Pull request for team I'm thinqing

team_solutions/Im_ThinQing/Check_flights.py

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+import json
+import pandas as pd
+import http.client
+
+# parser = argparse.ArgumentParser()
+# parser.add_argument('-o','--origion',type=str)
+# parser.add_argument('-d','--destination',type=str)
+# args = parser.parse_args()
+
+
+def check_flights(origin,destination,departure_date,return_date):
+    conn = http.client.HTTPSConnection("skyscanner50.p.rapidapi.com")
+
+    headers = {
+        'X-RapidAPI-Key': "20f4b82842msh0a60cfb5f9bf13ep1dfe41jsnaa9a78013956",
+        'X-RapidAPI-Host': "skyscanner50.p.rapidapi.com"
+        }
+
+    #conn.request("GET", "/api/v1/searchFlights?origin=ORD&destination=BOS&date=2023-01-29&returnDate=2023-01-30&adults=1&cabinClass=economy&filter=price&currency=USD&countryCode=US&market=en-US", headers=headers)
+
+    #conn.request("GET", f"/api/v1/searchFlights?origin={args.origion}&destination={args.destination}&date=2023-01-29&returnDate=2023-01-30&adults=1&cabinClass=economy&filter=price&currency=USD&countryCode=US&market=en-US", headers=headers)
+
+    conn.request("GET", f"/api/v1/searchFlights?origin={origin}&destination={destination}&date={departure_date}&returnDate={return_date}&adults=1&cabinClass=economy&filter=price&currency=USD&countryCode=US&market=en-US", headers=headers)
+
+    res = conn.getresponse()
+    data = res.read()
+    json_obj = json.loads(data)
+
+    flights = json_obj['data']
+
+    flight_price_total = []
+
+    id_departing = []
+    id_returning = []
+
+    departure_time_departing = []
+    departure_time_returning = []
+
+    arrival_time_departing = []
+    arrival_time_returning = []
+
+    duration_departing = []
+    duration_returning = []
+
+    carrier_departing = []
+    carrier_returning = []
+
+    for f in flights:
+        flight_price_total.append(f['price']['amount'])
+        id_departing.append(f['legs'][0]['id'])
+        id_returning.append(f['legs'][1]['id'])
+        departure_time_departing.append(f['legs'][0]['departure'])
+        departure_time_returning.append(f['legs'][1]['departure'])
+        arrival_time_departing.append(f['legs'][0]['arrival'])
+        arrival_time_returning.append(f['legs'][1]['arrival'])
+        duration_departing.append(f['legs'][0]['duration'])
+        duration_returning.append(f['legs'][1]['duration'])
+        carrier_departing.append(f['legs'][0]['carriers'][0]['name'])
+        carrier_returning.append(f['legs'][1]['carriers'][0]['name'])
+
+    df = pd.DataFrame({'flight_price_total':flight_price_total,'departure_time_departing':departure_time_departing,'departure_time_returning':departure_time_returning,'duration_departing':duration_departing,'duration_returning':duration_returning,'id_departing':id_departing,'id_returning':id_returning})
+
+    # convert time to morning / afternoon / night
+    df['departing_timeperiod'] = ['Morning' if 5<int(t.split('T')[1].split(':')[0])<12 else 'Afternoon' if 12<int(t.split('T')[1].split(':')[0])<18 else 'Evening' for t in departure_time_departing]
+    df['returning_timeperiod'] = ['Morning' if 5<int(t.split('T')[1].split(':')[0])<12 else 'Afternoon' if 12<int(t.split('T')[1].split(':')[0])<18 else 'Evening' for t in departure_time_returning]
+    df['time_code'] = ['00' if i == 'Morning' else '01' if i == 'Afternoon' else '10' for i in df['departing_timeperiod']]
+    df['airline_code'] = ['00' if i == 'American' else '01' if i == 'Delta' else '10' if i == 'United' else '11' for i in df['departing_timeperiod']]
+
+    return df
+
+#df.to_csv('flights_info.csv',index=False)

team_solutions/Im_ThinQing/Check_hotel.py

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+import json
+import pandas as pd
+import http.client
+
+def check_hotel(region_id,Departure_date,Return_date):
+    conn = http.client.HTTPSConnection("hotels-com-provider.p.rapidapi.com")
+
+    headers = {
+        'X-RapidAPI-Key': "20f4b82842msh0a60cfb5f9bf13ep1dfe41jsnaa9a78013956",
+        'X-RapidAPI-Host': "hotels-com-provider.p.rapidapi.com"
+        }
+
+    conn.request("GET", f"/v2/hotels/search?domain=US&sort_order=PRICE_LOW_TO_HIGH&locale=en_US&checkout_date={Return_date}&region_id={region_id}&adults_number=1&checkin_date={Departure_date}&available_filter=SHOW_AVAILABLE_ONLY&meal_plan=FREE_BREAKFAST&guest_rating_min=8&price_min=10&page_number=1&amenities=WIFI%2CPARKING&price_max=500&lodging_type=HOTEL%2CHOSTEL%2CAPART_HOTEL&star_rating_ids=3%2C4%2C5", headers=headers)
+
+    res = conn.getresponse()
+    data = res.read()
+
+    # parse 
+
+    json_obj = json.loads(data)
+
+    with open('hotel_info.json', 'w') as outfile:
+        json.dump(json_obj, outfile,indent=4)
+
+    hotels = json_obj['properties']
+
+    names = []
+    prices = []
+    review_score = []
+    review_count = []
+    star = []
+
+    for h in hotels:
+        names.append(h['name'])
+        prices.append(h['price']['lead']['formatted'])
+        review_score.append(h['reviews']['score'])
+        review_count.append(h['reviews']['total'])
+        star.append(h['star'])
+
+    df = pd.DataFrame({'name':names,'price':prices,'review_score':review_score,'review_count':review_count,'star':star})
+    return df

team_solutions/Im_ThinQing/Check_region_id.py

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+import json
+import airportsdata
+import http.client
+
+airports = airportsdata.load('IATA')
+
+def check_region_id(Destination):
+
+    city_name = airports[Destination]['city']
+
+    conn = http.client.HTTPSConnection("hotels-com-provider.p.rapidapi.com")
+
+    headers = {
+        'X-RapidAPI-Key': "20f4b82842msh0a60cfb5f9bf13ep1dfe41jsnaa9a78013956",
+        'X-RapidAPI-Host': "hotels-com-provider.p.rapidapi.com"
+        }
+
+    conn.request("GET", f"/v2/regions?locale=en_US&query={city_name}&domain=US", headers=headers)
+
+    res = conn.getresponse()
+    data = res.read()
+
+    region_id = json.loads(data.decode("utf-8"))['data'][0]['gaiaId']
+
+    return region_id
+
+

team_solutions/Im_ThinQing/Check_restaurant.py

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+import json
+import http.client
+import pandas as pd
+import airportsdata
+
+airports = airportsdata.load('IATA')
+
+def check_restaurant(Destination):
+
+    city_name = airports[Destination]['city']
+
+    conn = http.client.HTTPSConnection("yelp-reviews.p.rapidapi.com")
+
+    headers = {
+        'X-RapidAPI-Key': "20f4b82842msh0a60cfb5f9bf13ep1dfe41jsnaa9a78013956",
+        'X-RapidAPI-Host': "yelp-reviews.p.rapidapi.com"
+        }
+
+    conn.request("GET", f"/business-search?query=American&location={city_name}%2C%20MA%2C%20USA&yelp_domain=yelp.com", headers=headers)
+
+    res = conn.getresponse()
+    data = res.read()
+
+    json_obj =  json.loads(data)
+    restaurant = json_obj['data']
+
+    name = []
+    rating = []
+    review_count = []
+    price_range = []
+
+    for r in restaurant:
+        name.append(r['name'])
+        rating.append(r['rating'])
+        review_count.append(r['review_count'])
+        price_range.append(r['price_range'])
+
+    df = pd.DataFrame({'name':name,'rating':rating,'review_count':review_count,'price_range':price_range})
+    return df

team_solutions/Im_ThinQing/README.md

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+# I'm ThinkQing
+
+Members: Arjun Bhamra, Amanda Sutrisno, Maggpie Yao, Kusuma, Ruijie Zhu
+
+
+Background:
+Flight, hotel, and restaurant consistitue the majority of travel expanses. Optimizing the total cost of these three components is tricky, especially if the total number of combinations is large.
+
+Highlight:
+We applied grover's algorithm to optimize flight+hotel+resturant combinations with a given travel budget.
+
+Documentation: ./documentation/

team_solutions/Im_ThinQing/app.py

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+import random
+
+import sys,os
+sys.path.append(os.getcwd())
+
+from Check_flights import check_flights
+from Check_region_id import check_region_id
+from Check_hotel import check_hotel
+from Check_restaurant import check_restaurant
+from covalent_grover_sampler import grover_function
+from flask import Flask, render_template, request
+
+app = Flask(__name__) # Creating our Flask Instance
+
+@app.route('/', methods=['GET'])
+def index():
+    """ Displays the home page accessible at '/' """
+
+    return render_template('homepage.html')
+
+@app.route('/taskbar/', methods=['GET'])
+def taskbar():
+    return render_template('taskbar.html')
+
+@app.route('/result/', methods=['POST'])
+def result():
+    Origin = request.form['Origin']  
+    Destination = request.form['Destination']
+    Departure_date = request.form['Departure_date']
+    Return_date = request.form['Return_date']
+    Travel_time = request.form['Travel_time']
+    Airlines = request.form['Airlines']
+    Food_type = request.form['Food_type']
+    Price_range = request.form['Price_range']
+
+    df_flights = check_flights(Origin,Destination,Departure_date,Return_date)
+    lowest_flight_price = list(df_flights.flight_price_total)[0]
+    cheapest_flight_id_departing = list(df_flights.id_departing)[0]
+    cheapest_flight_id_returning = list(df_flights.id_returning)[0]
+
+
+    region_id = check_region_id(Destination)
+    df_hotel = check_hotel(region_id,Departure_date,Return_date)
+    flight_time_code_dict = {list(df_flights.time_code)[i]: list(df_flights.flight_price_total)[i] for i in range(len(df_flights))}
+    flight_airline_code_dict = {list(df_flights.airline_code)[i]: list(df_flights.flight_price_total)[i] for i in range(len(df_flights))}
+    lowest_hotel_price = list(df_hotel.price)[0]
+    cheapest_hotel_name = list(df_hotel.name)[0]
+
+    df_restaurant = check_restaurant(Destination)
+    lowest_restaurant_price = list(df_restaurant.price_range)[0]
+    cheapest_restaurant_name = list(df_restaurant.name)[0]
+
+    return render_template(
+            'taskbar.html',
+            Origin=Origin,
+            Destination=Destination,
+            Departure_date=Departure_date,
+            Return_date=Return_date,
+            Travel_time=Travel_time,
+            Airlines=Airlines,
+            Food_type=Food_type,
+            Price_range=Price_range,
+            lowest_flight_price=lowest_flight_price,
+            cheapest_flight_id_departing=cheapest_flight_id_departing,
+            cheapest_flight_id_returning=cheapest_flight_id_returning,
+            lowest_hotel_price=lowest_hotel_price,
+            cheapest_hotel_name=cheapest_hotel_name,
+            lowest_restaurant_price=lowest_restaurant_price,
+            cheapest_restaurant_name=cheapest_restaurant_name,
+            grover_result=grover_function("1100")
+        )
+
+
+if __name__ == '__main__':
+    port = 5000 + random.randint(0, 999)
+    url = f"http://127.0.0.1:{port}"
+    app.run(use_reloader=False, debug=True, port=port)

team_solutions/Im_ThinQing/covalent_grover_sampler.py

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+import os
+
+import covalent as ct
+from qiskit import QuantumCircuit
+from qiskit.compiler import transpile
+from qiskit.quantum_info import SparsePauliOp
+from qiskit_ibm_runtime import Estimator, QiskitRuntimeService, Session, Sampler
+
+from qiskit import IBMQ
+token = "09428f6730538a20ee6382d16ca3461c758d0346cdd54071ff0ae8651f1f91c0fcac8bfdb998a7688ff6f751877cab2f038d33cbfd1b6e7dc1ec8cbcdfc72b95"
+#IBMQ.save_account(token)
+IBMQ.load_account() # Load account from disk
+IBMQ.providers()    # List all available providers
+
+def grover_function(s:str):
+
+	import random
+	from qiskit.quantum_info import Statevector
+
+	#secret = random.randint(0,15)  # the owner is randomly picked
+	#secret_string = format(secret, '04b')  # format the owner in 7-bit string
+	secret_string = s
+	oracle = Statevector.from_label(secret_string)  # let the oracle know the owner
+
+	from qiskit.algorithms import AmplificationProblem
+
+	problem = AmplificationProblem(oracle, is_good_state=secret_string)
+
+	from qiskit.algorithms import Grover
+	import math
+
+	def grover(problem):
+	    n = int(math.sqrt((len(s))))
+	    grover = Grover(n)
+	    circuit = grover.construct_circuit(problem)
+	    circuit.measure_all() #NOTE THIS, REMOVED MEASUREALL BC OF OBSERVABLES, WILL SEE IF IT WORKS
+	    #grover_circuits.append(circuit)
+	    return circuit
+
+	@ct.electron(deps_pip=ct.DepsPip(["qiskit==0.40.0"]))
+	def evaluate_circuit(token: str, shots=100):
+	    #QiskitRuntimeService.save_account(channel="ibm_quantum",
+	    #                                  token=token,
+	    #                                  instance="ibm-q-community/mit-hackathon/main",
+	    #                                  overwrite=True)
+
+	    #with Session(service=QiskitRuntimeService(channel="ibm_quantum"), backend="ibmq_qasm_simulator"):#ibm_nairobi
+		#estimator = Estimator()
+		#sampler = Sampler()
+		#job = sampler.run(grover(problem))
+		#return job
+		#observables = (
+	    	#SparsePauliOp("ZZZ"),
+		#)
+		#job = estimator.run(circuits=grover(problem),
+		#                     observables=observables,
+		#                     shots=shots).result()
+		#return job
+
+	    with Session(service=QiskitRuntimeService(channel="ibm_quantum"), backend="ibmq_qasm_simulator"):
+	    	sampler = Sampler()
+	    	job = sampler.run(circuits=grover(problem), shots=shots)
+	    	result = job.result()
+	    	result_dict = result.quasi_dists[0].binary_probabilities()
+	    	answer = max(result_dict, key=result_dict.get)
+	    	return answer
+	    	#return result
+	    	#print(result)
+
+	@ct.lattice
+	def test(token):
+		return evaluate_circuit(token)
+		#job = evaluate_circuit(token)
+		#return job
+		#result = job.get_result()
+		#return result
+		#result_dict = result.quasi_dists[0].binary_probabilities()
+		#answer = max(result_dict, key=result_dict.get)
+		#return answer
+
+	dispatch_id = ct.dispatch(test)(token)
+	print(f"\n{dispatch_id}" + " - Running")
+
+	workflow_result = ct.get_result(dispatch_id, wait=True)
+	print("Result")
+	print(workflow_result.result)
+
+	#result_dict = workflow_result.quasi_dists[0].binary_probabilities()
+	#answer = max(result_dict, key=result_dict.get)
+	#print(answer)
+
+	print("Secret String: " + secret_string)
+
+	return workflow_result.result
+
+#grover_function("1100")