Limble/matching1.py at main · sabarishh14/Limble · GitHub

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import pandas as pd
import firebase_admin
from firebase_admin import credentials, db
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

def top_mentors_for_mentee(mentee_email, top_n=5):
    if not firebase_admin._apps:
        cred = credentials.Certificate("credentials.json")
        firebase_admin.initialize_app(cred, {"databaseURL": "https://limble-30e8a-default-rtdb.asia-southeast1.firebasedatabase.app/"})

    ref = db.reference('/')

    mentors_data = ref.child('mentor').get()
    mentees_data = ref.child('mentee').get()

    menteedf = list(mentees_data.values())
    mentordf = list(mentors_data.values())

    mentors_df = pd.DataFrame(mentordf)
    mentees_df = pd.DataFrame(menteedf)

    # Convert all values in the DataFrames to strings
    mentors_df = mentors_df.astype(str)
    mentees_df = mentees_df.astype(str)

    mentors_df['mentor_profile'] = mentors_df['designation'] + ' ' + mentors_df['field_of_work'] + ' ' + mentors_df['skills_expertise'] + ' ' + mentors_df['languages']
    mentees_df['mentee_profile'] = mentees_df['skills_needed'] + ' ' + mentees_df['current_skill_level'] + ' ' + mentees_df['learning_style'] + ' ' + mentees_df['expectations'] + ' ' + mentees_df['languages']

    vectorizer = TfidfVectorizer()

    mentor_profiles = vectorizer.fit_transform(mentors_df['mentor_profile'])
    mentee_profiles = vectorizer.transform(mentees_df['mentee_profile'])

    cosine_similarities = cosine_similarity(mentee_profiles, mentor_profiles)

    mentee_index = mentees_df.index[mentees_df['email'] == mentee_email][0]
    top_mentor_indices = cosine_similarities[mentee_index].argsort()[::-1][:top_n]

    top_mentors_info = []
    for idx in top_mentor_indices:
        mentor_info = mentors_df.iloc[idx].to_dict()
        top_mentors_info.append(mentor_info)

    return top_mentors_info