Recreate app.py

app.py

@@ -1,7 +1,150 @@
+import tensorflow.keras as keras
+import extract_bottleneck_features
+import cv2      
 import gradio as gr
+import numpy as np      
+from glob import glob
+from keras.preprocessing import image           
+InceptionV3_model = keras.models.load_model("saved_models/weights.best.InceptionV3.hdf5",)
+
+dog_names = [item[20:-1] for item in sorted(glob("dogImages/train/*/"))]
+labels =  dog_names
+
+def extract_InceptionV3(tensor):
+	from keras.applications.inception_v3 import InceptionV3, preprocess_input
+	return InceptionV3(weights='imagenet', include_top=False).predict(preprocess_input(tensor))
+
+
+def extract_Resnet50(tensor):
+	from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
+	return ResNet50(weights='imagenet', include_top=False).predict(preprocess_input(tensor))
+
+
+###########################################
+
+from tensorflow.keras.applications.resnet50 import preprocess_input
+
+######################################
+
+import tensorflow as tf
+from keras.preprocessing import image                  
+from tqdm import tqdm
+
+######################################
+
+from tensorflow.keras.applications.resnet50 import ResNet50
+# define ResNet50 model
+ResNet50_model = ResNet50(weights='imagenet')
+
+from keras.preprocessing import image                  
+from tqdm import tqdm
+
+
+
+from tensorflow.keras.applications.resnet50 import preprocess_input
+
+def ResNet50_predict_labels(img):
+    # returns prediction vector for image located at img_path
+    img = np.expand_dims(img, axis=0)
+    img = preprocess_input((img))
+    return np.argmax(ResNet50_model.predict(img)) 
+
+
+def path_to_tensor(img_path):
+    # loads RGB image as PIL.Image.Image type
+    #img = image.load_img(img_path, target_size=(224, 224))
+    # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
+    #x = image.img_to_array(img)
+    # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
+    return np.expand_dims(img_path, axis=0)
+
+
+
+# extract pre-trained face detector
+face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')
+
+
+
+def face_detector(image):
+    """
+    returns "True" if face is detected in image stored at image
+
+    """
+    
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray)
+    if len(faces) > 0:
+        return "Number of human faces found in this image: {}". format(len(faces))
+    else:
+        return "There are no human faces in this image"
+
+
+
+
+def InceptionV3_prediction_breed(img_path):
+    """
+    Return: dog breed that is predicted by the model
+    input: image
+    """
+    
+    # extract bottleneck features
+    bottleneck_feature = extract_InceptionV3(path_to_tensor(img_path))
+    # obtain predicted vector
+    predicted_vector = InceptionV3_model.predict(bottleneck_feature)
+    # return dog breed that is predicted by the model
+    return dog_names[np.argmax(predicted_vector)].split('.')[-1]
+
+
+
+def dog_detector(img):
+    """
+    input: uploaded image by user
+    return: "True" if a dog is detected in the image stored at img
+    """
+    
+    prediction = ResNet50_predict_labels(img)
+    return ((prediction <= 268) & (prediction >= 151)) 
+
+def identify_dog_app(img):
+    """This function predicts the breed of the human or dog"
+    
+    input: uploaded image by user
+    Return: dog or human, and breed of the uploaded image
+    """
+    
+    breed = InceptionV3_prediction_breed(img)
+    if dog_detector(img):
+        return("This looks like a dog and its breed is:"),"{}".format(breed)
+    elif face_detector(img):
+        return("This looks like a human but might be classified as a dog of the following breed:"),"{}".format(breed)
+    else:
+        return("I have no idea what this might be. Please upload another image!"), ("Not applicable")
+
+
+
+
+image = gr.inputs.Image(shape=(224, 224),  label="Image")
+label = gr.outputs.Label(num_top_classes=1)    
+
+iface = gr.Interface(   
+    fn=identify_dog_app,
+    inputs=image,
+    outputs=[gr.outputs.Label(label="Human or Dog?"), gr.outputs.Label(label="Breed:")],  
+    title="Human or dog Identification - Breed Classification",
+    #description ="Please find the jypyter notebook on ___",
+    article = 
+    '<b><span style="color: #ff9900;">Acknowledgement:</span></b><br/>'
+    +'<p><span style="color: #ff9900;">I would like to express my special thanks of gratitude'
+    +'to Misk &amp; Sdaia for giving me the opportunity to enrol in "Data Scientist" Udacity nanodegree,'
+    +'&nbsp;as well as to my mentor Mr. Haroon who was of great help during my learning journey.</span></p>'
+    +'<p><span style="color: #ff9900;">This is my capstone project and herewith I finish this ND.</span></p>',
+
+    theme="dark-huggingface"
+
+)
+
+iface.launch(share=True)
+
+
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()