Partial.py

import nltk
nltk.download('wordnet')
import csv
import spacy
from nltk.corpus import wordnet as wn

# Load spaCy's English language model
nlp = spacy.load('en_core_web_sm')

def get_synonyms(word):
    synonyms = set()
    for synset in wn.synsets(word):
        for lemma in synset.lemmas():
            synonyms.add(lemma.name())
    return synonyms

def calculate_similarity(keyword, domain):
    # Process the keyword and domain text using spaCy
    keyword_doc = nlp(keyword)
    domain_doc = nlp(domain)

    # Calculate similarity between the keyword and domain
    similarity = keyword_doc.similarity(domain_doc)

    # Get synonyms of the keyword and domain words
    keyword_synonyms = set()
    domain_synonyms = set()
    for token in keyword_doc:
        keyword_synonyms.update(get_synonyms(token.text.lower()))
    for token in domain_doc:
        domain_synonyms.update(get_synonyms(token.text.lower()))

    # Calculate similarity based on synonyms
    synonym_similarity = len(keyword_synonyms.intersection(domain_synonyms)) / len(keyword_synonyms.union(domain_synonyms))
    similarity *= synonym_similarity  # Scale similarity based on synonym match

    return similarity

# User input for the keyword
user_keyword = input("Enter a keyword: ")

max_score = float('-inf')
max_domain_name = None
all_scores = {}

# Open the CSV file and calculate similarity scores
with open('/workspaces/solv-a-thon/merged_data(2).csv', mode='r') as csv_file:
    csv_reader = csv.DictReader(csv_file)
    for row in csv_reader:
        domain_name = row['Research unit(s)']
        similarity_score = calculate_similarity(user_keyword, domain_name)
        all_scores[domain_name] = similarity_score

        if similarity_score > max_score:
            max_score = similarity_score
            max_domain_name = domain_name

# Print the domain with the highest score and all scores
print(f"The domain with the highest score for '{user_keyword}' is '{max_domain_name}' with a score of {max_score}")
print("All scores:")
for domain, score in all_scores.items():
    print(f"{domain}: {score}")