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Getting an error during runtime after submitting values for a regression model in flask
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Error:
Traceback (most recent call last): File "D:\Anaconda\lib\site-packages\flask\app.py", line 2463, in __call__ return self.wsgi_app(environ, start_response) File "D:\Anaconda\lib\site-packages\flask\app.py", line 2449, in wsgi_app response = self.handle_exception(e) File "D:\Anaconda\lib\site-packages\flask\app.py", line 1866, in handle_exception reraise(exc_type, exc_value, tb) File "D:\Anaconda\lib\site-packages\flask\_compat.py", line 39, in reraise raise value File "D:\Anaconda\lib\site-packages\flask\app.py", line 2446, in wsgi_app response = self.full_dispatch_request() File "D:\Anaconda\lib\site-packages\flask\app.py", line 1951, in full_dispatch_request rv = self.handle_user_exception(e) File "D:\Anaconda\lib\site-packages\flask\app.py", line 1820, in handle_user_exception reraise(exc_type, exc_value, tb) File "D:\Anaconda\lib\site-packages\flask\_compat.py", line 39, in reraise raise value File "D:\Anaconda\lib\site-packages\flask\app.py", line 1949, in full_dispatch_request rv = self.dispatch_request() File "D:\Anaconda\lib\site-packages\flask\app.py", line 1935, in dispatch_request return self.view_functions[rule.endpoint](**req.view_args) File "D:\Flask\app.py", line 30, in login ypred = model.predict(np.array(total)) File "D:\Anaconda\lib\site-packages\keras\engine\training.py", line 1462, in predict callbacks=callbacks) File "D:\Anaconda\lib\site-packages\keras\engine\training_arrays.py", line 324, in predict_loop batch_outs = f(ins_batch) File "D:\Anaconda\lib\site-packages\tensorflow\python\keras\backend.py", line 3292, in __call__ run_metadata=self.run_metadata) File "D:\Anaconda\lib\site-packages\tensorflow\python\client\session.py", line 1458, in __call__ run_metadata_ptr) tensorflow.python.framework.errors_impl.FailedPreconditionError: Error while reading resource variable dense_3/bias from Container: localhost. This could mean that the variable was uninitialized. Not found: Container localhost does not exist. (Could not find resource: localhost/dense_3/bias) [[{{node dense_3/BiasAdd/ReadVariableOp}}]]app.py file;
from flask import Flask,request,render_template from keras.models import load_model import numpy as np global model, graph import tensorflow as tf graph = tf.get_default_graph() model = load_model('regressor.h5') app = Flask(__name__) @app.route('/')#when even the browser finds localhost:5000 then def home():#excecute this function return render_template('index.html')#this function is returing the index.html file @app.route('/login', methods =['POST']) #when you click submit on html page it is redirection to this url def login():#as soon as this url is redirected then call the below functionality a = request.form['a'] b = request.form['b'] c = request.form['c'] d = request.form['s'] if (d == "newyork"): s1,s2,s3 = 0,0,1 if (d == "florida"): s1,s2,s3 = 0,1,0 if (d == "california"): s1,s2,s3 = 1,0,0 total = [[s1,s2,s3,a,b,c]] with graph.as_default(): ypred = model.predict(np.array(total)) y = ypred[0][0] print(ypred) # from html page what ever the text is typed that is requested from the form functionality and is stored in a name variable return render_template('index.html' ,abc = y)#after typing the name show this name on index.html file where we have created a varibale abc if __name__ == '__main__': app.run(debug = True)html file;
<html> <body> <form action = "http://localhost:5000/login" method = "post"> <p>enter marketing speed amount</p> <p> <input type = "text" name = "a" /></p> <p>enter Administartive amount</p> <p> <input type = "text" name = "b" /></p> <p>enter R and d amount</p> <p> <input type = "text" name = "c" /></p> <select name = 's'> <option value = "newyork"> newyork </option> <option value = "florida"> florida </option> <option value = "california"> california </option> </select> <p> <input type = "submit" value = "submit"/></p> </form> <b>{{abc}}</b> </body> </html>Regression Model;
import numpy as np import pandas as pd import sklearn import keras data=pd.read_csv(r"C:\Users\anil\Anaconda3\50_Startups.csv") from sklearn.preprocessing import LabelEncoder le=LabelEncoder() x=data.iloc[:,:4].values y=data.iloc[:,-1:].values x[:,3]=le.fit_transform(x[:,3]) from sklearn.preprocessing import OneHotEncoder one=OneHotEncoder() z=one.fit_transform(x[:,3:]).toarray() x=np.delete(x,3,axis=1) x=np.concatenate((z,x),axis=1) from sklearn.model_selection import train_test_split x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0) from sklearn.preprocessing import StandardScaler sc=StandardScaler() x_train=sc.fit_transform(x_train) x_test=sc.transform(x_test) from keras.models import Sequential from keras.layers import Dense regressor=Sequential() regressor.add(Dense(units=6,init="random_uniform",activation="relu")) regressor.add(Dense(units=7,init="random_uniform",activation="relu")) regressor.add(Dense(units=8,init="random_uniform",activation="relu")) regressor.add(Dense(units=9,init="random_uniform",activation="relu")) regressor.add(Dense(units=9,init="random_uniform",activation="relu")) regressor.add(Dense(units=9,init="random_uniform",activation="relu")) regressor.add(Dense(units=9,init="random_uniform",activation="relu")) regressor.add(Dense(units=9,init="random_uniform",activation="relu")) regressor.add(Dense(units=9,init="random_uniform",activation="relu")) regressor.add(Dense(units=9,init="random_uniform",activation="relu")) regressor.add(Dense(units=1,init="random_uniform")) regressor.compile(optimizer='adam',loss='mse',metrics=['mse']) regressor.fit(x_train,y_train,batch_size=10,epochs=170) y_pred = regressor.predict(x_test) print(y_pred) import matplotlib.pyplot as plt plt.plot(y_pred,color='red') plt.plot(y_test,color='blue') from sklearn.metrics import r2_score accuracy = r2_score(y_test,y_pred) print(accuracy) regressor.save('regressor.h5')please help, i do not seem to understand the error. The data was from a csv file called 50_Startups.csv I'm new to flask. The model was trained to predict the profit earned by a startup by studying the following inputs; State,Market spend,R and D spending,Administrative spending.
