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2022-052
Commit bf7489ed authored by Thejan Ganepola's avatar Thejan Ganepola
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Backend

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Backend/Heal.postman_collection-v2 0 → 100644
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{
"info": {
"_postman_id": "6b463409-1886-4304-a952-a34fc0a31bdc",
"name": "Heal",
"schema": "https://schema.getpostman.com/json/collection/v2.1.0/collection.json"
},
"item": [
{
"name": "LOCAL",
"item": [
{
"name": "risk",
"request": {
"method": "GET",
"header": [],
"url": {
"raw": "http://127.0.0.1:5000/risk",
"protocol": "http",
"host": [
"127",
"0",
"0",
"1"
],
"port": "5000",
"path": [
"risk"
]
}
},
"response": []
},
{
"name": "diet_workout",
"request": {
"method": "GET",
"header": [],
"url": {
"raw": "http://127.0.0.1:5000/diwo",
"protocol": "http",
"host": [
"127",
"0",
"0",
"1"
],
"port": "5000",
"path": [
"diwo"
]
}
},
"response": []
},
{
"name": "pharmacy",
"request": {
"method": "POST",
"header": [],
"body": {
"mode": "raw",
"raw": "{\r\n\"user_location\" : \"7.09408649773395,79.99345377711329\",\r\n\"medicine_list\" : \"salbutamol,theophylline\"\r\n}",
"options": {
"raw": {
"language": "json"
}
}
},
"url": {
"raw": "http://127.0.0.1:5000/pharmacy",
"protocol": "http",
"host": [
"127",
"0",
"0",
"1"
],
"port": "5000",
"path": [
"pharmacy"
]
}
},
"response": []
}
]
},
{
"name": "AWS",
"item": [
{
"name": "risk",
"request": {
"method": "GET",
"header": [],
"url": {
"raw": "ec2-54-242-87-59.compute-1.amazonaws.com:5003/risk",
"host": [
"ec2-54-242-87-59",
"compute-1",
"amazonaws",
"com"
],
"port": "5003",
"path": [
"risk"
]
}
},
"response": []
},
{
"name": "diet_workout",
"request": {
"method": "GET",
"header": [],
"url": {
"raw": "ec2-54-242-87-59.compute-1.amazonaws.com:5003/diwo",
"host": [
"ec2-54-242-87-59",
"compute-1",
"amazonaws",
"com"
],
"port": "5003",
"path": [
"diwo"
]
}
},
"response": []
},
{
"name": "pharmacy",
"request": {
"method": "POST",
"header": [],
"body": {
"mode": "raw",
"raw": "{\r\n\"user_location\" : \"7.09408649773395,79.99345377711329\",\r\n\"medicine_list\" : \"salbutamol,theophylline\"\r\n}",
"options": {
"raw": {
"language": "json"
}
}
},
"url": {
"raw": "ec2-54-242-87-59.compute-1.amazonaws.com:5003/pharmacy",
"host": [
"ec2-54-242-87-59",
"compute-1",
"amazonaws",
"com"
],
"port": "5003",
"path": [
"pharmacy"
]
}
},
"response": []
}
]
}
]
}
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Backend/dev/dev/data/diet_workout_dataset.csv 0 → 100644
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Backend/dev/dev/data/heart_dataset.csv 0 → 100644
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Backend/dev/dev/data/pharmacy_locs.json 0 → 100644
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[
{
"name":"Anura pharmacy",
"location":"7.0940864977339615,79.99345377711329",
"medicine":"salbutamol,theophylline,terbutaline"
},
{
"name":"Venura pharmacy",
"location":"7.0940864977339615,79.99345377711329",
"medicine":"terbutaline,bisoprolol,propanolol,diltiazem"
},
{
"name":"Onel pharmacy",
"location":"6.924009203281871,79.97785832551634",
"medicine":"salbutamol,theophylline,bisoprolol,diltiazem"
},
{
"name":"Pharmart pharmacy",
"location":"6.9170765501095826,79.9731878634781",
"medicine":"paracetamol,brufen,cetrizine"
},
{
"name":"Sahana Suwa pharmacy",
"location":"6.909795166009974,79.9712794543516",
"medicine":"arorva,deriphyllin retard,ibuprofen"
},
{
"name":"Excel Chemist pharmacy",
"location":"6.909111411712752,79.96812888133942",
"medicine":"acetaminophen,panadol,celox r"
},
{
"name":"Lanka Hospitals pharmacy",
"location":"6.890199838379547,79.8755232678455",
"medicine":"beta-blockers,metoprolol,atenolol"
},
{
"name":"Safeway Chemist pharmacy",
"location":"6.900765273496725,79.95812885160541",
"medicine":"diazepam,amoxicillin,atenolol"
},
{
"name":"Osulka pharmacy",
"location":"7.065439421205347,80.00579199852889",
"medicine":"lyrica,metoprolol,codein"
},
{
"name":"Med-X pharmacy",
"location":"6.905658609086574,79.96319536969301",
"medicine":"azithromycin,alprazolam,lorazepam"
},
{
"name":"Slim pharma pharmacy",
"location":"6.904108520002969,79.97499582551622",
"medicine":"asprin,morphine,losartan,insulin"
},
{
"name":"Uni Chemist pharmacy",
"location":"6.876906714846124,79.93725064085758",
"medicine":"omeprazole,metoprolol,epinephrine"
},
{
"name":"Liberty pharmacy",
"location":"6.911167058228385,79.85208210037594",
"medicine":"salbutamol,paracetomol,theophylline,terbutaline"
},
{
"name":"Harcourts pharmacy",
"location":"6.917029157743831,79.86644722366874",
"medicine":"bisoprolol,propranolol,paracetomol,theophylline"
},
{
"name":"Suwahasa pharmacy",
"location":"6.897430094321646, 79.92642060253203",
"medicine":"metoprolol,albuterol,diltiazem,brufen"
},
{
"name":"Health guard pharmacy",
"location":"6.915351746143819,79.85908343901018",
"medicine":"paracetamol,brufen,theophylline,amoxicillin"
},
{
"name":"Central pharmacy & store",
"location":"6.916017661229803,79.87667135250412",
"medicine":"atenolol,codein,metformin,januvia"
},
{
"name":"Sarasi pharmacy",
"location":"6.97766771303644,79.92491054974622",
"medicine":"trazodone,paracetamol,theophylline,lyrica"
},
{
"name":"Himaya pharmacy",
"location":"6.8954949709475555,79.96076454085762",
"medicine":"ceftriaxone,clarithromycin,erythromycin,cefclor"
},
{
"name":"Health Chemist pharmacy",
"location":"6.903887744124613,79.93547647320233",
"medicine":"salbutamol,terbutaline,diltiazem,bisprolol"
},
{
"name":"Rokem pharmacy",
"location":"6.903580379800135,79.95527011017482",
"medicine":"brufen,trazodone,paracetamol,lyrica"
},
{
"name":"New Koswatte chemist pharmacy",
"location":"6.906252512873709,79.92877771202234",
"medicine":"bisoprolol,propranolol,terbutaline,diltiazem"
},
{
"name":"Isuru pharmacy",
"location":"6.902048508051585,79.91718599852838",
"medicine":"clarithromycin,erythromycin,cefclor,diltiazem"
},
{
"name":"CFC Healthcare Pharmacy Battaramulla ",
"location":"6.896750209485456,79.92250838318691",
"medicine":"paracetamol,theophylline,atenolol,codein"
},
{
"name":"Pelawatte pharmacy",
"location":"6.891533775472255,79.9284086831869",
"medicine":"metoprolol,albuterol,diltiazem,brufen"
},
{
"name":"S.A.K pharmacy",
"location":"6.966268386952313,79.90001644085787",
"medicine":"salbutamol,paracetomol,theophylline,diltiazem"
},
{
"name":"Yukee pharmacy",
"location":"7.231367395308464,80.24504313901123",
"medicine":"bisoprolol,propranolol,paracetomol,beta-blockers"
},
{
"name":" Losetha pharmacy",
"location":"6.966355838731583,79.9275027255164",
"medicine":"atenolol,beta-blockers,salbutamol,paracetamol"
},
{
"name":"Lanka pharmacy",
"location":"7.2111603452394055, 79.8401644866482",
"medicine":"beta-blockers,metoprolol,atenolol,paracetamol"
},
{
"name":"Nawaloka care pharmacy",
"location":"7.207141215951129, 79.84963235407916",
"medicine":"salbutamol,theophylline,bisoprolol,diltiazem"
}
]
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Backend/dev/dev/diet_workout_prediction.ipynb 0 → 100644
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Backend/dev/dev/pharmacy_recommendation.ipynb 0 → 100644
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Backend/dev/dev/risk_prediction.ipynb 0 → 100644
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Backend/prod/prod/app.py 0 → 100644
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from inference import *
from flask_cors import CORS
from flask import Flask, Response, request
app = Flask(__name__)
CORS(app)
@app.route('/risk', methods=['GET'])
def risk():
risk_level, HeartRateArr, BloodOxygenLevelArr, BodyTemperatureArr = risk_level_prediction()
return Response(
response=json.dumps({
"status": "success",
"HeartRate": HeartRateArr,
"BloodOxygenLevel": BloodOxygenLevelArr,
"BodyTemperature": BodyTemperatureArr,
"RiskLevel": f"{risk_level}"
}),
status=200,
mimetype="application/json"
)
@app.route('/diwo', methods=['GET'])
def diwo():
exercise_plan, diet_plan, HeartRateArr, BloodOxygenLevelArr, BodyTemperatureArr = diet_workout_prediction()
return Response(
response=json.dumps({
"status": "success",
"HeartRate": HeartRateArr,
"BloodOxygenLevel": BloodOxygenLevelArr,
"BodyTemperature": BodyTemperatureArr,
"ExercisePlan": f"{exercise_plan}",
"DietPlan": f"{diet_plan}"
}),
status=200,
mimetype="application/json"
)
@app.route('/pharmacy', methods=['POST'])
def pharmacy():
'''
{
"user_location" : "7.09408649773395,79.99345377711329",
"medicine_list" : "salbutamol,theophylline"
}
'''
data = request.get_json()
user_location = data['user_location']
medicine_list = data['medicine_list']
rec_list = pharmacy_recommendation(user_location, medicine_list)
return Response(
response=json.dumps({
"status": "success",
"pharmacy_recommendation": rec_list
}),
status=200,
mimetype="application/json"
)
if __name__ == "__main__":
app.run(
debug=True,
host='0.0.0.0',
port=5000,
threaded=False,
use_reloader=True
)
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Backend/prod/prod/data/health_data.csv 0 → 100644
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Backend/prod/prod/data/pharmacy_locs.json 0 → 100644
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[
{
"name":"Anura pharmacy",
"location":"7.0940864977339615,79.99345377711329",
"medicine":"salbutamol,theophylline,terbutaline"
},
{
"name":"Venura pharmacy",
"location":"7.0940864977339615,79.99345377711329",
"medicine":"terbutaline,bisoprolol,propanolol,diltiazem"
},
{
"name":"Onel pharmacy",
"location":"6.924009203281871,79.97785832551634",
"medicine":"salbutamol,theophylline,bisoprolol,diltiazem"
},
{
"name":"Pharmart pharmacy",
"location":"6.9170765501095826,79.9731878634781",
"medicine":"paracetamol,brufen,cetrizine"
},
{
"name":"Sahana Suwa pharmacy",
"location":"6.909795166009974,79.9712794543516",
"medicine":"arorva,deriphyllin retard,ibuprofen"
},
{
"name":"Excel Chemist pharmacy",
"location":"6.909111411712752,79.96812888133942",
"medicine":"acetaminophen,panadol,celox r"
},
{
"name":"Lanka Hospitals pharmacy",
"location":"6.890199838379547,79.8755232678455",
"medicine":"beta-blockers,metoprolol,atenolol"
},
{
"name":"Safeway Chemist pharmacy",
"location":"6.900765273496725,79.95812885160541",
"medicine":"diazepam,amoxicillin,atenolol"
},
{
"name":"Osulka pharmacy",
"location":"7.065439421205347,80.00579199852889",
"medicine":"lyrica,metoprolol,codein"
},
{
"name":"Med-X pharmacy",
"location":"6.905658609086574,79.96319536969301",
"medicine":"azithromycin,alprazolam,lorazepam"
},
{
"name":"Slim pharma pharmacy",
"location":"6.904108520002969,79.97499582551622",
"medicine":"asprin,morphine,losartan,insulin"
},
{
"name":"Uni Chemist pharmacy",
"location":"6.876906714846124,79.93725064085758",
"medicine":"omeprazole,metoprolol,epinephrine"
},
{
"name":"Liberty pharmacy",
"location":"6.911167058228385,79.85208210037594",
"medicine":"salbutamol,paracetomol,theophylline,terbutaline"
},
{
"name":"Harcourts pharmacy",
"location":"6.917029157743831,79.86644722366874",
"medicine":"bisoprolol,propranolol,paracetomol,theophylline"
},
{
"name":"Suwahasa pharmacy",
"location":"6.897430094321646, 79.92642060253203",
"medicine":"metoprolol,albuterol,diltiazem,brufen"
},
{
"name":"Health guard pharmacy",
"location":"6.915351746143819,79.85908343901018",
"medicine":"paracetamol,brufen,theophylline,amoxicillin"
},
{
"name":"Central pharmacy & store",
"location":"6.916017661229803,79.87667135250412",
"medicine":"atenolol,codein,metformin,januvia"
},
{
"name":"Sarasi pharmacy",
"location":"6.97766771303644,79.92491054974622",
"medicine":"trazodone,paracetamol,theophylline,lyrica"
},
{
"name":"Himaya pharmacy",
"location":"6.8954949709475555,79.96076454085762",
"medicine":"ceftriaxone,clarithromycin,erythromycin,cefclor"
},
{
"name":"Health Chemist pharmacy",
"location":"6.903887744124613,79.93547647320233",
"medicine":"salbutamol,terbutaline,diltiazem,bisprolol"
},
{
"name":"Rokem pharmacy",
"location":"6.903580379800135,79.95527011017482",
"medicine":"brufen,trazodone,paracetamol,lyrica"
},
{
"name":"New Koswatte chemist pharmacy",
"location":"6.906252512873709,79.92877771202234",
"medicine":"bisoprolol,propranolol,terbutaline,diltiazem"
},
{
"name":"Isuru pharmacy",
"location":"6.902048508051585,79.91718599852838",
"medicine":"clarithromycin,erythromycin,cefclor,diltiazem"
},
{
"name":"CFC Healthcare Pharmacy Battaramulla ",
"location":"6.896750209485456,79.92250838318691",
"medicine":"paracetamol,theophylline,atenolol,codein"
},
{
"name":"Pelawatte pharmacy",
"location":"6.891533775472255,79.9284086831869",
"medicine":"metoprolol,albuterol,diltiazem,brufen"
},
{
"name":"S.A.K pharmacy",
"location":"6.966268386952313,79.90001644085787",
"medicine":"salbutamol,paracetomol,theophylline,diltiazem"
},
{
"name":"Yukee pharmacy",
"location":"7.231367395308464,80.24504313901123",
"medicine":"bisoprolol,propranolol,paracetomol,beta-blockers"
},
{
"name":" Losetha pharmacy",
"location":"6.966355838731583,79.9275027255164",
"medicine":"atenolol,beta-blockers,salbutamol,paracetamol"
},
{
"name":"Lanka pharmacy",
"location":"7.2111603452394055, 79.8401644866482",
"medicine":"beta-blockers,metoprolol,atenolol,paracetamol"
},
{
"name":"Nawaloka care pharmacy",
"location":"7.207141215951129, 79.84963235407916",
"medicine":"salbutamol,theophylline,bisoprolol,diltiazem"
}
]
\ No newline at end of file
Backend/prod/prod/inference.py 0 → 100644
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import json
import pickle
import numpy as np
import pandas as pd
import tensorflow as tf
from pymongo import MongoClient
from math import sin, cos, sqrt, atan2, radians, asin
try:
client = MongoClient("mongodb+srv://admin:admin@early-detection.jevtz.mongodb.net/test")
db = client["Heal"]
client.server_info()
print("DB accessed !")
except Exception as e:
print("*************************************************************")
print(e)
print("*************************************************************")
##################################### PARAMS #########################################
risk_level_mapping = {
0 : "situation doesn't exist",
1 : "Manageable",
2 : "Normal Healthy",
3 : "Risky"
}
excercise_plan_mapping = {
0: 'No exercises',
1: 'sleep,Rest, Yoga',
2: 'jogging, walking, Running',
3: 'swimming, Breathing exercises'
}
diet_plan_mapping = {
0: 'No food plan',
1: 'Low cholesterol food',
2: 'Balance Diet which contain all the nutrients',
3: 'Hot Beverages & Meals Food rich in potassium'
}
##################################### LOAD MODELS #########################################
risk_model = tf.keras.models.load_model('weights/risk_prediction.h5')
risk_model.compile(
optimizer='adam',
loss='categorical_crossentropy',
metrics=[
tf.keras.metrics.CategoricalAccuracy(),
tf.keras.metrics.Precision(),
tf.keras.metrics.Recall(),
tf.keras.metrics.AUC()
])
with open('weights/scalar - risk_prediction.pkl', 'rb') as f:
risk_scalar = pickle.load(f)
diet_workout_model = tf.keras.models.load_model('weights/diet_workout_prediction.h5')
diet_workout_model.compile(
loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy']
)
with open('weights/scalar - diet_workout_prediction.pkl', 'rb') as f:
diet_workout_scalar = pickle.load(f)
##################################### PREDICTION #########################################
def risk_level_prediction():
health_collection = db["health"]
health_data = pd.DataFrame(list(health_collection.find()))
sample_data = health_data[-50:][["HeartRate","BloodOxygenLevel","BodyTemperature"]]
HeartRateArr = sample_data["HeartRate"].values.tolist()
BloodOxygenLevelArr = sample_data["BloodOxygenLevel"].values.tolist()
BodyTemperatureArr = sample_data["BodyTemperature"].values.tolist()
Xinf = sample_data.values
Xinf = risk_scalar.transform(Xinf)
Xinf = Xinf.reshape(1, 50, 3)
risk_level = risk_model.predict(Xinf).squeeze()
risk_level = np.argmax(risk_level)
risk_level = risk_level_mapping[risk_level]
return risk_level, HeartRateArr, BloodOxygenLevelArr, BodyTemperatureArr
def diet_workout_prediction():
health_collection = db["health"]
health_data = pd.DataFrame(list(health_collection.find()))
sample_data = health_data[-50:][["HeartRate","BloodOxygenLevel","BodyTemperature"]]
HeartRateArr = sample_data["HeartRate"].values.tolist()
BloodOxygenLevelArr = sample_data["BloodOxygenLevel"].values.tolist()
BodyTemperatureArr = sample_data["BodyTemperature"].values.tolist()
Xinf = sample_data.values
Xinf = diet_workout_scalar.transform(Xinf)
exercise_plan_idxs, diet_plan_idxs = diet_workout_model.predict(Xinf)
exercise_plan_idxs = exercise_plan_idxs.squeeze()
diet_plan_idxs = diet_plan_idxs.squeeze()
exercise_plan_idxs = np.argmax(exercise_plan_idxs, axis=1)
diet_plan_idxs = np.argmax(diet_plan_idxs, axis=1)
exercise_plans = [excercise_plan_mapping[idx] for idx in exercise_plan_idxs]
diet_plans = [diet_plan_mapping[idx] for idx in diet_plan_idxs]
mode_exercise_plan = max(set(exercise_plans), key=exercise_plans.count)
mode_diet_plan = max(set(diet_plans), key=diet_plans.count)
return mode_exercise_plan, mode_diet_plan, HeartRateArr, BloodOxygenLevelArr, BodyTemperatureArr
def haversine(p1, p2):
lat1, lon1 = p1
lat2, lon2 = p2
lat1 = float(lat1)
lon1 = float(lon1)
lat2 = float(lat2)
lon2 = float(lon2)
# convert decimal degrees to radians
lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
# haversine formula
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
c = 2 * asin(sqrt(a))
r = 6371 # Radius of earth in kilometers. Use 3956 for miles. Determines return value units.
distance_km = c * r
return distance_km
def pharmacy_recommendation(user_location, medicine_list):
user_location = user_location.split(',')
user_location = (float(user_location[0]), float(user_location[1]))
medicine_list = medicine_list.split(',')
medicine_list = [medicine.strip() for medicine in medicine_list]
pharmacy_collection = db["pharmacy"]
df_pharmacy = pd.DataFrame(list(pharmacy_collection.find()))
df_pharmacy[['latitude', 'longitude']] = df_pharmacy['location'].str.split(',', 1, expand=True)
del df_pharmacy['location']
df_pharmacy['latitude'] = df_pharmacy['latitude'].str.strip()
df_pharmacy['longitude'] = df_pharmacy['longitude'].str.strip()
df_pharmacy['latitude'] = df_pharmacy['latitude'].astype(float)
df_pharmacy['longitude'] = df_pharmacy['longitude'].astype(float)
df_pharmacy["medicine"] = df_pharmacy["medicine"].str.split(',')
df_pharmacy = df_pharmacy.loc[df_pharmacy['medicine'].apply(lambda x: set(medicine_list).issubset([m.strip() for m in x]))]
df_pharmacy['distance'] = df_pharmacy.apply(lambda row: haversine(user_location, (row['latitude'], row['longitude'])), axis=1)
df_pharmacy.sort_values(by=['distance'], inplace=True)
df_pharmacy = df_pharmacy.head(5)
df_pharmacy = df_pharmacy[['name', 'distance', 'medicine']]
df_pharmacy['distance'] = df_pharmacy['distance'].apply(lambda x: round(x, 2))
rec_list = []
for _, row in df_pharmacy.iterrows():
rec_json = {}
rec_json["name"] = f"{row['name']}"
rec_json["distance"] = f"{row['distance']} km"
rec_json["available medicines"] = f"{row['medicine']}"
rec_list.append(rec_json)
return rec_list
\ No newline at end of file
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