def task1(location, search_string):
"""
:param location: location around which the restaurants are required
:param search_string: keyword searched, e.g., coffee, thai, indian
:return: 5 restaurants or cafes ordered by distance from the place
If the location returns multiple results, the location closest to the ip of the search device is used.
If a unique location can't be found in such a situation, a regions based results can be returned.
"""
factual = Factual(key=key, secret=secret)
# get the nearest location
nearest_location = find_smart_location(factual, location)
if 'Error' in nearest_location:
return Response(json.dumps(
{'Error': 'No location of that name or postcode found. '
'Please check the location or postcode name and try again.'}
),
mimetype='application/json'
), 400
# form the initial query based on geo location
query = form_geo_query(factual, lat=nearest_location['latitude'], lon=nearest_location['longitude'])
# add the filters
query = query.search('{}'.format(search_string)).filters({"category_ids": {"$includes": 338}})
data = query.data()
if len(data):
return Response(json.dumps(data), mimetype='application/json')
else:
return Response(json.dumps(
{'Error': 'The search returned no match. Please change the location or search string and try again.'}),
mimetype='application/json'
), 400
def task2(location, meal_of_the_day):
"""
:param location: location around which the restaurants are looked up
:param meal_of_the_day: the meal of the day, i.e., lunch, dinner, breakfast etc.
:return: json body with 5 restaurants that meet the criteria
# we can take many approaches to match the restaurants:
# 1. We can take a attributes based approach, i.e., likes, dislikes, requirements of all individuals
# 2. We can take an individual based approach, i.e., each person's likes, dislikes, and requirements at a time
# 3. Possibly combine the weighted score of the first two approaches
I am demonstrating a hybrid approach where the personal likes are checked for three people
"""
factual = Factual(key=key, secret=secret)
individuals = request.get_json()
# get the nearest location
nearest_location = find_smart_location(factual, location)
if 'Error' in nearest_location:
return Response(json.dumps({
'Error':
'No location of that name or postcode found. '
'Please check the location or postcode name and try again.'
}),
mimetype='application/json'), 400
# initiate query with geo location search
query = form_geo_query(factual,
lat=nearest_location['latitude'],
lon=nearest_location['longitude'])
try:
data = get_restaurants(individuals, meal_of_the_day, query)
except:
return Response(json.dumps({
'Error':
'An error occurred. Please try using one of breakfast, lunch, or dinner'
}),
mimetype='application/json')
if len(data) > 4: # if at least 5 results are found, return
return Response(json.dumps(data), mimetype='application/json')
return Response(json.dumps(
{'Error': 'No matching restaurant found. Please try again.'}),
mimetype='application/json'), 400
def task1(location, search_string):
"""
:param location: location around which the restaurants are required
:param search_string: keyword searched, e.g., coffee, thai, indian
:return: 5 restaurants or cafes ordered by distance from the place
If the location returns multiple results, the location closest to the ip of the search device is used.
If a unique location can't be found in such a situation, a regions based results can be returned.
"""
factual = Factual(key=key, secret=secret)
# get the nearest location
nearest_location = find_smart_location(factual, location)
if 'Error' in nearest_location:
return Response(json.dumps({
'Error':
'No location of that name or postcode found. '
'Please check the location or postcode name and try again.'
}),
mimetype='application/json'), 400
# form the initial query based on geo location
query = form_geo_query(factual,
lat=nearest_location['latitude'],
lon=nearest_location['longitude'])
# add the filters
query = query.search('{}'.format(search_string)).filters(
{"category_ids": {
"$includes": 338
}})
data = query.data()
if len(data):
return Response(json.dumps(data), mimetype='application/json')
else:
return Response(json.dumps({
'Error':
'The search returned no match. Please change the location or search string and try again.'
}),
mimetype='application/json'), 400
def task2(location, meal_of_the_day):
"""
:param location: location around which the restaurants are looked up
:param meal_of_the_day: the meal of the day, i.e., lunch, dinner, breakfast etc.
:return: json body with 5 restaurants that meet the criteria
# we can take many approaches to match the restaurants:
# 1. We can take a attributes based approach, i.e., likes, dislikes, requirements of all individuals
# 2. We can take an individual based approach, i.e., each person's likes, dislikes, and requirements at a time
# 3. Possibly combine the weighted score of the first two approaches
I am demonstrating a hybrid approach where the personal likes are checked for three people
"""
factual = Factual(key=key, secret=secret)
individuals = request.get_json()
# get the nearest location
nearest_location = find_smart_location(factual, location)
if 'Error' in nearest_location:
return Response(json.dumps(
{'Error': 'No location of that name or postcode found. '
'Please check the location or postcode name and try again.'}
),
mimetype='application/json'
), 400
# initiate query with geo location search
query = form_geo_query(factual, lat=nearest_location['latitude'], lon=nearest_location['longitude'])
try:
data = get_restaurants(individuals, meal_of_the_day, query)
except:
return Response(json.dumps(
{'Error': 'An error occurred. Please try using one of breakfast, lunch, or dinner'}
), mimetype='application/json'
)
if len(data) > 4: # if at least 5 results are found, return
return Response(json.dumps(data), mimetype='application/json')
return Response(json.dumps({'Error': 'No matching restaurant found. Please try again.'}),
mimetype='application/json'), 400