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app.py
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app.py
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from flask import Flask, request, render_template
import pickle
import numpy as np
app = Flask(__name__)
@app.route('/')
def home():
return render_template('home.html')
@app.route('/getprediction',methods=['POST','GET'])
def get_delay():
if request.method=='POST':
result=request.form
#Get form contents
tv = result['trading_volume']
change = result['change_in_percent']
oc = result['Open-Close']
hl = result['High-Low']
#Prepare the feature vector for prediction
pkl_file = open('var_predict', 'rb')
index_dict = pickle.load(pkl_file)
cat_vector = np.zeros(len(index_dict)).reshape(1,-1)
try:
cat_vector[index_dict['trading_volume'+str(tv)]] = 1
except:
pass
try:
cat_vector[index_dict['change_in_%'+str(change)]] = 1
except:
pass
try:
cat_vector[index_dict['Open-Close'+str(oc)]] = 1
except:
pass
try:
cat_vector[index_dict['High-Low'+str(hl)]] = 1
except:
pass
#Load model (SVC)
pkl_file = open('model.pkl', 'rb')
model = pickle.load(pkl_file)
prediction = model.predict(cat_vector)
return render_template('result.html',prediction=prediction)
if __name__ == '__main__':
app.debug = True
app.run()