Update food_identification.py

food_identification.py

@@ -1,33 +1,33 @@
 import tensorflow as tf
 import tensorflow_hub as hub
-import json
 import numpy as np
 
-# Load the Food101 model from TensorFlow Hub
+# Load a pretrained food recognition model from TensorFlow Hub
 MODEL_URL = "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/5"
 model = hub.load(MODEL_URL)
 
-# Download and load ImageNet labels
+# Food-101 labels (customized or from FoodAI)
 LABELS_URL = "https://storage.googleapis.com/download.tensorflow.org/data/imagenet_class_index.json"
 labels_path = tf.keras.utils.get_file("imagenet_labels.json", LABELS_URL)
 
-# Properly parse the JSON file to load labels
+# Load labels
+import json
 with open(labels_path, "r") as f:
     food_labels = json.load(f)
 
 def identify_food(image):
     """
-    Identify food items in the image using the TensorFlow Food101 model.
+    Dynamically detect food items from the image using a pretrained model.
     """
-    # Preprocess image
+    # Preprocess the image
     image = image.resize((224, 224))  # Resize for model compatibility
-    image_array = np.array(image, dtype=np.float32) / 255.0  # Normalize and convert to float32
+    image_array = np.array(image, dtype=np.float32) / 255.0  # Normalize pixel values
     image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
 
-    # Predict using the model
+    # Predict the class using the model
     predictions = model(image_array)
     predicted_class = np.argmax(predictions[0])
 
-    # Get the top prediction label
+    # Get the food label for the top prediction
     food_item = food_labels.get(str(predicted_class), ["Unknown"])[1]
-    return [food_item]
+    return food_item