#pip install imageai #pip install Tensorflow #pip install keras from imageai.Prediction import ImagePrediction import os from keras.models import load_model Givenimage= "picc.jpg" execution_path=os.getcwd() #print(execution_path) prediction = ImagePrediction() prediction.setModelTypeAsSqueezeNet() prediction.setModelPath(os.path.join(execution_path, "squeezenet_weights_tf_dim_ordering_tf_kernels.h5" )) prediction.loadModel() predictions, probabilities = prediction.predictImage(os.path.join(execution_path, Givenimage), result_count= 3 ) for eachPrediction, eachProbability in zip (predictions, probabilities): print (eachPrediction , " : " , eachProbability) Result: WARNING:tensorflow:11 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f941e211598> tr...
import requests from bs4 import BeautifulSoup import pprint url= 'https://news.ycombinator.com/' url2= 'https://news.ycombinator.com/news?p=2' res=requests.get(url) res2=requests.get(url2) soup=BeautifulSoup(res.text, 'html.parser' ) soup2=BeautifulSoup(res2.text, 'html.parser' ) links=soup.select( '.storylink' ) subtext=soup.select( '.subtext' ) links2=soup2.select( '.storylink' ) subtext2=soup2.select( '.subtext' ) def sorting_votes ( hnlist ): return sorted (hnlist,key= lambda k:k[ 'votes' ],reverse= True ) megalinks=links + links2 megasubtext=subtext+subtext2 def custom_hn ( links , subtext ): hn=[] for idx,item in enumerate (links): title=links[idx].getText() href=links[idx].get( 'href' , None ) vote=subtext[idx].select( '.score' ) if...