From 55981cfe1bd5e6df75b63c9a818cb414af3b824c Mon Sep 17 00:00:00 2001 From: paulinebrunet03 <70296017+paulinebrunet03@users.noreply.github.com> Date: Tue, 24 Oct 2023 15:33:54 -0700 Subject: [PATCH 01/25] Create "I don't know, what do you feel like" Proposal --- ...'t know, what do you feel like\" Proposal" | 34 +++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 "\"I don't know, what do you feel like\" Proposal" diff --git "a/\"I don't know, what do you feel like\" Proposal" "b/\"I don't know, what do you feel like\" Proposal" new file mode 100644 index 0000000000..eb89f41c85 --- /dev/null +++ "b/\"I don't know, what do you feel like\" Proposal" @@ -0,0 +1,34 @@ +Intelligent Dining Decision Assistant Application + +Group Members: +Pauline Brunet, NetID: pbrunet2 +Catherine Orlando, NetID: co24 +Austin Harmon, NetID: austin31 +Mitchell Kopczyk, NetID: kopczyk2 +Louis Hamilton, NetID: ??? + +Topic Chosen: +Building an application that aids indecisive users in making informed dining choices by providing personalized restaurant recommendations and insights. + +Problem and Relevance to Theme and Class: +Choosing a restaurant can be a complex decision-making process, made even more challenging due to the vast amount of online information available. Our application intends to make this process more manageable and intelligent. The chosen topic aligns with our Text Information Systems class by incorporating concepts such as semantic analysis, relevance, probability ranking, and collaborative filtering to improve the accuracy and relevance of restaurant recommendations and insights. + +Datasets, Algorithms, or Techniques Planned: + +Datasets: Utilize publicly available restaurant APIs for obtaining detailed information, reviews, and ratings. + +Techniques and Concepts from the Course: +Semantic Analysis: Understanding user queries and reviews to generate relevant restaurant recommendations. +Bag of Words Representation & Vector Space Model: For text representation and understanding the context of user preferences and restaurant information. +Probability Ranking and Relevance: Ensuring that the most suitable restaurant recommendations are shown to the users. +Collaborative Filtering: Using user-item interactions for personalizing restaurant recommendations. +Evaluation Metrics (Precision, Recall, MAP): To evaluate the performance and relevance of our recommendations. + +Demonstration and Programming Language: +We plan to conduct user-based evaluations to receive feedback on the relevance and usefulness of our recommendations, as well as the overall user experience of our application. + +Programming Language: +We intend to use languages such as Python for backend development and potentially JavaScript along with suitable frameworks for frontend development. + +Workload Justification: +Tasks Will Go Here… Task1, Task2, Task3, etc… From 3663c313982e95024c80d3387fef3269bace7958 Mon Sep 17 00:00:00 2001 From: louishamilton <37088794+louishamilton@users.noreply.github.com> Date: Wed, 25 Oct 2023 09:58:02 -0500 Subject: [PATCH 02/25] Added my NetID --- "\"I don't know, what do you feel like\" Proposal" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\"I don't know, what do you feel like\" Proposal" "b/\"I don't know, what do you feel like\" Proposal" index eb89f41c85..a90b460c34 100644 --- "a/\"I don't know, what do you feel like\" Proposal" +++ "b/\"I don't know, what do you feel like\" Proposal" @@ -5,7 +5,7 @@ Pauline Brunet, NetID: pbrunet2 Catherine Orlando, NetID: co24 Austin Harmon, NetID: austin31 Mitchell Kopczyk, NetID: kopczyk2 -Louis Hamilton, NetID: ??? +Louis Hamilton, NetID: louisch3 Topic Chosen: Building an application that aids indecisive users in making informed dining choices by providing personalized restaurant recommendations and insights. From 69e22ad65cb4b9d604ef9ca0a6e1fa75ab7a0907 Mon Sep 17 00:00:00 2001 From: louishamilton <37088794+louishamilton@users.noreply.github.com> Date: Wed, 25 Oct 2023 10:22:18 -0500 Subject: [PATCH 03/25] Add Autsin's Text and Formula to Proposal, some minor additional changes --- ...'t know, what do you feel like\" Proposal" | 36 ++++++++++++++----- 1 file changed, 28 insertions(+), 8 deletions(-) diff --git "a/\"I don't know, what do you feel like\" Proposal" "b/\"I don't know, what do you feel like\" Proposal" index a90b460c34..bbd9c3b549 100644 --- "a/\"I don't know, what do you feel like\" Proposal" +++ "b/\"I don't know, what do you feel like\" Proposal" @@ -11,18 +11,20 @@ Topic Chosen: Building an application that aids indecisive users in making informed dining choices by providing personalized restaurant recommendations and insights. Problem and Relevance to Theme and Class: -Choosing a restaurant can be a complex decision-making process, made even more challenging due to the vast amount of online information available. Our application intends to make this process more manageable and intelligent. The chosen topic aligns with our Text Information Systems class by incorporating concepts such as semantic analysis, relevance, probability ranking, and collaborative filtering to improve the accuracy and relevance of restaurant recommendations and insights. +Choosing a restaurant can be a complex decision-making process, made even more challenging due to the vast amount of online information available. Our application intends to make this process more manageable and intelligent. The chosen topic aligns with our Text Information Systems class by incorporating concepts such as semantic analysis, relevance, probabilistic ranking, and collaborative filtering to improve the accuracy and relevance of restaurant recommendations and insights. Datasets, Algorithms, or Techniques Planned: -Datasets: Utilize publicly available restaurant APIs for obtaining detailed information, reviews, and ratings. + Datasets: Utilize publicly available restaurant APIs for obtaining detailed information, reviews, and ratings. -Techniques and Concepts from the Course: -Semantic Analysis: Understanding user queries and reviews to generate relevant restaurant recommendations. -Bag of Words Representation & Vector Space Model: For text representation and understanding the context of user preferences and restaurant information. -Probability Ranking and Relevance: Ensuring that the most suitable restaurant recommendations are shown to the users. -Collaborative Filtering: Using user-item interactions for personalizing restaurant recommendations. -Evaluation Metrics (Precision, Recall, MAP): To evaluate the performance and relevance of our recommendations. + Algorithms: Our current algorithm for relevance (R) is R = (Number of unique query terms in document / Number of query terms) * 100 + + Techniques and Concepts from the Course: + Semantic Analysis: Understanding user queries and reviews to generate relevant restaurant recommendations. + Bag of Words Representation & Vector Space Model: For text representation and understanding the context of user preferences and restaurant information. + Probabilistic Relevance Ranking for Text Retrieval: Ensuring that the most suitable restaurant recommendations are shown to the users. + Collaborative Filtering: Using user-item interactions for personalizing restaurant recommendations. + Evaluation Metrics (Precision, Recall, MAP): To evaluate the performance and relevance of our recommendations. Demonstration and Programming Language: We plan to conduct user-based evaluations to receive feedback on the relevance and usefulness of our recommendations, as well as the overall user experience of our application. @@ -30,5 +32,23 @@ We plan to conduct user-based evaluations to receive feedback on the relevance a Programming Language: We intend to use languages such as Python for backend development and potentially JavaScript along with suitable frameworks for frontend development. +Application Outcome: +Every time a user enters a query and clicks the “Find Restaurants” button, the system will generate a +Python list that consists of a collection of restaurants that are relevant to the query. The generation of +this list is made possible by an inverted index. Next to each restaurant in the results, a relevance score +will be displayed. The relevance score is calculated based on the following formula: +Relevance = (Number of unique query terms in document / Number of query terms) * 100 + +In this scenario, each restaurant name displayed in the results represents a document. Each document is +a text file that contains the name of the restaurant on the first line. The next lines contain information +about the restaurant, which is mostly the food items offered by the restaurant. When the program is not +running, more restaurant text files can be added to the main folder and the program will incorporate +these files into the inverted index when it starts. + +There is no limit to the number of queries a user can enter. While the program is actively running, the +user can continually keep entering queries and all results will be maintained. Each new result list will +be displayed at the top of the previous results. If the collection of result lists exceeds the window +height, the user will be able to scroll down to the bottom of the results. + Workload Justification: Tasks Will Go Here… Task1, Task2, Task3, etc… From 500e0f043f34440cb0c3e172771c3fa0947ea976 Mon Sep 17 00:00:00 2001 From: austin3131 <144085220+austin3131@users.noreply.github.com> Date: Thu, 26 Oct 2023 02:12:01 -0500 Subject: [PATCH 04/25] Update "I don't know, what do you feel like" Proposal --- ... don't know, what do you feel like\" Proposal" | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git "a/\"I don't know, what do you feel like\" Proposal" "b/\"I don't know, what do you feel like\" Proposal" index bbd9c3b549..50e6746d93 100644 --- "a/\"I don't know, what do you feel like\" Proposal" +++ "b/\"I don't know, what do you feel like\" Proposal" @@ -35,9 +35,11 @@ We intend to use languages such as Python for backend development and potentiall Application Outcome: Every time a user enters a query and clicks the “Find Restaurants” button, the system will generate a Python list that consists of a collection of restaurants that are relevant to the query. The generation of -this list is made possible by an inverted index. Next to each restaurant in the results, a relevance score -will be displayed. The relevance score is calculated based on the following formula: +this list is made possible by an inverted index. Next to each restaurant in the results, the city, state, and +a relevance score will be displayed. The relevance score is calculated based on the following formula: Relevance = (Number of unique query terms in document / Number of query terms) * 100 +The only exception to the relevance formula is when certain stop words are entered, such as “and” and “the”. +Stop words are ignored during the relevance calculation. In this scenario, each restaurant name displayed in the results represents a document. Each document is a text file that contains the name of the restaurant on the first line. The next lines contain information @@ -50,5 +52,14 @@ user can continually keep entering queries and all results will be maintained. E be displayed at the top of the previous results. If the collection of result lists exceeds the window height, the user will be able to scroll down to the bottom of the results. +The user has the option to filter the results by specifying their location with a city and state name. +If the user decides to enter their location, the program will still return the original results, +except the results that correspond to a different location will be replaced with the text “Location Filter Applied”. +The location filter will only allow the user to see the results that correspond to the specified location. The user will still be +able to view the count of the other search results for different locations, which could encourage the user to try their +query with a different location, or remove the entire filter. To be the most effective, the user should specify their +location in the query and the location filter. + + Workload Justification: Tasks Will Go Here… Task1, Task2, Task3, etc… From 2af32e3f1627c8f5ed9cbeec413fbab6f78a4fd0 Mon Sep 17 00:00:00 2001 From: austin3131 <144085220+austin3131@users.noreply.github.com> Date: Thu, 26 Oct 2023 11:04:00 -0500 Subject: [PATCH 05/25] Update "I don't know, what do you feel like" Proposal --- "\"I don't know, what do you feel like\" Proposal" | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git "a/\"I don't know, what do you feel like\" Proposal" "b/\"I don't know, what do you feel like\" Proposal" index 50e6746d93..edccfbdae8 100644 --- "a/\"I don't know, what do you feel like\" Proposal" +++ "b/\"I don't know, what do you feel like\" Proposal" @@ -42,9 +42,9 @@ The only exception to the relevance formula is when certain stop words are enter Stop words are ignored during the relevance calculation. In this scenario, each restaurant name displayed in the results represents a document. Each document is -a text file that contains the name of the restaurant on the first line. The next lines contain information -about the restaurant, which is mostly the food items offered by the restaurant. When the program is not -running, more restaurant text files can be added to the main folder and the program will incorporate +a text file that contains the name of the restaurant on the first line. The city and state are included on the second line. +The next lines contain information about the restaurant, which is mostly the food items offered by the restaurant. +When the program is not running, more restaurant text files can be added to the main folder and the program will incorporate these files into the inverted index when it starts. There is no limit to the number of queries a user can enter. While the program is actively running, the From c51b36f52e23b18e54da4c4ae932255342007f8e Mon Sep 17 00:00:00 2001 From: kopczyk93 <127679010+kopczyk93@users.noreply.github.com> Date: Thu, 26 Oct 2023 21:33:42 -0500 Subject: [PATCH 06/25] Update "I don't know, what do you feel like" Proposal These are some basics. We can add more detail for Program Draft parts on specifics we will be working on. --- ...'t know, what do you feel like\" Proposal" | 46 ++++++++++++++++++- 1 file changed, 44 insertions(+), 2 deletions(-) diff --git "a/\"I don't know, what do you feel like\" Proposal" "b/\"I don't know, what do you feel like\" Proposal" index edccfbdae8..1267566f04 100644 --- "a/\"I don't know, what do you feel like\" Proposal" +++ "b/\"I don't know, what do you feel like\" Proposal" @@ -60,6 +60,48 @@ able to view the count of the other search results for different locations, whic query with a different location, or remove the entire filter. To be the most effective, the user should specify their location in the query and the location filter. - Workload Justification: -Tasks Will Go Here… Task1, Task2, Task3, etc… +1. Initial Project Proposal Draft (Completed) + - Conducted research and brainstorming. + - Drafted the initial proposal outlining the project's goals and methodology. + - Total hours spent: 10 hours (2 hours/student) + +2. Project Proposal Edit and Proofread + - Revised, refined, and proofread the proposal for clarity. + - Ensured that the proposal met all project requirements and guidelines. + - Total hours spent: 5 hours (1 hour/student) + +3. First Program Draft Development (Completed) + - Developed the initial version of the program, focusing on core functionalities. + - Conducted basic testing and debugging. + - Total hours spent: 30 hours (6 hours/student) + +4. Second Program Draft Development + - Improved and expanded upon the initial code. + - Conducted further testing, identifying areas for improvement and optimization. + - Total hours spent: 25 hours (5 hours/student) + +5. Final Program Draft Development + - Conducted final refinements to optimize the code. + - Carried out comprehensive testing and debugging to ensure functionality and reliability. + - Total hours spent: 20 hours (4 hours/student) + +6. Initial Progress Report + - Compiled and documented the project's progress, identifying completed tasks. + - Total hours spent: 2 hours (0.4 hours/student) + +7. Progress Report Edit and Proofread + - Reviewed and revised the progress report, ensuring accuracy and clarity. + - Total hours spent: 2 hours (0.4 hours/student) + +8. Program Documentation Development + - Developed detailed documentation explaining the program’s code and functionalities. + - Ensured that the documentation is clear and understandable. + - Total hours spent: 3 hours (0.6 hours/student) + +9. Program Presentation + - Prepared a cohesive presentation to showcase the project’s development process and final product. + - Practiced the presentation to ensure smooth delivery. + - Total hours spent: 3 hours (0.6 hours/student) + +Total Workload: 100 hours From f510d5a53d1e8b2c69644f8218d5eb3a19df0a3e Mon Sep 17 00:00:00 2001 From: kopczyk93 <127679010+kopczyk93@users.noreply.github.com> Date: Fri, 27 Oct 2023 20:10:05 -0500 Subject: [PATCH 07/25] Update "I don't know, what do you feel like" Proposal --- "\"I don't know, what do you feel like\" Proposal" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\"I don't know, what do you feel like\" Proposal" "b/\"I don't know, what do you feel like\" Proposal" index 1267566f04..96d240b81b 100644 --- "a/\"I don't know, what do you feel like\" Proposal" +++ "b/\"I don't know, what do you feel like\" Proposal" @@ -1,7 +1,7 @@ Intelligent Dining Decision Assistant Application Group Members: -Pauline Brunet, NetID: pbrunet2 +Pauline Brunet, NetID: pbrunet2 (Captain) Catherine Orlando, NetID: co24 Austin Harmon, NetID: austin31 Mitchell Kopczyk, NetID: kopczyk2 From 122e978cbe93e2b6d765ffde77c497f9d8f2d9ed Mon Sep 17 00:00:00 2001 From: austin3131 <144085220+austin3131@users.noreply.github.com> Date: Fri, 27 Oct 2023 23:37:01 -0500 Subject: [PATCH 08/25] Add files via upload --- PDF_Proposal.pdf | Bin 0 -> 124234 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 PDF_Proposal.pdf diff --git a/PDF_Proposal.pdf b/PDF_Proposal.pdf new file mode 100644 index 0000000000000000000000000000000000000000..dec04a049e5899d30a55cdcdb19953ce19e2ded0 GIT binary patch literal 124234 zcmdqI1ymhRvo8w4f=h7SI0V@n*Wm8%A-KD{y95dDZV48GySoG@1SdFz;6d+({4M8v z-#h2rch_C(J!V1mbXQeZ_w-Evs;Z$>5D{ZwVq`-`psWS|AR{mVKmc0<3uFXdUZ9G* zoiR{Y-$~!f))c6qZ))raU$tbrKF9CEm+NC(bxz8W;5mk{~#k6+Za7{@u=*#zMlF33fnr{I00CJ(&k2v+JGl@ z0H#N~lmJYePlOrtNL~Z}sqB&OKPdxS?O+U6_RDM~V@F$O2SZ~=uu)>RHcpR(3BdNK 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zA%sH+;SfSNgb)rPghL475JEVF5Dp=m0|Ps+G8=}nIWVwKv-Q{i~P zh%K7~Lm?a(gacI5te7zf2iT|CdJql_!hu0JK<&dGkIex}10_9-Kv{JY_!|_M8=uw@ z7$E>_#3wS04@{M60U#s6`mC%+gE{nG7ASY~@{!2798kRv)$HVdy- Date: Fri, 27 Oct 2023 23:39:35 -0500 Subject: [PATCH 09/25] Update "I don't know, what do you feel like" Proposal --- "\"I don't know, what do you feel like\" Proposal" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\"I don't know, what do you feel like\" Proposal" "b/\"I don't know, what do you feel like\" Proposal" index 1267566f04..96d240b81b 100644 --- "a/\"I don't know, what do you feel like\" Proposal" +++ "b/\"I don't know, what do you feel like\" Proposal" @@ -1,7 +1,7 @@ Intelligent Dining Decision Assistant Application Group Members: -Pauline Brunet, NetID: pbrunet2 +Pauline Brunet, NetID: pbrunet2 (Captain) Catherine Orlando, NetID: co24 Austin Harmon, NetID: austin31 Mitchell Kopczyk, NetID: kopczyk2 From 2f070bba2255c0b77827249b082d50274f937d1d Mon Sep 17 00:00:00 2001 From: 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XI~^OlKL-&6$PN>{X=p@bL{a`1CzTzC literal 0 HcmV?d00001 From ecafd1ecd393304606d7151ee44cd2349f1f853a Mon Sep 17 00:00:00 2001 From: austin3131 <144085220+austin3131@users.noreply.github.com> Date: Fri, 1 Dec 2023 18:12:27 -0600 Subject: [PATCH 11/25] Create project_code.py --- project_code.py | 150 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 150 insertions(+) create mode 100644 project_code.py diff --git a/project_code.py b/project_code.py new file mode 100644 index 0000000000..b6537748ba --- /dev/null +++ b/project_code.py @@ -0,0 +1,150 @@ + +import os +from tkinter import * +from collections import defaultdict + + + + +class Search: + + def __init__(self):# this function reads and indexes files + + + self.invertedindex = invertedindex = defaultdict(list) + self.list1 = list1 = [] + self.titlelist = titlelist= [] + #created a new folder on the desktop and added a lot of text files + #replaced \ with \\ in the folder path + + filelocations = "C:\\Users\\Admin\\Desktop\\restaurants" + os.chdir(filelocations) #make the program read files from the specified path + + for eachfile in os.listdir(): #for every file in the folder + + with open(eachfile, "r") as r: #open the file + self.list1.append(r.read()) #append the contents of the file to list1 + + + + + for eachdoccontent in list1: #for each batch of text in a file + tokens = eachdoccontent.split() #split all text into tokens + + for eachtoken in tokens: #for each token in a file + + lowercasewords = eachtoken.lower() #lowercase each token before indexing + removed_1 = lowercasewords.replace(".","") #remove "." before indexing + removed_2 = removed_1.replace(",", "") # remove "," before indexing + removed_3 = removed_2.replace("and", "") # remove "and" before indexing + removed_4 = removed_3.replace("the", "") # remove "the" before indexing + + if list1.index(eachdoccontent) not in invertedindex[removed_4]: #if a document number is not a value for a particular token + invertedindex[removed_4].append(list1.index(eachdoccontent)) #add that document number as a value for that token + + + + def searchindex(self,query,location): #this function queries the inverted index + + querylen = len(query.split()) + resultlist = [] + for eachqueryterm in query.split():#split all query terms + + lowercasequery = eachqueryterm.lower()#lowercase the user query + query_remove_1 = lowercasequery.replace(".","")#remove "." from user query + query_remove_2 = query_remove_1.replace(",", "") #remove "," from user query + if eachqueryterm == "and": #remove "and" from user query + querylen = querylen - 1 + if eachqueryterm == "the": #remove "the" from user query + querylen = querylen - 1 + + for item in self.invertedindex[query_remove_2]: #for every item in the inverted index for a query term + resultlist.append(item) #append each item to result list + + rankdict = {} + for item in resultlist: #for each item in the result list + if item in rankdict: #if the item is in the rankdict + rankdict[item] = rankdict[item] + 1 #increase the count by 1 + else: + rankdict[item] = 1 #if not, then assign 1 + + for item in rankdict: + rankdict[item] = rankdict[item] / querylen #calculating the relavance score + + ranklist = (sorted(rankdict, key=rankdict.get, reverse = True))#this sorts the dictionary by value + + + totalcounter = 1 + relloccounter = 0 + guilist = [] + # this is the output if at least 1 word of the query is in a document + for item in ranklist: + #print the first line in the file and the relevance score + #print(self.list1[item].split("\n")[0],": relevance "+str(round(rankdict[item]*100,3)) + "%") + if location != "": + if self.list1[item].split("\n")[1] == location : + guilist.append("#"+ str(totalcounter) + " " +self.list1[item].split("\n")[0] + ", LOC: " + self.list1[item].split("\n")[1] + ", relevance: "+str(round(rankdict[item]*100,3)) + "%") + relloccounter = relloccounter + 1 + else: + guilist.append("Location Filter Applied") + + if location == "": + guilist.append("#"+ str(totalcounter) + " " +self.list1[item].split("\n")[0] + ", LOC: " + self.list1[item].split("\n")[1] + ", relevance: " + str(round(rankdict[item] * 100, 3)) + "%") + totalcounter = totalcounter + 1 + # this is the output if no words of the query are in any documents + if len(ranklist) == 0: + #print("No words in any document matched your query.") + + guilist.append("No words in any document matched your query.") + + + return guilist, totalcounter-1, relloccounter + + +gui = Tk() +gui.geometry("850x600") +gui.title('The Restaurant Selector') + +titlelabel = Label(gui, text="What do you feel like eating today?",font=("Times", 21)) +titlelabel.pack() + +inputtxt = Text(gui, width=40,height=5) +inputtxt.pack() + +filterlabel = Label(gui, text="(Optional) Please enter a city name and the first two letters of the state. eg. Urbana IL",font=("Times", 8)) +filterlabel.pack() + +filterlabel2 = Label(gui, text="NOTE: The format must exactly match the format of the example.",font=("Times", 8)) +filterlabel2.pack() + +locationtxt = Entry(gui, width=20) +locationtxt.pack() + +resultslabel = Label(gui, text="Results",font=("Times", 12)) +resultslabel.pack() + +results = Text(gui, width=95,height=24) +results.pack() + + +def guifunction(): + + location = locationtxt.get() + + gettext = inputtxt.get("1.0", "end") + + results.insert("1.0", "\nYour Query: "+gettext) + + for item in Search().searchindex(gettext,location)[0].__reversed__(): + results.insert("1.0", "\n" + str(item)) + + results.insert("1.0", "\n" + str(Search().searchindex(gettext, location)[2]) + " relevant to your specified location") + results.insert("1.0", "\n" + str(Search().searchindex(gettext, location)[1]) + " total results returned") + + + +searchbutton = Button(gui, text ="Find Restaurants", command = guifunction) +searchbutton.place(x=70,y=60) + + +gui.mainloop() From f9a7939fd4ce201cd24f99f9b1e4e86a6c1ce2c3 Mon Sep 17 00:00:00 2001 From: mitch344 <35354833+mitch344@users.noreply.github.com> Date: Sat, 2 Dec 2023 14:24:19 -0600 Subject: [PATCH 12/25] Improve resturant loading Restaurants will load from a restaurant folder inside script path --- project_code.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/project_code.py b/project_code.py index b6537748ba..e1ea4994f0 100644 --- a/project_code.py +++ b/project_code.py @@ -14,11 +14,14 @@ def __init__(self):# this function reads and indexes files self.invertedindex = invertedindex = defaultdict(list) self.list1 = list1 = [] self.titlelist = titlelist= [] - #created a new folder on the desktop and added a lot of text files - #replaced \ with \\ in the folder path - filelocations = "C:\\Users\\Admin\\Desktop\\restaurants" - os.chdir(filelocations) #make the program read files from the specified path + #resturant folder in the same path as the script + self.filelocations = os.path.join(os.path.dirname(__file__), "restaurants") + if not os.path.exists(self.filelocations): + messagebox.showerror("Error", "Restaurant folder does not exist") + exit() + + os.chdir(self.filelocations) #make the program read files from the specified path for eachfile in os.listdir(): #for every file in the folder @@ -26,8 +29,6 @@ def __init__(self):# this function reads and indexes files self.list1.append(r.read()) #append the contents of the file to list1 - - for eachdoccontent in list1: #for each batch of text in a file tokens = eachdoccontent.split() #split all text into tokens From 636dfc868fa4f590926f7e4da1abf7a4abebcbe4 Mon Sep 17 00:00:00 2001 From: mitch344 <35354833+mitch344@users.noreply.github.com> Date: Sat, 2 Dec 2023 14:46:36 -0600 Subject: [PATCH 13/25] Refactor & Modularization Refactored most of the code & simplified documentation. --- project_code.py | 244 ++++++++++++++++++++---------------------------- 1 file changed, 100 insertions(+), 144 deletions(-) diff --git a/project_code.py b/project_code.py index e1ea4994f0..9eadf06e5a 100644 --- a/project_code.py +++ b/project_code.py @@ -1,151 +1,107 @@ - import os from tkinter import * from collections import defaultdict - - - -class Search: - - def __init__(self):# this function reads and indexes files - - - self.invertedindex = invertedindex = defaultdict(list) - self.list1 = list1 = [] - self.titlelist = titlelist= [] - - #resturant folder in the same path as the script - self.filelocations = os.path.join(os.path.dirname(__file__), "restaurants") - if not os.path.exists(self.filelocations): +class RestaurantSearch: + def __init__(self): + # Initializes the RestaurantSearch class, creating the inverted index and document list + self.inverted_index = defaultdict(list) + self.documents = [] + self.load_and_index_files() + + def load_and_index_files(self): + # Loads restaurant data from files and indexes them for search + file_location = os.path.join(os.path.dirname(__file__), "restaurants") + if not os.path.exists(file_location): messagebox.showerror("Error", "Restaurant folder does not exist") exit() - os.chdir(self.filelocations) #make the program read files from the specified path - - for eachfile in os.listdir(): #for every file in the folder - - with open(eachfile, "r") as r: #open the file - self.list1.append(r.read()) #append the contents of the file to list1 - - - for eachdoccontent in list1: #for each batch of text in a file - tokens = eachdoccontent.split() #split all text into tokens - - for eachtoken in tokens: #for each token in a file - - lowercasewords = eachtoken.lower() #lowercase each token before indexing - removed_1 = lowercasewords.replace(".","") #remove "." before indexing - removed_2 = removed_1.replace(",", "") # remove "," before indexing - removed_3 = removed_2.replace("and", "") # remove "and" before indexing - removed_4 = removed_3.replace("the", "") # remove "the" before indexing - - if list1.index(eachdoccontent) not in invertedindex[removed_4]: #if a document number is not a value for a particular token - invertedindex[removed_4].append(list1.index(eachdoccontent)) #add that document number as a value for that token - - - - def searchindex(self,query,location): #this function queries the inverted index - - querylen = len(query.split()) - resultlist = [] - for eachqueryterm in query.split():#split all query terms - - lowercasequery = eachqueryterm.lower()#lowercase the user query - query_remove_1 = lowercasequery.replace(".","")#remove "." from user query - query_remove_2 = query_remove_1.replace(",", "") #remove "," from user query - if eachqueryterm == "and": #remove "and" from user query - querylen = querylen - 1 - if eachqueryterm == "the": #remove "the" from user query - querylen = querylen - 1 - - for item in self.invertedindex[query_remove_2]: #for every item in the inverted index for a query term - resultlist.append(item) #append each item to result list - - rankdict = {} - for item in resultlist: #for each item in the result list - if item in rankdict: #if the item is in the rankdict - rankdict[item] = rankdict[item] + 1 #increase the count by 1 - else: - rankdict[item] = 1 #if not, then assign 1 - - for item in rankdict: - rankdict[item] = rankdict[item] / querylen #calculating the relavance score - - ranklist = (sorted(rankdict, key=rankdict.get, reverse = True))#this sorts the dictionary by value - - - totalcounter = 1 - relloccounter = 0 - guilist = [] - # this is the output if at least 1 word of the query is in a document - for item in ranklist: - #print the first line in the file and the relevance score - #print(self.list1[item].split("\n")[0],": relevance "+str(round(rankdict[item]*100,3)) + "%") - if location != "": - if self.list1[item].split("\n")[1] == location : - guilist.append("#"+ str(totalcounter) + " " +self.list1[item].split("\n")[0] + ", LOC: " + self.list1[item].split("\n")[1] + ", relevance: "+str(round(rankdict[item]*100,3)) + "%") - relloccounter = relloccounter + 1 - else: - guilist.append("Location Filter Applied") - - if location == "": - guilist.append("#"+ str(totalcounter) + " " +self.list1[item].split("\n")[0] + ", LOC: " + self.list1[item].split("\n")[1] + ", relevance: " + str(round(rankdict[item] * 100, 3)) + "%") - totalcounter = totalcounter + 1 - # this is the output if no words of the query are in any documents - if len(ranklist) == 0: - #print("No words in any document matched your query.") - - guilist.append("No words in any document matched your query.") - - - return guilist, totalcounter-1, relloccounter - - -gui = Tk() -gui.geometry("850x600") -gui.title('The Restaurant Selector') - -titlelabel = Label(gui, text="What do you feel like eating today?",font=("Times", 21)) -titlelabel.pack() - -inputtxt = Text(gui, width=40,height=5) -inputtxt.pack() - -filterlabel = Label(gui, text="(Optional) Please enter a city name and the first two letters of the state. eg. Urbana IL",font=("Times", 8)) -filterlabel.pack() - -filterlabel2 = Label(gui, text="NOTE: The format must exactly match the format of the example.",font=("Times", 8)) -filterlabel2.pack() - -locationtxt = Entry(gui, width=20) -locationtxt.pack() - -resultslabel = Label(gui, text="Results",font=("Times", 12)) -resultslabel.pack() - -results = Text(gui, width=95,height=24) -results.pack() - - -def guifunction(): - - location = locationtxt.get() - - gettext = inputtxt.get("1.0", "end") - - results.insert("1.0", "\nYour Query: "+gettext) - - for item in Search().searchindex(gettext,location)[0].__reversed__(): - results.insert("1.0", "\n" + str(item)) - - results.insert("1.0", "\n" + str(Search().searchindex(gettext, location)[2]) + " relevant to your specified location") - results.insert("1.0", "\n" + str(Search().searchindex(gettext, location)[1]) + " total results returned") - - - -searchbutton = Button(gui, text ="Find Restaurants", command = guifunction) -searchbutton.place(x=70,y=60) - - -gui.mainloop() + for filename in os.listdir(file_location): + with open(os.path.join(file_location, filename), "r") as file: + content = file.read() + self.documents.append(content) + self.index_document(content) + + def index_document(self, content): + # Creates an index of words from the document content for search functionality + tokens = content.lower().split() + doc_id = len(self.documents) - 1 + for token in tokens: + cleaned_token = token.strip(".,").replace("and", "").replace("the", "") + if doc_id not in self.inverted_index[cleaned_token]: + self.inverted_index[cleaned_token].append(doc_id) + + def search(self, query, location): + # Searches indexed documents based on the query and location, returning relevant results + query_terms = query.lower().split() + query_len = len(query_terms) + result_list = [] + for term in query_terms: + cleaned_term = term.strip(".,").replace("and", "").replace("the", "") + result_list.extend(self.inverted_index[cleaned_term]) + + rank_dict = {doc_id: result_list.count(doc_id) / query_len for doc_id in set(result_list)} + ranked_results = sorted(rank_dict, key=rank_dict.get, reverse=True) + + return self.format_results(ranked_results, rank_dict, location), len(ranked_results) + + def format_results(self, ranked_results, rank_dict, location): + # Formats the search results for display, filtering by location if specified + output = [] + location_match_count = 0 + for doc_id in ranked_results: + title, loc = self.documents[doc_id].split("\n")[:2] + if location and loc != location: + continue + location_match_count += 1 + relevance = round(rank_dict[doc_id] * 100, 3) + output.append(f"{title}, LOC: {loc}, relevance: {relevance}%") + return output, location_match_count + + +class GUI: + def __init__(self, master): + # Initializes the GUI elements and sets up the main window + self.master = master + master.title('The Restaurant Selector') + master.geometry("850x600") + + self.search_engine = RestaurantSearch() + + Label(master, text="What do you feel like eating today?", font=("Times", 21)).pack() + self.query_entry = Text(master, width=40, height=5) + self.query_entry.pack() + + Label(master, text="(Optional) Please enter a city name and the first two letters of the state. e.g., Urbana IL", font=("Times", 8)).pack() + self.location_entry = Entry(master, width=20) + self.location_entry.pack() + + self.results_text = Text(master, width=95, height=24) + self.results_text.pack() + + Button(master, text="Find Restaurants", command=self.perform_search).pack() + + def perform_search(self): + # Initiates a search when the 'Find Restaurants' button is clicked + query = self.query_entry.get("1.0", "end").strip() + location = self.location_entry.get().strip() + results, total_results = self.search_engine.search(query, location) + results, location_match_count = results + + self.results_text.delete("1.0", END) + self.results_text.insert("1.0", f"{total_results} total results returned\n") + if location: + self.results_text.insert(END, f"Location Filter Applied\n{location_match_count} relevant to your specified location\n") + else: + self.results_text.insert(END, "No Location Filter Applied\n") + for result in results: + self.results_text.insert(END, result + "\n") + +def main(): + # The main function to run the application + root = Tk() + gui = GUI(root) + root.mainloop() + +if __name__ == "__main__": + main() From 675be063b54ab310afeeb09d784e020a40b93689 Mon Sep 17 00:00:00 2001 From: kopczyk93 <127679010+kopczyk93@users.noreply.github.com> Date: Fri, 8 Dec 2023 20:03:06 -0600 Subject: [PATCH 14/25] Hamilton's BM25 Update --- project_code.py | 68 ++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 59 insertions(+), 9 deletions(-) diff --git a/project_code.py b/project_code.py index 9eadf06e5a..5e81818404 100644 --- a/project_code.py +++ b/project_code.py @@ -1,19 +1,25 @@ import os from tkinter import * from collections import defaultdict +import math class RestaurantSearch: def __init__(self): # Initializes the RestaurantSearch class, creating the inverted index and document list - self.inverted_index = defaultdict(list) - self.documents = [] + self.inverted_index = dict() + self.documents = [] + self.doc_lengths = dict() + self.total_doc_len = 0.0 self.load_and_index_files() + self.document_count = len(self.documents) + self.avg_doc_len = self.total_doc_len / self.document_count def load_and_index_files(self): # Loads restaurant data from files and indexes them for search file_location = os.path.join(os.path.dirname(__file__), "restaurants") if not os.path.exists(file_location): - messagebox.showerror("Error", "Restaurant folder does not exist") + #messagebox.showerror("Error", "Restaurant folder does not exist") + print("Error restaurant folder not found") exit() for filename in os.listdir(file_location): @@ -26,21 +32,38 @@ def index_document(self, content): # Creates an index of words from the document content for search functionality tokens = content.lower().split() doc_id = len(self.documents) - 1 + self.doc_lengths[doc_id] = len(tokens) + # redo with form [token][doc_id] + self.inverted_index[doc_id] = dict() for token in tokens: cleaned_token = token.strip(".,").replace("and", "").replace("the", "") - if doc_id not in self.inverted_index[cleaned_token]: - self.inverted_index[cleaned_token].append(doc_id) + if cleaned_token not in self.inverted_index: + self.inverted_index[cleaned_token] = dict() + if doc_id in self.inverted_index[cleaned_token]: + self.inverted_index[cleaned_token][doc_id] += 1 + else: + self.inverted_index[cleaned_token][doc_id] = 1 + self.total_doc_len += len(tokens) def search(self, query, location): # Searches indexed documents based on the query and location, returning relevant results query_terms = query.lower().split() - query_len = len(query_terms) + query_counts = dict() + qt_df = dict() + query_len = len(query_terms) + result_list = [] for term in query_terms: cleaned_term = term.strip(".,").replace("and", "").replace("the", "") - result_list.extend(self.inverted_index[cleaned_term]) + if cleaned_term in self.inverted_index: + query_counts[cleaned_term] = query_counts.get(cleaned_term, 0) + 1 + qt_df[cleaned_term] = len(self.inverted_index[cleaned_term]) + result_list.extend(self.inverted_index[cleaned_term]) + + unique_results = set(result_list) + num_unique_results = len(unique_results) - rank_dict = {doc_id: result_list.count(doc_id) / query_len for doc_id in set(result_list)} + rank_dict = {doc_id: self.bm25(doc_id, query_counts, num_unique_results, qt_df) for doc_id in unique_results} ranked_results = sorted(rank_dict, key=rank_dict.get, reverse=True) return self.format_results(ranked_results, rank_dict, location), len(ranked_results) @@ -49,15 +72,42 @@ def format_results(self, ranked_results, rank_dict, location): # Formats the search results for display, filtering by location if specified output = [] location_match_count = 0 + # Scale every result as a percentage of the most relevant result + top_score = rank_dict[ranked_results[0]] for doc_id in ranked_results: title, loc = self.documents[doc_id].split("\n")[:2] if location and loc != location: continue location_match_count += 1 - relevance = round(rank_dict[doc_id] * 100, 3) + relevance = '{:.3f}'.format(round((rank_dict[doc_id] / top_score) * 100, 3)) output.append(f"{title}, LOC: {loc}, relevance: {relevance}%") return output, location_match_count + def bm25(self, doc_id, query_counts, num, qt_df): + b_ = .75 + k1_ = 1.6 + k3_ = 1 + + okapi_bm25 = 0 + + for t in query_counts: + if doc_id in self.inverted_index[t]: + # Inverse Document Frequency Term + IDF = math.log( + 1.0 + (self.document_count - qt_df[t] + 0.5) / (qt_df[t] + 0.5)); + + # Term Frequency with Document Length Normalization + TF = ((k1_ + 1.0) * self.inverted_index[t][doc_id]) / ((k1_ * ((1.0 - b_) + b_ * self.doc_lengths[doc_id] / self.avg_doc_len)) + + self.inverted_index[t][doc_id]); + + # QTF handles how to value appearances of a term multiple times in the same query + QTF = ((k3_ + 1.0) * query_counts[t]) / (k3_ + query_counts[t]); + + #Okapi BM25 + okapi_bm25 += TF * IDF * QTF + + return okapi_bm25 + class GUI: def __init__(self, master): From 6244fcab303e57116a8ea55e73400ecdd045a66a Mon Sep 17 00:00:00 2001 From: paulinebrunet03 <70296017+paulinebrunet03@users.noreply.github.com> Date: Sat, 9 Dec 2023 07:57:36 -0800 Subject: [PATCH 15/25] Add files via upload --- restaurants.zip | Bin 0 -> 7933 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 restaurants.zip diff --git a/restaurants.zip b/restaurants.zip new file mode 100644 index 0000000000000000000000000000000000000000..61d3fd6f77132ceee6f486ff69ee8d8e0db7c46f GIT binary patch literal 7933 zcma)>cRbbo`^S&HIriQ}GP8+L#Bm5&4dqyw$BwL=>>EW!h+BlLvdW5Na~vc)(Xg`j zUcd9{dv|y0?&Np%Q2670U9Zpkd9KfOz0%Xh!>0oQG3|ZV}Yq( 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zE@e#INk^=xdVkH1Ss4eodq@Fo_79wKaOv={Tlr`_zQuwn@DC>5#~b_qJNdh{kLi2^ z*8ca%LO^~w`A-%%@V^MZ82x(MKPSR;Spg&cdt@QhaX{E`{!`B1(EMvoOm`IUZ@)(t zLJe()U_Lf%Ht7FD7^Vpa7_HwU3vq}}g5`6HE#EPs0N)1F(E+Tz?~#SrC-_+u7VG91 zO+XGz;{fo6zDE{=MRsi$R_sBx*qTex?eejR-!!go;Oqee*U`+WQS%^-0?9%=; XlKoa50U>ZOHTn++5eU@I2>kSaE}Y0` literal 0 HcmV?d00001 From 05647045e5766a5804d193583cf7a9b3dd6be973 Mon Sep 17 00:00:00 2001 From: louishamilton <37088794+louishamilton@users.noreply.github.com> Date: Sat, 9 Dec 2023 12:16:30 -0600 Subject: [PATCH 16/25] Update project_code.py Make location match smarter and change relative relevance score to take location filter into account --- project_code.py | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/project_code.py b/project_code.py index 5e81818404..5326dd9010 100644 --- a/project_code.py +++ b/project_code.py @@ -2,6 +2,7 @@ from tkinter import * from collections import defaultdict import math +import re class RestaurantSearch: def __init__(self): @@ -73,10 +74,17 @@ def format_results(self, ranked_results, rank_dict, location): output = [] location_match_count = 0 # Scale every result as a percentage of the most relevant result - top_score = rank_dict[ranked_results[0]] + top_score = 0 + + for doc_id in ranked_results: + title, loc = self.documents[doc_id].split("\n")[:2] + if location and not self.match_location(loc, location): + continue + top_score = max(top_score, rank_dict[doc_id]) + for doc_id in ranked_results: title, loc = self.documents[doc_id].split("\n")[:2] - if location and loc != location: + if location and not self.match_location(loc, location): continue location_match_count += 1 relevance = '{:.3f}'.format(round((rank_dict[doc_id] / top_score) * 100, 3)) @@ -108,6 +116,12 @@ def bm25(self, doc_id, query_counts, num, qt_df): return okapi_bm25 + def match_location(self, a, b): + regex = re.compile('[^a-zA-Z]') + a_new = regex.sub('', a).lower() + b_new = regex.sub('', b).lower() + return a_new == b_new + class GUI: def __init__(self, master): From c9aea63d8504ee57986b78ea7c3dc0f2bdf3fa8f Mon Sep 17 00:00:00 2001 From: corlando12 <144142855+corlando12@users.noreply.github.com> Date: Fri, 15 Dec 2023 12:04:14 -0500 Subject: [PATCH 17/25] Rename project_code.py to restaurant_search.py --- project_code.py => restaurant_search.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename project_code.py => restaurant_search.py (100%) diff --git a/project_code.py b/restaurant_search.py similarity index 100% rename from project_code.py rename to restaurant_search.py From 9d1e062bd66c2b98c60d9a6932519b657b3821d0 Mon Sep 17 00:00:00 2001 From: paulinebrunet03 <70296017+paulinebrunet03@users.noreply.github.com> Date: Fri, 15 Dec 2023 09:10:24 -0800 Subject: [PATCH 18/25] Add files via upload --- ReadmeF.pdf | Bin 0 -> 70479 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ReadmeF.pdf diff --git a/ReadmeF.pdf b/ReadmeF.pdf new file mode 100644 index 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zo8;jg8|xBtR+gVkpRF)_?wp#y3NFuKiMMxr_=4FL+hRg7V0SJbN0XFxa15P9m za0IwF{6h@rG5Iw(>wSzw1Yk@lhDMWtT#+3g@~{Mm3`$iYMnsdz%KZ_^Boa_!3VCQW z20VpQj06#s?}5e>Ah389+d^m}5RFPP%*tnmFrZ)*^RReOJjyWwTIS#s`oj_kSmk?Q ziD*2SWraKfnFz{GIYwSFHX@pcR_>38!GN+=Y)gb7Q0PiAG8$C9atwf=r5KNU92iKh x40s-5@)@!zM>z)u@?=yH*dp+4mJvkxIru6B@s4wO^2I@vNk13@Ve7C5_HS^OJH!A0 literal 0 HcmV?d00001 From df0554e1761870b7ff035cd99d6d8f8dc36d3d2c Mon Sep 17 00:00:00 2001 From: paulinebrunet03 <70296017+paulinebrunet03@users.noreply.github.com> Date: Fri, 15 Dec 2023 09:10:54 -0800 Subject: [PATCH 19/25] Delete README.md --- README.md | 3 --- 1 file changed, 3 deletions(-) delete mode 100644 README.md diff --git a/README.md b/README.md deleted file mode 100644 index a7b40d2cc8..0000000000 --- a/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# CourseProject - -Please fork this repository and paste the github link of your fork on Microsoft CMT. Detailed instructions are on Coursera under Week 1: Course Project Overview/Week 9 Activities. 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