add model

.gitattributes

@@ -33,3 +33,4 @@ 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
+.h5 filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,7 +1,103 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+#export
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.optimizers import Adam, Adamax
+from tensorflow.keras.metrics import categorical_crossentropy
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Activation, Dropout, BatchNormalization
+from tensorflow.keras import regularizers
+from keras.callbacks import EarlyStopping, LearningRateScheduler
+import numpy as np
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.efficientnet import preprocess_input
 import gradio as gr
+# In[2]:
+
+
+# Create Model Structure
+class_labels = ['Angry', 'Other', 'Sad', 'Happy']
+
+
+img_size = (224, 224)
+channels = 3
+img_shape = (img_size[0], img_size[1], channels)
+class_count = len(class_labels) # to define number of classes in dense layer
+
+# create pre-trained model (you can built on pretrained model such as :  efficientnet, VGG , Resnet )
+# we will use efficientnetb3 from EfficientNet family.
+base_model = tf.keras.applications.efficientnet.EfficientNetB5(include_top= False, weights= "imagenet", input_shape= img_shape, pooling= 'max')
+base_model.trainable = False
+
+model = Sequential([
+    base_model,
+    BatchNormalization(axis= -1, momentum= 0.99, epsilon= 0.001),
+    Dense(256, activation='relu'),
+    Dense(128, kernel_regularizer= regularizers.l2(l= 0.016), activity_regularizer= regularizers.l1(0.006),
+                bias_regularizer= regularizers.l1(0.006), activation= 'relu'),
+    Dropout(rate= 0.45, seed= 123),
+    Dense(class_count, activation= 'softmax')
+])
+
+model.trainable = False
+
+# model.compile(Adamax(learning_rate= 0.001), loss= 'categorical_crossentropy', metrics= ['accuracy'])
 
-def greet(name):
-    return "Hello " + name + "!!"
+model.summary()
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+# In[6]:
+
+
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.efficientnet import preprocess_input
+import matplotlib.pyplot as plt
+
+class_labels = ['Angry', 'Other', 'Sad', 'Happy']
+model.load_weights('my_model_weights.h5')
+
+
+def predict_and_display(img_array):
+    
+    # img = image.load_img(image_path, target_size=(224, 224))
+    # img_array = image.img_to_array(img)
+    # print(imimg_arrayg)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = preprocess_input(img_array)
+
+    prediction = model.predict(img_array)
+    predicted_class_index = np.argmax(prediction)
+    
+    # class_indices = train_gen.class_indices
+    # class_labels = list(class_indices.keys())
+    predicted_class_label = class_labels[predicted_class_index]
+    
+    # plt.imshow(img)
+    # plt.axis('off')
+    # if predicted_class_label == 'Other':
+    #     plt.title(f"The pet is normal")
+    # else:
+    #     plt.title(f"The Pet is {predicted_class_label}")
+    # plt.show()
+    return f"This pet is {predicted_class_label}"
+
+    
+# In[11]:
+
+# In[ ]:
+
+iface = gr.Interface(fn=predict_and_display, 
+                     inputs=gr.Image(shape=(224, 224)), 
+                     outputs="label",
+                     title="Pet Emotion Detection",
+                     description="Fine tune EfficientNet on Pet's emotion datasets",
+                     examples=[["examples/happy_cat.jpeg"], ["examples/happy_dog.jpeg"], ["examples/cats.jpg"]]
+                     )
 iface.launch()
+
+# %%

inference.py

@@ -0,0 +1,102 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+#export
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.optimizers import Adam, Adamax
+from tensorflow.keras.metrics import categorical_crossentropy
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Activation, Dropout, BatchNormalization
+from tensorflow.keras import regularizers
+from keras.callbacks import EarlyStopping, LearningRateScheduler
+import numpy as np
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.efficientnet import preprocess_input
+
+# In[2]:
+
+
+# Create Model Structure
+class_labels = ['Angry', 'Other', 'Sad', 'Happy']
+
+
+img_size = (224, 224)
+channels = 3
+img_shape = (img_size[0], img_size[1], channels)
+class_count = len(class_labels) # to define number of classes in dense layer
+
+# create pre-trained model (you can built on pretrained model such as :  efficientnet, VGG , Resnet )
+# we will use efficientnetb3 from EfficientNet family.
+base_model = tf.keras.applications.efficientnet.EfficientNetB5(include_top= False, weights= "imagenet", input_shape= img_shape, pooling= 'max')
+base_model.trainable = False
+
+model = Sequential([
+    base_model,
+    BatchNormalization(axis= -1, momentum= 0.99, epsilon= 0.001),
+    Dense(256, activation='relu'),
+    Dense(128, kernel_regularizer= regularizers.l2(l= 0.016), activity_regularizer= regularizers.l1(0.006),
+                bias_regularizer= regularizers.l1(0.006), activation= 'relu'),
+    Dropout(rate= 0.45, seed= 123),
+    Dense(class_count, activation= 'softmax')
+])
+
+model.trainable = False
+
+# model.compile(Adamax(learning_rate= 0.001), loss= 'categorical_crossentropy', metrics= ['accuracy'])
+
+model.summary()
+
+# In[6]:
+
+
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.efficientnet import preprocess_input
+import matplotlib.pyplot as plt
+
+class_labels = ['Angry', 'Other', 'Sad', 'Happy']
+
+
+def predict_and_display(image_path, model):
+    
+    img = image.load_img(image_path, target_size=(224, 224))
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = preprocess_input(img_array)
+
+    prediction = model.predict(img_array)
+    predicted_class_index = np.argmax(prediction)
+    
+    # class_indices = train_gen.class_indices
+    # class_labels = list(class_indices.keys())
+    predicted_class_label = class_labels[predicted_class_index]
+    
+    plt.imshow(img)
+    plt.axis('off')
+    if predicted_class_label == 'Other':
+        plt.title(f"The pet is normal")
+    else:
+        plt.title(f"The Pet is {predicted_class_label}")
+    plt.show()
+
+model.load_weights('my_model_weights.h5')
+
+
+# Replace 'path_to_test_image' with the path to the image you want to test
+image_path_to_test = 'pets-facial-expression-dataset/Angry/02.jpg'
+predict_and_display(image_path_to_test, model)
+
+# In[11]:
+
+
+image_path_to_test = 'IMG_0243.jpg'
+predict_and_display(image_path_to_test, model)
+
+# In[ ]:
+
+
+

my_model_weights.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:db40006daa10fd8afd6717e3adac116fd1bcfa7de1010b62c3f54e9b41484113
+size 116948920