def load_model(file_path): """ Load a pysster.Model object. Parameters ---------- file_path : str A file containing a pickled pysster.Model object (file_path.h5 must also exist, see save_model()). Returns ------- model : pysster.Model A Model object. """ from pysster.Model import Model from keras.models import load_model as load_keras if not os.path.exists(file_path): raise RuntimeError("Path not found.") if not os.path.exists("{}.h5".format(file_path)): raise RuntimeError("HDF5 file not found.") with gzip.open(file_path, "rb") as handle: params = pickle.load(handle) model = Model(params, None) model.model = load_keras("{}.h5".format(file_path)) return model
from keras.models import load_keras from scikit-image import transform import numpy as np from keras.preprocessing.image import load_img from flask import Flask, request, render_template , jsonify app = Flask(__name__) covid = load_keras('Covid19.h5') def Load_Images(img): pred_img = np.array(img).astype('float32')/255 pred_img = transform.resize(pred_img,(200,200,3)) pred_img = np.expand_dims(pred_img,axis=0) return pred_img @app.route('/') def index(): print('Working') @app.route('/CovidRequest', methods=['POST']) def post(): imagefile = request.files.get('imagefile', '') Image_to_pred = load_img(imagefile) Image_to_pred = Load_Images(Image_to_pred) prediction = np.argmax(covid.predict(Image_to_pred)) if (prediction == 0): return jsonify({'prediction':'Non Informative Data'}) elif (prediction == 1): return jsonify({'prediction':'Negative'})