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| from streamlit import secrets | |
| VESRION = "1.0.1" | |
| API_URL_summary = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn" | |
| API_URL_name = "https://api-inference.huggingface.co/models/dbmdz/bert-large-cased-finetuned-conll03-english" | |
| API_URL_qna = "https://api-inference.huggingface.co/models/deepset/tinyroberta-squad2" | |
| API_TOKEN = secrets["API_TOKEN"] | |
| HEADERS = {"Authorization": f"Bearer {API_TOKEN}"} | |
| SENTENCE_TRANSFORMER_MODEL = "paraphrase-distilroberta-base-v1" | |
| LLM_REPO_ID = "MBZUAI/LaMini-T5-223M" | |
| # A custom exception-like class to show a streamlit-styled error | |
| class StreamlitException: | |
| def __init__(self, message): | |
| self.message = message | |
| # A set of technical skills | |
| TECH_SKILLS = set([ | |
| 'Python', 'R', 'SQL', 'Java', 'MATLAB', 'Mathematica', 'C#', 'C++', 'Javascript', | |
| 'NumPy', 'SciPy', 'Pandas', 'Theano', 'Caffe', 'SciKit-learn', 'Matplotlib', 'Seaborn', | |
| 'Plotly', 'TensorFlow', 'Keras', 'NLTK', 'PyTorch', 'Gensim', 'Urllib', 'BeautifulSoup4', | |
| 'PySpark', 'PyMySQL', 'SQAlchemy', 'MongoDB', 'sqlite3', 'Flask', 'Deeplearning4j', 'EJML', | |
| 'dplyr', 'ggplot2', 'reshape2', 'tidyr', 'purrr', 'readr', 'Apache', 'Spark', 'Git', 'GitHub', | |
| 'GitLab', 'Bitbucket', 'SVN', 'Mercurial', 'Trello', 'PyCharm', 'IntelliJ', 'Visual Studio', | |
| 'Sublime', 'JIRA', 'TFS', 'Linux', 'Unix', 'Hadoop HDFS', 'Google Cloud Platform', 'MS Azure Cloud', | |
| 'SQL', 'NoSQL', 'Data Warehouse', 'Data Lake', 'SWL', 'HiveQL', 'AWS', 'RedShift', 'Kinesis', 'EMR', | |
| 'EC2', 'Lambda', 'Data Analysis', 'Data Mining', 'Data Visualization', 'Statistical Analysis', | |
| 'Multivariate Analysis', 'Stochastic Optimization', 'Linear Regression', 'ANOVA', | |
| 'Hypothesis Testing', 'Forecasting', 'ARIMA', 'Sentiment Analysis', 'Predictive Analysis', | |
| 'Pattern Recognition', 'Classification', 'Behavioural Modelling', 'Supervised Machine Learning Algorithms', | |
| 'Linear Regression', 'Logistic Regression', 'Support Vector Machines', 'Decision Trees and Random Forests', | |
| 'Naïve Bayes Classifiers', 'K Nearest Neighbors', 'Unsupervised Machine Learning Algorithms', 'K Means Clustering', | |
| 'Gaussian Mixtures', 'Hidden Markov Models', 'Auto Encoders', 'Imbalanced Learning', 'SMOTE', 'AdaSyn', 'NearMiss', | |
| 'Deep Learning Artificial Neural Networks', 'Machine Perception', 'Document Tokenization', 'Token Embedding', | |
| 'Word Models', 'Word2Vec', 'Doc2Vec', 'FastText', 'Bag of Words', 'TF/IDF', 'Bert', 'Elmo', 'LDA', | |
| 'Machine Language Comprehension', 'Sentiment Analysis', 'Predictive Maintenance', 'Demand Forecasting', | |
| 'Fraud Detection', 'Client Segmentation', 'Marketing Analysis', 'AWS', 'MS Azure', 'Google Cloud Platform', | |
| 'CI/CD', 'IaaC', 'big data', 'h2o', 'gbm', 'pytorch', 'caffe', 'opencv', 'deeplearning4j', 'neo4j', | |
| 'decision-trees', 'decision trees', 'programming', 'jira', 'excel', 'sas', 'vba', 'random forest', | |
| 'xgboost', 'xgb', 'regression', 'logistic regression', 'linear regression', 'clustering', 'pca', | |
| 'hypothesis testing', 'ab testing', 'ab-testing', 'bigquery', 'vertexai','amazon web services', | |
| 'google cloud services', 'node.js', 'linux', 'unix', 'hive', 'spark', 'pyspark', 'java', 'c++', | |
| 'python', 'r', 'database', 'algorithm', 'data structure', 'ai', 'ml', 'machine learning', 'python', | |
| 'keras', 'tensorflow', 'tf', 'sql', 'aws', 'azure', 'gcp', 'cloud', 'deep learning', 'neural network', | |
| 'computer vision', 'optimization', 'statistics', 'time series', 'time series', 'time series forecasting', | |
| 'time series forecasting', 'modelling', 'forecasting', 'etl', 'mlops', 'natural language processing', | |
| 'computer vision', 'knn', 'image processing', 'nlp' | |
| ]) | |