Merge pull request #57 from utmgdsc/feature/recommendation-messages

Recommendation Messages

backend/models/energy.py

@@ -3,6 +3,7 @@
 """
 
 from __future__ import annotations
+import random
 from typing import Union
 import json
 from datetime import datetime
@@ -22,7 +23,6 @@ class EnergyEntry(CARBON_MODEL):
     electricity: int  # measured in kWh
     province: str
     household: int
-    metric_threshold = 200/3
 
     def __init__(self, oid: ObjectId, user_id: ObjectId, carbon_emissions: int, date: Union[str, datetime],
                  heating_oil: int, natural_gas: int, province: str, household: int, electricity: int) -> None:
@@ -127,10 +127,10 @@ def from_json(doc: json) -> EnergyEntryRecommendation:
 
     @staticmethod
     def from_energy_entry(energy_entry: EnergyEntry) -> EnergyEntryRecommendation:
-        submetric_threshold = EnergyEntry.metric_threshold/3
-        heating_oil_recommendation = "Heating oil emissions look good!"
-        natural_gas_recommendation = "Natural gas emissions look good!"
-        electricity_recommendation = "Electricity emissions look good!"
+        submetric_threshold = 11
+        heating_oil_recommendation = "Looking good!"
+        natural_gas_recommendation = "Looking good!"
+        electricity_recommendation = "Looking good!"
 
         heating_oil_carbon_emissions = energy_entry.heating_oil * 2.753
         natural_gas_carbon_emissions = energy_entry.natural_gas * 1.96
@@ -166,13 +166,25 @@ def from_energy_entry(energy_entry: EnergyEntry) -> EnergyEntryRecommendation:
             electricity_carbon_emissions = (energy_entry.electricity * 0.84) / energy_entry.household
 
         if heating_oil_carbon_emissions > submetric_threshold:
-            heating_oil_recommendation = "Heating oil emissions too high"
+            recommendation1 = "Be conservative by opting to dress up/down instead of turning on the AC/heater."
+            recommendation2 = "Consider investing in ENERGY STAR certified products"
+            recommendation3 = "Consider improving your home insulation, if applicable"
+            recommendations = [recommendation1, recommendation2, recommendation3]
+            heating_oil_recommendation = random.choice(recommendations)
 
         if natural_gas_carbon_emissions > submetric_threshold:
-            natural_gas_recommendation = "Natural gas emissions too high"
+            recommendation1 = "Consider replacing natural gas heating systems with electric alternatives"
+            recommendation2 = "Consider investing in ENERGY STAR certified products"
+            recommendation3 = "Consider improving your home insulation, if applicable"
+            recommendations = [recommendation1, recommendation2, recommendation3]
+            natural_gas_recommendation = random.choice(recommendations)
 
         if electricity_carbon_emissions > submetric_threshold:
-            electricity_recommendation = "Electricity emissions too high"
+            recommendation1 = "Avoid phantom power by unplugging devices you are not actually using (ex.chargers, toasters, etc.)"
+            recommendation2 = "Consider investing in ENERGY STAR certified products"
+            recommendation3 = "Be conservative by opting to dress up/down instead of turning on the AC/heater"
+            recommendations = [recommendation1, recommendation2, recommendation3]
+            electricity_recommendation = random.choice(recommendations)
 
         return EnergyEntryRecommendation(heating_oil_recommendation, natural_gas_recommendation, electricity_recommendation)
 

backend/models/food.py

@@ -3,6 +3,7 @@
 """
 
 from __future__ import annotations
+import random
 from typing import Union
 import json
 from datetime import datetime
@@ -25,7 +26,7 @@ class FoodEntry(CARBON_MODEL):
     cheese: int
     milk: int
     food_waste: int
-    metric_threshold = 200 / 3
+
 
     # food measurements in # of 100g servings
     def __init__(self, oid: ObjectId, user_id: ObjectId, carbon_emissions: int, date: Union[str, datetime],
@@ -75,14 +76,14 @@ def from_json(doc: json) -> FoodEntry:
         )
 
     def calculate_carbon_emissions(self) -> float:
-        beef_carbon_emissions = self.beef * 15.5
-        lamb_carbon_emissions = self.lamb * 5.84
-        pork_carbon_emissions = self.pork * 2.4
-        chicken_carbon_emissions = self.chicken * 1.8
-        fish_carbon_emissions = self.fish * 1.8
-        cheese_carbon_emissions = self.cheese * 2.79
+        beef_carbon_emissions = self.beef * 9.9
+        lamb_carbon_emissions = self.lamb * 3.9
+        pork_carbon_emissions = self.pork * 1.2
+        chicken_carbon_emissions = self.chicken * 0.98
+        fish_carbon_emissions = self.fish * 1.3
+        cheese_carbon_emissions = self.cheese * 2.3
         milk_carbon_emissions = self.milk * 0.8
-        food_waste_carbon_emissions = self.food_waste * 0.25
+        food_waste_carbon_emissions = self.food_waste * 0.0025
         return sum([beef_carbon_emissions, lamb_carbon_emissions, pork_carbon_emissions,
                     chicken_carbon_emissions, fish_carbon_emissions, cheese_carbon_emissions,
                     milk_carbon_emissions, food_waste_carbon_emissions])
@@ -91,6 +92,7 @@ def __repr__(self) -> str:
         return f'Food ID: {self.oid.__str__()}'
 
 
+
 class FoodEntryRecommendation(DB_MODEL):
     beef_recommendation: str
     lamb_recommendation: str
@@ -142,48 +144,81 @@ def from_json(doc: json) -> FoodEntryRecommendation:
 
     @staticmethod
     def from_food_entry(food_entry: FoodEntry) -> FoodEntryRecommendation:
-        submetric_threshold = FoodEntry.metric_threshold / 8
-        beef_recommendation = "Beef emissions look good!"
-        lamb_recommendation = "Lamb emissions look good!"
-        pork_recommendation = "Pork emissions look good!"
-        chicken_recommendation = "Chicken emissions look good!"
-        fish_recommendation = "Fish emissions look good!"
-        cheese_recommendation = "Cheese emissions look good!"
-        milk_recommendation = "Milk emissions look good!"
-        food_waste_recommendation = "Food waste emissions look good!"
-
-        beef_carbon_emissions = food_entry.beef * 15.5
-        lamb_carbon_emissions = food_entry.lamb * 5.84
-        pork_carbon_emissions = food_entry.pork * 2.4
-        chicken_carbon_emissions = food_entry.chicken * 1.8
-        fish_carbon_emissions = food_entry.fish * 1.8
-        cheese_carbon_emissions = food_entry.cheese * 2.79
+        submetric_threshold = 11
+        beef_recommendation = "Looking good!"
+        lamb_recommendation = "Looking good!"
+        pork_recommendation = "Looking good!"
+        chicken_recommendation = "Looking good!"
+        fish_recommendation = "Looking good!"
+        cheese_recommendation = "Looking good!"
+        milk_recommendation = "Looking good!"
+        food_waste_recommendation = "Looking good!"
+
+        beef_carbon_emissions = food_entry.beef * 9.9
+        lamb_carbon_emissions = food_entry.lamb * 3.9
+        pork_carbon_emissions = food_entry.pork * 1.2
+        chicken_carbon_emissions = food_entry.chicken * 0.98
+        fish_carbon_emissions = food_entry.fish * 1.3
+        cheese_carbon_emissions = food_entry.cheese * 2.3
+
         milk_carbon_emissions = food_entry.milk * 0.8
-        food_waste_carbon_emissions = food_entry.food_waste * 0.25
+        food_waste_carbon_emissions = food_entry.food_waste * 0.0025
 
         if beef_carbon_emissions > submetric_threshold:
-            beef_recommendation = "Beef emissions too high"
+            recommendation1 = "Try opting for white meat (fish, chicken, etc.)"
+            recommendation2 = "Consider alternate forms of protein (chicken, fish, eggs, etc.)"
+            recommendation3 = "Try opting for low-impact plant protein sources like peas, legumes and tofu"
+            recommendations = [recommendation1, recommendation2, recommendation3]
+            beef_recommendation = random.choice(recommendations)
 
         if lamb_carbon_emissions > submetric_threshold:
-            lamb_recommendation = "Lamb emissions too high"
-
+            recommendation1 = "Try opting for white meat (fish, chicken, etc.)"
+            recommendation2 = "Consider alternate forms of protein (chicken, fish, eggs, etc.)"
+            recommendation3 = "Try opting for low-impact plant protein sources like peas, legumes and tofu"
+            recommendations = [recommendation1, recommendation2, recommendation3]
+            lamb_recommendation = random.choice(recommendations)
+
         if pork_carbon_emissions > submetric_threshold:
-            pork_recommendation = "Pork emissions too high"
+            recommendation1 = "Try opting for white meat (fish, chicken, etc.)"
+            recommendation2 = "Consider alternate forms of protein (chicken, fish, eggs, etc.)"
+            recommendation3 = "Try opting for low-impact plant protein sources like peas, legumes and tofu"
+            recommendations = [recommendation1, recommendation2, recommendation3]
+            pork_recommendation = random.choice(recommendations)
 
         if chicken_carbon_emissions > submetric_threshold:
-            chicken_recommendation = "Chicken emissions too high"
+            recommendation1 = "Try opting for low-impact plant protein sources (peas, legumes and tofu)"
+            recommendation2 = "Consider alternate forms of protein (eggs, whey, etc.)"
+            recommendation3 = "Consider opting for seitan"
+            recommendations = [recommendation1, recommendation2, recommendation3]
+            chicken_recommendation = random.choice(recommendations)
 
         if fish_carbon_emissions > submetric_threshold:
-            fish_recommendation = "Fish emissions too high"
+            recommendation1 = "Consider alternate forms of protein (chicken, eggs, whey, etc.)"
+            recommendation2 = "Try opting for low-impact plant protein sources (peas, legumes and tofu)"
+            recommendation3 = ""
+            recommendations = [recommendation1, recommendation2, recommendation3]
+            fish_recommendation = random.choice(recommendations)
 
         if cheese_carbon_emissions > submetric_threshold:
-            cheese_recommendation = "Cheese emissions too high"
+            recommendation1 = "Consider alternatives to cheese spreads (hummus, guacamole, etc.)"
+            recommendation2 = "Consider plant-based cheeses (made from nuts, soy, or tapioca)"
+            recommendation3 = "Consider alternatives to cheese (tofu, tempeh, etc.)"
+            recommendations = [recommendation1, recommendation2, recommendation3]
+            cheese_recommendation = random.choice(recommendations)
 
         if milk_carbon_emissions > submetric_threshold:
-            milk_recommendation = "Milk emissions too high"
+            recommendation1 = "Consider opting for almond milk"
+            recommendation2 = "Consider opting for soy milk"
+            recommendation3 = "Consider opting for oat milk"
+            recommendations = [recommendation1, recommendation2, recommendation3]
+            milk_recommendation = random.choice(recommendations)
 
         if food_waste_carbon_emissions > submetric_threshold:
-            food_waste_recommendation = "Food waste emissions too high"
+            recommendation1 = "Meal planning! Create a realistic shopping list so you only buy what you need"
+            recommendation2 = "Carefully assess the expiry date of all food items that you purchase"
+            recommendation3 = "First in First out (FIFO)! Use older ingredients first before they expire."
+            recommendations = [recommendation1, recommendation2, recommendation3]
+            food_waste_recommendation = random.choice(recommendations)
 
         return FoodEntryRecommendation(beef_recommendation, lamb_recommendation, pork_recommendation, chicken_recommendation,
                                        fish_recommendation, cheese_recommendation, milk_recommendation, food_waste_recommendation)

backend/models/transportation.py

@@ -5,6 +5,7 @@
 from __future__ import annotations
 from typing import Union
 import json
+import random
 from datetime import datetime
 from models.abstract_carbon_model import CARBON_MODEL
 from models.abstract_db_model import DB_MODEL
@@ -21,8 +22,7 @@ class TransportationEntry(CARBON_MODEL):
     motorbike: int
     electric_car: int
     gasoline_car: int
-    fuel_efficiency: float
-    metric_threshold = 200 / 3
+    fuel_efficieny: float
 
     def __init__(self, oid: ObjectId, user_id: ObjectId, carbon_emissions: int, date: Union[str, datetime],
                  bus: int, train: int, motorbike: int, electric_car: int, gasoline_car: int, fuel_efficiency: float) -> None:
@@ -82,7 +82,6 @@ class TransportationEntryRecommendation(DB_MODEL):
     motorbike_recommendation: str
     electric_car_recommendation: str
     gasoline_car_recommendation: str
-    metric_threshold: int
 
     def __init__(self, bus_recommendation: str, train_recommendation: str, motorbike_recommendation: str,
                  electric_car_recommendation: str, gasoline_car_recommendation: str) -> None:
@@ -114,12 +113,12 @@ def from_json(doc: json) -> TransportationEntryRecommendation:
 
     @staticmethod
     def from_transportation_entry(transportation_entry: TransportationEntry) -> TransportationEntryRecommendation:
-        submetric_threshold = TransportationEntry.metric_threshold / 5
-        bus_recommendation = "Bus emissions look good!"
-        train_recommendation = "Train emissions look good!"
-        motorbike_recommendation = "Motorbike emissions look good!"
-        electric_car_recommendation = "Electric car emissions look good!"
-        gasoline_car_recommendation = "Gasoline emissions look good!"
+        submetric_threshold = 11
+        bus_recommendation = "Looking good!"
+        train_recommendation = "Looking good!"
+        motorbike_recommendation = "Looking good!"
+        electric_car_recommendation = "Looking good!"
+        gasoline_car_recommendation = "Looking good!"
 
         bus_carbon_emissions = transportation_entry.bus * 0.103
         train_carbon_emissions = transportation_entry.train * 0.037
@@ -128,19 +127,39 @@ def from_transportation_entry(transportation_entry: TransportationEntry) -> Tran
         gasoline_car_carbon_emissions = ((transportation_entry.fuel_efficiency * 2.3) / 100) * transportation_entry.gasoline_car
 
         if bus_carbon_emissions > submetric_threshold:
-            bus_recommendation = "Bus emissions too high"
+            recommendation1 = "Consider walking short distances"
+            recommendation2 = "Consider riding an E-Bike or E-scooter when possible"
+            recommendation3 = "Consider riding a bicycle when travelling short distances"
+            recommendations = [recommendation1, recommendation2, recommendation3]
+            bus_recommendation = random.choice(recommendations)
 
         if train_carbon_emissions > submetric_threshold:
-            train_recommendation = "Train emissions too high"
+            recommendation1 = "Consider walking short distances"
+            recommendation2 = "Consider local public transport when possible (buses, trams)"
+            recommendation3 = "Consider riding a bicycle, E-Bike or E-scooter when travelling short distances"
+            recommendations = [recommendation1, recommendation2, recommendation3]
+            train_recommendation = random.choice(recommendations)
 
         if motorbike_carbon_emissions > submetric_threshold:
-            motorbike_recommendation = "Motorbike emissions too high"
+            recommendation1 = "Consider walking or biking short distances"
+            recommendation2 = "Consider taking public transportation more often"
+            recommendation3 = "Consider carpooling when possible"
+            recommendations = [recommendation1, recommendation2, recommendation3]
+            motorbike_recommendation = random.choice(recommendations)
 
         if electric_car_carbon_emissions > submetric_threshold:
-            electric_car_recommendation = "Electric car emissions too high"
+            recommendation1 = "Consider taking public transportation more often"
+            recommendation2 = "Consider carpooling when possible"
+            recommendation3 = "Consider walking or biking short distances"
+            recommendations = [recommendation1, recommendation2, recommendation3]
+            electric_car_recommendation = random.choice(recommendations)
 
         if gasoline_car_carbon_emissions > submetric_threshold:
-            gasoline_car_recommendation = "Gasoline car emissions too high"
+            recommendation1 = "Consider taking public transportation more often"
+            recommendation2 = "Consider carpooling when possible"
+            recommendation3 = "Consider walking or biking short distances"
+            recommendations = [recommendation1, recommendation2, recommendation3]
+            gasoline_car_recommendation = random.choice(recommendations)
 
         return TransportationEntryRecommendation(bus_recommendation, train_recommendation,
                                                  motorbike_recommendation, electric_car_recommendation, gasoline_car_recommendation)