Delete deploy (1).py

deploy (1).py

@@ -1,79 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Deploy
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1bK9oazNpp0ELz8EATsAmWYgKaiUpdV3W
-"""
-
-import os
-
-import tensorflow as tf
-
-import keras
-from keras.models import Sequential
-from keras.layers import Dense, Dropout, Flatten
-from keras.layers import Conv2D, MaxPooling2D, Conv3D, BatchNormalization
-from keras import backend as K
-from PIL import Image
-import numpy as np
-
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import OneHotEncoder
-
-import matplotlib.pyplot as plt
-from matplotlib.pyplot import imshow
-
-import pandas as pd
-import zipfile
-from google.colab import files
-
-from tensorflow.keras.models import load_model
-
-model=load_model("/content/model.h5")
-
-!pip install gradio
-import gradio as gr
-
-pip install --upgrade gradio
-
-def trans_image(pillow_image):
-    a = np.array(pillow_image.resize((128,128)))
-    a = a.reshape(1,128,128,3)
-    return a
-
-def brain(predict):
-    if predict==0:
-        return 'Glioma Tumor'
-    elif predict==1:
-        return 'Meningioma Tumor'
-    elif predict==2:
-        return 'No Tumor or Normal'
-    else:
-        return 'Pituitary Tumor'
-    
-def predictions(x):
-    a = model.predict(x)
-    classi = np.where(a == np.amax(a))[1][0]
-    return str(a[0][classi]*100), brain(classi)
-
-class_names = ['Glioma Tumor','Meningioma Tumor','No Tumor or Normal','Pituitary Tumor']
-
-import gradio
-import requests
-
-def predicts(img):
-    img = img.reshape((1, 128, 128, 3))
-    prediction = model.predict(img).flatten()
-
-    return {class_names[i]: float(prediction[i]) for i in range(4)}
-image = gradio.inputs.Image(shape=(128, 128), source="upload")
-label = gr.outputs.Label(num_top_classes= 5)
-
-iface = gr.Interface(
-    fn = predicts, 
-    inputs = image, 
-    outputs = label,
-    )
-iface.launch(debug=True, share=True)