williamantony3

line-bot-sdk
flask
requests

williamantony3

from flask import Flask, request, abort

from linebot import (
    LineBotApi, WebhookHandler
)
from linebot.exceptions import (
    InvalidSignatureError
)
from linebot.models import *
#tambahkan ini#########################

williamantony3

def handle_message(event):
    msg_from_user = event.message.text
    if msg_from_user == 'Data-covid':
    	message = TextSendMessage("Data COVID-19 " + negara + "\nPositif: " + positif + "\nSembuh: " + sembuh + "\nMeninggal: " + meninggal)
    	line_bot_api.reply_message(event.reply_token, message)

williamantony3

from linebot.models import *
#tambahkan ini#########################
import requests
import json
url = "https://api.kawalcorona.com/indonesia/"
response = requests.get(url)
parsed = response.json()[0]
negara = parsed["name"]
positif = parsed["positif"]
sembuh = parsed["sembuh"]

williamantony3

# Channel Access Token
line_bot_api = LineBotApi('YOUR_CHANNEL_ACCESS_TOKEN')
# Channel Secret
handler = WebhookHandler('YOUR_CHANNEL_SECRET')

williamantony3

print("Confusion Matrix Test")
df_cm = pd.DataFrame(cm, index = (0, 1), columns = (0, 1))
plt.figure(figsize = (10,7))
sn.set(font_scale=1.4)
sn.heatmap(df_cm, annot=True, fmt='g')
print("Confusion Matrix Train")
df_cm_train = pd.DataFrame(cm_train, index = (0, 1), columns = (0, 1))
plt.figure(figsize = (10,7))
sn.set(font_scale=1.4)
sn.heatmap(df_cm_train, annot=True, fmt='g')

williamantony3

## EXTRA: Confusion Matrix
cm = confusion_matrix(y_test, y_pred) # rows = truth, cols = prediction
cm_train = confusion_matrix(y_train, y_pred_train)
print("Test Data Accuracy: %0.4f" % accuracy_score(y_test, y_pred))
print(cm)
print("Train Data Accuracy: %0.4f" % accuracy_score(y_train, y_pred_train))
print(cm_train)

williamantony3

#Let's see how our model performed
print("Hasil Test")
print(classification_report(y_test, y_pred))

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# Predict
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)
score = classifier.evaluate(X_test, y_test)
print("Score test", score)
y_pred_train = classifier.predict(X_train)
y_pred_train = (y_pred_train > 0.5)
score_pred_train = classifier.evaluate(X_train, y_train)
print("Score train", score_pred_train)

williamantony3

# list all data in history
print(history.history.keys())
# summarize history for accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()