Delete hf_present_bit_fined_grainedclassification.py

hf_present_bit_fined_grainedclassification.py

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-# -*- coding: utf-8 -*-
-"""HF-Present_BiT_Fined_grainedClassification.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/11qlOL9TSLiX4a-D6rwkxw7sp2zyUHCE9
-
-# IMPORT LIBRARIES AND DATASET
-"""
-
-#@title Libraries
-import tensorflow as tf
-import tensorflow_hub as hub
-
-import tensorflow_datasets as tfds
-
-import time
-
-from PIL import Image
-import requests
-from io import BytesIO
-
-import matplotlib.pyplot as plt
-import numpy as np
-
-import os
-import pathlib
-
-#@title Gradio
-!pip install gradio --quiet
-import gradio as gr
-
-from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
-from keras.preprocessing import image
-import tensorflow as tf
-
-#@title Dùng TensorFlow Datasets để load bộ data oxford_flowers102
-import tensorflow_datasets as tfds
-
-(train, val, test), info = tfds.load('oxford_flowers102', 
-                                      split=['train', 'validation', 'test'],
-                                      shuffle_files=True,
-                                      as_supervised=True, 
-                                      with_info=True)
-NUM_CLASSES = 102
-
-#@title Lưu danh sách tên vào file txt
-f = open("flower_names.txt", "w")
-f.write(info.features['label'].names[0])
-
-i=1
-while i<102:
-  f.write(" \n")
-  f.write(info.features['label'].names[i])
-  i+=1
-f.close()
-
-from google.colab import drive
-drive.mount('/content/drive')
-
-"""# Data_Preprocessing
-
-"""
-
-from tensorflow import cast, float32
-from tensorflow.data.experimental import AUTOTUNE
-from tensorflow import one_hot 
-from tensorflow.image import resize
-
-def preprocess_data(image, label):
-  """
-  Normalizes images: `uint8` -> `float32`.
-  One hot encoding labels
-  Resize to (224, 224)
-  """
-
-
-  return resize(cast(image, float32)/255. , [224, 224]), one_hot(label, 102)
-
-"""# GRADIO"""
-
-# Load a previously trained model for gradio demonstration
-model2=tf.keras.models.load_model('/content/drive/MyDrive/Colab Notebooks/BiT fine_grained-17Jun-20220617T065450Z-001/BiT fine_grained-17Jun')
-
-model2.summary()
-
-with open('/content/flower_names.txt') as f:
-    labels = f.readlines()
-
-from numpy import exp
-def softmax(vector):
- e = exp(vector)
- return e / e.sum()
-
-def image_to_output (input_img):
-
- 
-
-
-  gr_img=[]
-  gr_img.append(input_img)
-  img2=resize(cast(gr_img, float32)/255. , [224, 224])
-
-  #print(img2)
-
-  x_test=np.asarray(img2)
-
-  prediction = model2.predict(x_test,batch_size=1).flatten()
-  prediction=softmax(prediction)
-
-  confidences = {labels[i]: float(prediction[i]) for i in range(102)}
-#  confidences = {labels[i]:float(top[i]) for i in range(num_predictions)}
-
-  return confidences
-
-import gradio as gr
-
-gr.Interface(fn=image_to_output, 
-             inputs=gr.inputs.Image(shape=(224,224)),
-             outputs=gr.outputs.Label(num_top_classes=5),
-             interpretation="default"
-             ).launch()
-
-"""## Acknowledgements
-
-This colab is based on 
-
- BigTransfer (BiT): A step-by-step tutorial for state-of-the-art vision
-By Jessica Yung and Joan Puigcerver
-"""