# Flask網頁 ``` import os import numpy as np import pandas as pd import time from sklearn.svm import SVC from sklearn.externals import joblib from sklearn.metrics import classification_report import load_svm_model from flask import Flask, request,render_template from flask_uploads import UploadSet, configure_uploads, IMAGES,patch_request_class app = Flask(__name__) app.config['UPLOADED_PHOTOS_DEST'] = os.getcwd() photos = UploadSet('photos', IMAGES) configure_uploads(app, photos) patch_request_class(app) html = ''' <!DOCTYPE html> <title>Upload File</title> <h1>image upload</h1> <form method=post enctype=multipart/form-data> <input type=file name=photo> <input type=submit value=上傳> </form> ''' @app.route('/', methods=['GET', 'POST']) def upload_file(): if request.method == 'POST' and 'photo' in request.files: filename = photos.save(request.files['photo']) file_url = photos.url(filename) return html + '<br><img src=' + file_url + '>' + '<br>' + "This image is " + pred return html if __name__ == '__main__': df = pd.read_csv('./featuresOne.csv') #transform DataFrame to numpy.array DataSet=df.values #make Format Feature:[[a1,b1,...,z1],.....[aN,bN,...,zN]]Label:[L1,L2,L3,...,LN] Feature = DataSet[:,2:] Label = DataSet[:,1:2] Label = Label.flatten() pred = load_svm_model.evaluate(Feature,Label) app.run(port=9000, debug=True) ``` 第一部分是上傳圖片的時候先執行擷取特徵裡面的函式把CSV做出來再執行load_svm_model。 第二部分把load_svm_model.py import進去，執行裡面的函式。