67 lines
1.8 KiB
Python
Executable File
67 lines
1.8 KiB
Python
Executable File
import logging
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import firebase_admin
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from firebase_admin import credentials, firestore
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from flask import Flask
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from tensorflow import keras
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class Firebase:
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def __init__(self):
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self.creds = credentials.Certificate(
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'icl-iot-weather-firebase-adminsdk.json')
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firebase_admin.initialize_app(self.creds)
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self.db = firestore.client()
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def pull_from_db(self, orderby=u'timestamp'):
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doc_ref = self.db.collection('weather_data')
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query = doc_ref.order_by(orderby,
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direction=firestore.Query.DESCENDING).limit(1)
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doc = query.stream()
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return doc
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def convert_to_float(self, data):
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doc_elements = []
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for element in data:
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doc_elements.append(element)
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feature_temp = doc_elements[0].to_dict()['temp']
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return feature_temp
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def get_feature(self):
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doc = self.pull_from_db()
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feature = self.convert_to_float(doc)
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return feature
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class VirtualProbe:
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def __init__(self):
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self.firebase = Firebase()
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self.model = keras.models.load_model('virtual_probe.model')
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def predict_soil_temp(self):
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feature = self.firebase.get_feature()
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predicted_temp = self.model.predict([feature])
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return predicted_temp
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if __name__ == "__main__":
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logging.root.setLevel(logging.DEBUG)
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probe = VirtualProbe()
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node = Flask(__name__)
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@node.route("/")
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def root():
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return "IoT-ICL DE Weather Master Node running..."
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@node.route("/hmdt")
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def humidity():
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return {'success': True, 'value': 0}
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@node.route("/temp")
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def temp():
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temp = probe.predict_soil_temp()
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return {'success': True, 'value': temp}
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node.run(host='0.0.0.0', port='3333', use_reloader=False)
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