Update app.py

app.py

@@ -1,153 +1,13 @@
-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("weights.best.InceptionV3.hdf5",)
-
-#dog_names = [item[20:-1] for item in sorted(glob("dogImages/train/*/"))]
-
-dog_names= ['Affenpinscher', 'Afghan_hound', 'Airedale_terrier', 'Akita', 'Alaskan_malamute', 'American_eskimo_dog', 'American_foxhound', 'American_staffordshire_terrier', 'American_water_spaniel', 'Anatolian_shepherd_dog', 'Australian_cattle_dog', 'Australian_shepherd', 'Australian_terrier', 'Basenji', 'Basset_hound', 'Beagle', 'Bearded_collie', 'Beauceron', 'Bedlington_terrier', 'Belgian_malinois', 'Belgian_sheepdog', 'Belgian_tervuren', 'Bernese_mountain_dog', 'Bichon_frise', 'Black_and_tan_coonhound', 'Black_russian_terrier', 'Bloodhound', 'Bluetick_coonhound', 'Border_collie', 'Border_terrier', 'Borzoi', 'Boston_terrier', 'Bouvier_des_flandres', 'Boxer', 'Boykin_spaniel', 'Briard', 'Brittany', 'Brussels_griffon', 'Bull_terrier', 'Bulldog', 'Bullmastiff', 'Cairn_terrier', 'Canaan_dog', 'Cane_corso', 'Cardigan_welsh_corgi', 'Cavalier_king_charles_spaniel', 'Chesapeake_bay_retriever', 'Chihuahua', 'Chinese_crested', 'Chinese_shar-pei', 'Chow_chow', 'Clumber_spaniel', 'Cocker_spaniel', 'Collie', 'Curly-coated_retriever', 'Dachshund', 'Dalmatian', 'Dandie_dinmont_terrier', 'Doberman_pinscher', 'Dogue_de_bordeaux', 'English_cocker_spaniel', 'English_setter', 'English_springer_spaniel', 'English_toy_spaniel', 'Entlebucher_mountain_dog', 'Field_spaniel', 'Finnish_spitz', 'Flat-coated_retriever', 'French_bulldog', 'German_pinscher', 'German_shepherd_dog', 'German_shorthaired_pointer', 'German_wirehaired_pointer', 'Giant_schnauzer', 'Glen_of_imaal_terrier', 'Golden_retriever', 'Gordon_setter', 'Great_dane', 'Great_pyrenees', 'Greater_swiss_mountain_dog', 'Greyhound', 'Havanese', 'Ibizan_hound', 'Icelandic_sheepdog', 'Irish_red_and_white_setter', 'Irish_setter', 'Irish_terrier', 'Irish_water_spaniel', 'Irish_wolfhound', 'Italian_greyhound', 'Japanese_chin', 'Keeshond', 'Kerry_blue_terrier', 'Komondor', 'Kuvasz', 'Labrador_retriever', 'Lakeland_terrier', 'Leonberger', 'Lhasa_apso', 'Lowchen', 'Maltese', 'Manchester_terrier', 'Mastiff', 'Miniature_schnauzer', 'Neapolitan_mastiff', 'Newfoundland', 'Norfolk_terrier', 'Norwegian_buhund', 'Norwegian_elkhound', 'Norwegian_lundehund', 'Norwich_terrier', 'Nova_scotia_duck_tolling_retriever', 'Old_english_sheepdog', 'Otterhound', 'Papillon', 'Parson_russell_terrier', 'Pekingese', 'Pembroke_welsh_corgi', 'Petit_basset_griffon_vendeen', 'Pharaoh_hound', 'Plott', 'Pointer', 'Pomeranian', 'Poodle', 'Portuguese_water_dog', 'Saint_bernard', 'Silky_terrier', 'Smooth_fox_terrier', 'Tibetan_mastiff', 'Welsh_springer_spaniel', 'Wirehaired_pointing_griffon', 'Xoloitzcuintli', 'Yorkshire_terrier']
-
-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('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=False) 
-
-
-
-
+from gradio.mix import Series
+
+description = "Generate your own D&D story!"
+title = "French Story Generator using Opus MT and GPT-2"
+translator_fr = gr.Interface.load("huggingface/Helsinki-NLP/opus-mt-fr-en")
+story_gen = gr.Interface.load("huggingface/pranavpsv/gpt2-genre-story-generator")
+translator_en = gr.Interface.load("huggingface/Helsinki-NLP/opus-mt-en-fr")
+examples = [["L'aventurier est approché par un mystérieux étranger, pour une nouvelle quête."]]
+
+Series(translator_fr, story_gen, translator_en, description = description,
+        title = title,
+        examples=examples, inputs = gr.inputs.Textbox(lines = 10)).launch()