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.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+101_food_class_100_percent_saved_big_model/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

101_food_class_100_percent_saved_big_model/keras_metadata.pb

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+size 772844

101_food_class_100_percent_saved_big_model/saved_model.pb

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8f56a7ef6c9e44334287d49fc6659778101a3251e30a7aa867f740fabf3c65e9
+size 5951086

101_food_class_100_percent_saved_big_model/variables/variables.data-00000-of-00001

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4e297ea1a0347b28c514af39144a31d64c1abb4f40e2a6f3447702573f1c912b
+size 16922493

app.py

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+import tensorflow as tf
+import streamlit as st
+# import cv2
+from PIL import Image
+from streamlit_image_select import image_select
+# import os
+
+def load_and_prep_image(filename, img_shape=224, scale=True):
+  """
+  Reads in an image from filename, turns it into a tensor and reshapes into
+  (224, 224, 3).
+
+  Parameters
+  ----------
+  filename (str): string filename of target image
+  img_shape (int): size to resize target image to, default 224
+  scale (bool): whether to scale pixel values to range(0, 1), default True
+  """
+  # # Read in the image
+  # img = tf.io.read_file(filename)
+  # # Decode it into a tensor
+  # img = tf.io.decode_image(img)
+  img = tf.convert_to_tensor(filename)
+  # Resize the image
+  img = tf.image.resize(img, [img_shape, img_shape])
+  if scale:
+    # Rescale the image (get all values between 0 and 1)
+    return img/255.
+  else:
+    return img
+
+class_names = ['apple_pie', 'baby_back_ribs', 'baklava', 'beef_carpaccio', 'beef_tartare', 'beet_salad', 'beignets',
+    'bibimbap', 'bread_pudding', 'breakfast_burrito', 'bruschetta', 'caesar_salad', 'cannoli', 'caprese_salad', 'carrot_cake', 
+    'ceviche', 'cheese_plate', 'cheesecake', 'chicken_curry', 'chicken_quesadilla', 'chicken_wings', 'chocolate_cake',
+    'chocolate_mousse', 'churros', 'clam_chowder', 'club_sandwich', 'crab_cakes', 'creme_brulee', 'croque_madame', 
+    'cup_cakes', 'deviled_eggs', 'donuts', 'dumplings', 'edamame', 'eggs_benedict', 'escargots', 'falafel', 'filet_mignon', 
+    'fish_and_chips', 'foie_gras', 'french_fries', 'french_onion_soup', 'french_toast', 'fried_calamari', 'fried_rice', 
+    'frozen_yogurt', 'garlic_bread', 'gnocchi', 'greek_salad', 'grilled_cheese_sandwich', 'grilled_salmon', 'guacamole', 
+    'gyoza', 'hamburger', 'hot_and_sour_soup', 'hot_dog', 'huevos_rancheros', 'hummus', 'ice_cream', 'lasagna', 'lobster_bisque', 
+    'lobster_roll_sandwich', 'macaroni_and_cheese', 'macarons', 'miso_soup', 'mussels', 'nachos', 'omelette', 'onion_rings', 
+    'oysters', 'pad_thai', 'paella', 'pancakes', 'panna_cotta', 'peking_duck', 'pho', 'pizza', 'pork_chop', 'poutine', 'prime_rib', 
+    'pulled_pork_sandwich', 'ramen', 'ravioli', 'red_velvet_cake', 'risotto', 'samosa', 'sashimi', 'scallops', 'seaweed_salad', 
+    'shrimp_and_grits', 'spaghetti_bolognese', 'spaghetti_carbonara', 'spring_rolls', 'steak', 'strawberry_shortcake', 'sushi', 
+    'tacos', 'takoyaki', 'tiramisu', 'tuna_tartare', 'waffles']
+
+#load model
+@st.cache(allow_output_mutation = True)
+def cache_model(model_name):
+    model = tf.keras.models.load_model(model_name)
+    return (model)
+model = cache_model("101_food_class_100_percent_saved_big_model")
+# model = tf.keras.models.load_model("101_food_class_100_percent_saved_big_model")
+
+st.write("""
+         # Food Classification App
+         """
+         )
+
+st.write("""#### ***Upload food image and this app will classify the uploaded image from one of the mentioned categories.***""")
+
+st.write("""
+        **Some major food categories**
+        ```
+        pizza, cup_cakes, donuts, samosa, ice_cream, french_fries, waffles etc.
+        ```
+        for full categories list please visit the [link](https://github.com/gourav300/food_app)
+        """)
+
+### load file
+uploaded_file = st.file_uploader("Upload an image file for above mentioned Food Categories or select a food image", type=["jpg", "png", "jpeg"])
+
+
+test_img = image_select(
+    label="Select a sample food image",
+    images=[
+        "test_images/Cardamom-Saffron-Cupcakes-1.jpg",
+        "test_images/doughnut_1.jpg",
+        "test_images/frenchfries.jpg",
+        "test_images/Punjabi-Samosa-2.jpg",
+        "test_images/dumpling.jpg",
+        "test_images/pizza_1.jpg",
+        "test_images/waffle.jpg",
+        "test_images/pancake.jpg"
+    ],
+    captions=["Cupcake", "Doughnut", "French fries", "Samosa", "Dumpling", "Pizza", "Waffle", "Pancake"],
+)
+
+# st.set_option('deprecation.showfileUploaderEncoding', False)
+
+
+if uploaded_file is not None:
+
+    image = Image.open(uploaded_file)
+    st.image(image,width = 500)#, use_column_width=True)
+else:
+    image = Image.open(test_img)
+    st.image(image, width = 500)#,use_column_width=True)
+    
+# Load the image and make predictions
+img = load_and_prep_image(image, scale=False) # don't scale images for EfficientNet predictions
+pred_prob = model.predict(tf.expand_dims(img, axis=0)) # model accepts tensors of shape [None, 224, 224, 3]
+pred_class = class_names[pred_prob.argmax()] # find the predicted class
+
+st.write(f"## Predicted food: {pred_class}, with Probability: {pred_prob.max():.2f}")

requirements.txt

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+tensorflow == 2.11.0
+streamlit == 1.15.2
+Pillow == 9.3.0
+streamlit-image-select == 0.5.1