rb1/ccr

1import gradio as gr2import pandas as pd3import pickle4import nltk5from nltk.stem.porter import PorterStemmer6ps= PorterStemmer()7# def greet(name):8#     return "Hello " + name + "!!"9 10similarity = pickle.load(open('./skills_similarity.pkl', 'rb'))11new_df = pd.read_csv('./new_df.csv')12 13def stem(text):14  y = []15 16  for i in text.split():17    y.append(ps.stem(i))18 19  return " ".join(y)20 21 22def recommend(skill):23  skill = skill.replace(" ", "")24  skill = skill.lower()25  skill = stem(skill)26  course_index = new_df[new_df['skills'] == skill].index[0]27  distances = similarity[course_index]28  course_list = sorted(list(enumerate(distances)),reverse = True, key=lambda x:x[1])[1:11]29  # print(course_list)30  ids = []31  for i in course_list:32    # print(new_df.iloc[i[0]][['_id', 'name', 'skills']], i[1])33    # print(new_df.iloc[i[0]][['_id']], i[1])34    # return new_df.iloc[i[0]][['_id']]35    ids.append(new_df.loc[i[0]]['_id'])36  return ids37  #  for i in course_list:38  #    print(new_df.iloc[i[0]].name, new_df.loc[i])39 40iface = gr.Interface(fn=recommend, inputs="text", outputs="text")41iface.launch()