.0
-    predictions = cnn_model.predict(img_array)
-    predicted_class = np.argmax(predictions, axis=1)
-    confidence = float(np.max(predictions))
-    return cnn_class_labels[predicted_class[0]], confidence
+    try:
+        print("[CNN_DEBUG] Forcing CPU for CNN prediction")
+        with tf.device('/CPU:0'): # This line requires 'import tensorflow as tf'
+            img = image.load_img(img_path, target_size=(150, 150))
+            img_array = image.img_to_array(img)
+            img_array = np.expand_dims(img_array, axis=0) / 255.0
+            predictions = cnn_model.predict(img_array)
+            predicted_class = np.argmax(predictions, axis=1)
+            confidence = float(np.max(predictions))
+        print(f"[CNN_DEBUG] Prediction successful on CPU: {cnn_class_labels[predicted_class[0]]}")
+        return cnn_class_labels[predicted_class[0]], confidence
+    except Exception as e:
+        print(f"[CNN_ERROR] Exception during CNN prediction: {e}")
+        traceback.print_exc()
+        raise # Re-raise the exception to be caught by the calling function
 
 @app.route('/static/uploads/<path:filename>')
 def serve_video(filename):