This project contains a Python script (gui.py) for a graphical user interface (GUI) that performs search and query expansion on a dataset related to alternative medicine. The GUI was generated using Tkinter Designer by Parth Jadhav. Features
Search Functionality: Allows users to search the dataset using boolean and TF-IDF based queries.
Query Expansion: Expands user queries by extracting relevant terms from top-ranked documents.
Results Display: Displays the search results within the GUI.
Preprocessing: Preprocesses the dataset for effective searching.
Inverted Index Creation: Builds an inverted index for efficient query processing.
Weight Calculation: Calculates weights (TF-IDF scores) for terms in the dataset.
Requirements
Python 3.x
Tkinter
NLTK
You can install the necessary packages using pip:pip install nltk
How to Run:
1 Clone this repository:git clone https://github.com/yourusername/your-repo-name.git.
2 Navigate to the project directory: cd your-repo-name.
3 Run the GUI script: python gui.py.
Files
gui.py: The main script for the GUI.
preprocessing+inverted index+weight: Script for preprocessing the dataset+Script for creating the inverted index+Script for calculating term weights (TF-IDF).
tfidf-result.txt: File containing TF-IDF scores for terms.
processed_documents.txt: File containing preprocessed documents.
inverted-index.json: File containing the inverted index of the dataset.
DATA-OF-ALTERNATIVE-MEDICINE-1.txt: The dataset file.
Usage
Search: Enter your query in the search box and click the "Search" button.
Query Expansion: The expanded query functionality can be triggered by clicking the respective button.
Results: The search results are displayed in the text widget area of the GUI.
Functions Main Functions
preprocess_text: Preprocesses the input text (tokenization, normalization, stopword removal, stemming).
load_inverted_index: Loads the inverted index from a JSON file.
calculate_tfidf_for_query: Calculates the TF-IDF scores for the query terms.
calculate_document_scores: Calculates similarity scores for each document based on TF-IDF scores.
rank_documents: Ranks documents based on cosine similarity scores.
boolean_search: Performs a boolean search on the dataset.
read_documents: Reads the dataset and stores it in a dictionary.
on_button_click: Callback function for the search button.
expand_query: Expands the user's query using top-ranked documents.
GUI Elements
Entry Widget: For user to input search queries.
Button Widget: To initiate search and query expansion.
Text Widget: To display search results.
Canvas: To layout and design the GUI elements.